Rapid Micro Methods Blog

Birmingham Researchers Find New Approach to Detect Infections Causing Meningitis and Encephalitis

2026-10-13T04:04:36.284-04:00

Image created by Dr. Michael J. Miller

New research led by Aston University’s Dr Ghaniah Hassan-Smith, has found a new technique to diagnose infections of the central nervous system, such as meningitis and encephalitis, which can be difficult to determine using conventional methods.

The method developed by Dr Hassan-Smith, clinical senior lecturer at Aston Medical School, and honorary consultant neurologist at University Hospitals Birmingham (UHB) and her collaborators, is based on metagenomic sequencing, an existing and extremely sensitive genetic test, combined with a new and unique computational ‘filtering’ system to identify likely disease-causing bacteria and viruses from the complex information generated.

The research team included Dr Zaki Hassan Smith, clinical associate professor at Aston Medical School and consultant endocrinologist at UHB, and collaborators at the University of Birmingham – Professor Nicholas Loman, director of the Institute of Microbiology and Infection, Dr Joshua Quick, UKRI Future Leaders and Dr Nicola Cumley, clinical scientist.

Central nervous system infections can be serious and require rapid diagnosis and treatment. Usually, such infections are diagnosed by taking cerebrospinal fluid by lumbar puncture and combining knowledge of the patient’s symptoms with laboratory tests such as microscopy, microorganism culture and targeted polymerase chain reaction (PCR) tests for particular organisms. This requires the medical team to have some knowledge of what they are looking for.

In a substantial proportion of patients with a suspected central nervous system infection (over 50% in some settings, such as intensive care), no causative pathogenic organism is identified. This may be because the pathogen is unexpected, present only in small amounts or because antibiotics or antiviral treatment have already been started.

Rather than testing for individual pathogens, metagenomic sequencing analyses all the genetic material present in a sample, takes out all the unnecessary genomic data and identifies DNA and RNA and which microorganisms it is likely to have come from. This means medical staff can look for many different bacteria, viruses and other microorganisms simultaneously, including unusual or unexpected pathogens. This is particularly relevant for central nervous system infections, where there can be major diagnostic challenges.

The researchers tested cerebrospinal fluid samples from patients with central nervous system infections, sourced from UHB, and compared them with non-infectious neurological control samples.

Samples can also be contaminated with other microorganisms, for example those found on skin or in laboratory reagents. This can lead to signals which appear disproportionately important. Dr Ghaniah Hassan-Smith and the team developed a series of filters to remove this ‘background noise’ based on bioinformatics. This is a technique that uses computational methods to understand biological data, in this case, the DNA and RNA signals from the fluid samples.

The filters are able to separate potentially meaningful pathogen signals from this background noise and so determine what the infectious agent is. The method brings together several pieces of sequencing information rather than relying on a single result.

Dr Ghaniah Hassan-Smith said:

“Central nervous system infections such as meningitis and encephalitis can be extremely serious, but in many patients we never identify the organism responsible. Conventional tests are very good at finding the pathogens we suspect, but metagenomic sequencing gives us the exciting possibility of looking much more broadly, including for unusual or unexpected causes of infection.

“The difficulty is that looking for everything also means finding a lot of noise. Not every microbial signal detected in a sample represents a genuine infection, so interpreting these very complex datasets is one of the major challenges in bringing metagenomic sequencing into clinical practice.

“This isn't intended to replace conventional microbiology. Rather, we hope the work contributes to developing more standardised ways of interpreting metagenomic sequencing and ultimately helps move this powerful technology closer to routine clinical practice.”

Reference

Cumley, N., Quick, J., Brier, T. et al. Pathogen detection in central nervous system infections: moving metagenomic sequencing closer to clinical practice. BMC Infect Dis 26, 1517 (2026). https://doi.org/10.1186/s12879-026-13276-9

Abstract

Background

Central nervous system infections (CNSI) contribute significantly to global disability and mortality, but the causative agent is often undetected. Metagenomic sequencing offers the potential to enhance diagnostic sensitivity, particularly in cases of unusual or partially treated infections. However, caution is required in interpretation of metagenomics data due to technical artefacts from contamination or non-specific read mapping which can reveal a broad spectrum of biologically plausible but diagnostically unlikely organisms.

Methods

This study compares the performance of metagenomic sequencing with standard clinical microbiology methods using cerebrospinal fluid (CSF) from patients with CNSI and non-infected control samples. To evaluate sensitivity of different laboratory approaches, we sequenced DNA and RNA metagenomic libraries extracted from CSF, using both cell-free and cellular fractions. We then devised a set of simple, easily interpreted yet rigorous filters tailored for clinical metagenomics to generate a framework for result interpretation that can be readily applied by clinical scientists.

Results

We demonstrate that composite filtering strategies are essential to reduce misleading signals and support standardised workflows. Additionally, our results suggest that a cell-free sample preparation approach can improve confidence in identifying clinically relevant pathogens, highlighting the impact of sample preparation on results quality.

Conclusion

In this study we describe a reproducible method that can be incorporated into a practical framework for clinical application of metagenomic sequencing in CNSI diagnostics.

VU LSC Researchers Develop Technology for Faster Antibiotic Susceptibility Detection

2026-10-13T03:51:54.384-04:00

Image created by Dr. Michael J. Miller

It can often take several days to determine which antibiotic will be effective for a specific patient. Researchers at the Life Sciences Center of Vilnius University (VU LSC), together with their partners, are developing technology that could reduce this time to just a few hours. For this, they are using the bacteria themselves: the researchers aim to use biofilms that bacteria form as natural biological indicators that can show, in real time, whether a selected antibiotic is effective. 

Dr Rokas Žalnėravičius, leader of the VU LSC research team, says that making diagnosis more convenient is not the only reason why faster determination of bacterial susceptibility to antibiotics is important. The sooner it is determined which drug will be effective against the pathogen causing a particular patient’s infection, the sooner targeted treatment can be prescribed, avoiding unnecessary antibiotic use.

‘Today, it often takes one to three days to receive the results of an antibiotic susceptibility test. When treating a severe infection, this is a long time. Our goal is to take advantage of the natural biological processes of bacteria to obtain these results within a few hours and thus help doctors select the right antibiotic for a particular patient more quickly,’ said Dr Žalnėravičius.

The research is being carried out as part of a project funded by the Research Council of Lithuania, with researchers from the Center for Physical Sciences and Technology, the Nature Research Centre (NRC), and the Biofilms Research Center for Biointerfaces of Malmö University in Sweden working alongside VU LSC researchers. Young researchers are also involved in the project: Dr Eglė Malachovskienė, research assistant at LSC, Bachelor’s student at VU LSC Rugilė Jonaitytė, and Master’s student at VU LSC Evelina Lukaitė.

Bacterial ‘fortresses’ hinder treatment

Bacterial biofilms are one of the key areas of the researchers’ work. These are communities naturally formed by bacteria, in which the microorganisms are embedded in a protective polymeric matrix. It acts as a barrier, helping bacteria protect themselves against adverse environmental conditions, the human immune response, and antibiotics.

Biofilms can form on a wide range of surfaces, such as glass, metal, plastic, and tissue. As a result, they can also be found on medical devices, such as catheters, infusion systems, and implants. They become particularly dangerous when they form inside the human body or in wounds, as it can lead to difficult-to-heal infections and other serious complications.

‘An individual bacterium and bacteria living in a biofilm can be compared to a person in an open field and a person in a well-fortified fortress. The biofilm matrix provides bacteria with additional protection, making them much more difficult to reach and destroy with antibiotics. This is exactly why such infections are one of the most complex problems in clinical microbiology,’ explained Dr Žalnėravičius.

The antibiotic resistance of bacteria in biofilms can be up to a thousand times greater than that of individual bacteria. Therefore, antibiotics alone are often insufficient to treat such infections; repeated wound cleaning or surgical removal of damaged tissue may be required.

Are antibiotics effective? Bacteria reveal the answer

Currently, an antibiotic susceptibility test is typically performed to select the appropriate antibiotic for a patient. Bacteria in a biological sample taken from the patient are first cultured and identified, after which their susceptibility to different antibiotics is determined. Such testing usually takes one to three days.

The method being developed by VU LSC researchers is based on an entirely different principle – the natural ability of bacteria to transfer electrons into their surroundings.

Metabolic reactions constantly take place in bacterial cells, generating electrons. Under certain conditions, some of these electrons are transferred outside the cell. If bacteria grow on an electrically conductive surface, for example, an electrode, this movement of electrons can be recorded by electromechanical methods.

This gives researchers the opportunity to monitor in real time how active the bacteria in a biofilm are.

‘For bacteria, electron transfer is part of their natural metabolism, but for us, it is a biological signal. We can monitor bacterial activity and, by introducing an antibiotic into the system, observe how the bacteria respond to it. If metabolic activity decreases, we receive a signal that the antibiotic is inhibiting or inactivating the bacteria,’ told Dr Žalnėravičius.

In this way, the bacterial biofilm itself becomes a kind of biological indicator, making it possible to assess the effect of an antibiotic in real time rather than after several days.

The goal: the right antibiotic for the right patient

The research is still ongoing, and its results have not yet been published; the researchers, therefore, note that considerable work remains before the technology can be used in clinical practice. Nevertheless, the first results have been encouraging for the team.

‘These are only the first steps in developing the technology, but the results we have obtained so far have exceeded our initial expectations. What matters most to us is that the generated scientific knowledge could turn into an actual benefit for the patient in the future, making it possible to determine the effective antibiotic and start targeted treatment as soon as possible,’ said Dr Žalnėravičius.

According to the researcher, such an opportunity would be particularly relevant for patients for whom biofilm-related infections pose the greatest risk, including the elderly and patients with difficult-to-heal wounds.

Faster determination of antibiotic susceptibility could also have broader implications. A more precise selection of antibiotics could reduce their excessive use and, at the same time, slow the spread of antibiotic-resistant bacteria.

‘We would like to move towards a situation in which an antibiotic is prescribed not by trial and error, but on the basis of rapidly obtained information about the susceptibility of a specific pathogen. The more targeted the use of antibiotics, the more opportunities to preserve their effectiveness in the future,’ said Dr Žalnėravičius.

The project being implemented by VU LSC and funded by the Research Council of Lithuania (No. P-PD-24-155) demonstrates that, in the future, antibiotic use will be individualised and based on susceptibility determined in advance.

University of Windsor Student Researcher Shines a Light on Bacterial Identification

2026-10-13T03:47:36.709-04:00

Image created by Dr. Michael J. Miller

This summer, science student Kitti Keller learned a lesson on how melding biology and physics could one day help identify bacterial pathogens that threaten human health.

"We're hoping to develop a technology so one day you can go to the clinic, give a tiny blood or urine sample and get immediate results showing what's making you sick," says the third-year biology student.

"That would speed up the diagnostic process significantly and you could get treatment a lot faster, which could prevent many infections from progressing."

Keller is working in Dr. Steven Rehse's biomedical physics lab with support from an Undergraduate Student Research Award (USRA) from the Natural Sciences and Engineering Research Council of Canada.

The lab includes biologists, biomedical science students and physicists among other majors. The bio students grow and prepare the bacteria; the physicists use lasers to identify the samples.

"We deposit the bacteria on tiny filters which get put on a steel plate, sent over to the laser side, where the physicists shoot the laser into the sample," she says.

Keller's research involves laser-induced breakdown spectroscopy (LIBS), a technique for rapid bacterial identification. When the laser hits the sample, it creates a plasma that produces a unique spectrum of light.

"Our research group - the Rehse lab - has previously demonstrated the ability to detect and distinguish clinically relevant pathogens, including Escherichia coli, Staphylococcus epidermidis and Mycobacterium smegmatis, in blood and urine samples," says Keller.

"Essentially, it's telling us what atoms are in the bacteria we're shooting and we're training the computer algorithm to be able to recognize what atoms and in what amounts are in certain kinds of bacteria."

During first-year orientation, Keller, then an Outstanding Scholars candidate, heard a student from Rehse's lab speak about the research, and she was hooked.

She started volunteering in his lab in her first semester. In second year, she became an Outstanding Scholar and continued working in the lab - this time as a paid undergraduate researcher.

Since then, she has presented her research and won first place with her research poster at the UWill Discover Conference.

That determination and focus extend beyond the lab. At 16, Keller won second place at the karate world championship in Ireland and again the following year in Florida. On campus, she co-leads the visual arts group Science Meets Art (SMArt), has served on the executive team of Crafting for a Cure and on the Outstanding Scholars gala committee.

Looking down the road, Keller would like to pursue a graduate degree in microbiology as her passion for research continues to deepen.

Before graduate school, she plans to continue working in the Rehse lab and complete a fourth-year thesis focused on developing a novel silicon-wafer-based substrate for bacterial identification.

"The research has exciting potential to improve diagnostic technologies," she says.

"The future is vast and I would love to see this technology applied sometime during my lifetime."

Scientists Target Faster Sepsis Pathogen Identification Through New International Project

2026-10-13T03:41:40.368-04:00

Image created by Dr. Michael J. Miller

In the UK, sepsis affects an estimated 245,000 people each year and claims 48,000 lives, equivalent to five deaths every hour, according to the UK Sepsis Trust.

Across Europe, more than 3.4 million people develop sepsis annually and around 700,000 die, according to the European Sepsis Alliance.

Sepsis is a life-threatening response to infection that can lead to shock, multiple organ failure and death if it is not recognised early and managed promptly. One of the major challenges in treating sepsis is identifying the pathogen responsible for an infection quickly enough to support timely clinical decision-making. Against that backdrop, research into blood-based diagnostics has the potential to contribute to the wider effort to improve how clinicians understand and respond to the condition.

The GeSIC project brings together the team of Professor Maïwenn Kersaudy-Kerhoas from the School of Engineering and Physical Sciences and a clinical team at the University Medical Center Mainz, with the University of Mainz acting as the university partner.

The collaboration is focused on advancing a rapid blood-based DNA sequencing approach designed to identify infection-causing pathogens, bringing together Heriot-Watt's engineering expertise and clinical knowledge from intensive care. The newly funded phase is expected to begin in January and will extend work already underway between the teams.

The partnership has already resulted in a medical case report published earlier this year in the BMJ Case Reports, produced collaboratively by Professor Kersaudy-Kerhoas and the medical team in Mainz.

The medical case study provides an early real-world example of the potential of the approach. In a patient being treated for sepsis whose standard blood cultures remained negative throughout her stay in hospital, the blood-based DNA sequencing test developed by Heriot-Watt University identified two likely infection-causing pathogens, several days before they were confirmed through other bodily fluid cultures.

Conventional culture techniques can take between two and seven days to identify a pathogen and may return negative results in over 60% of suspected sepsis cases. The case report explored a different route, looking for fragments of microbial DNA circulating in a patient's blood rather than waiting for organisms to grow in a laboratory culture.

Using the iSEP-SEQ research workflow, the complete analytical process took approximately six hours. Although the result was generated within a research setting and did not guide treatment in this case, the findings highlight the potential for this type of technology to support faster pathogen identification alongside existing diagnostics, particularly where conventional cultures are delayed or negative.

The research and the wider partnership was showcased on 2 September when Clemens Hoch, Minister for Science, Further Education and Health in Rheinland-Pfalz, visited Heriot-Watt's Edinburgh campus as part of a three-day visit to Scotland.

Minister Hoch and a delegation of academic and clinical representatives heard about the clinical context for GeSIC, the work completed so far and the next phase of the project.

The visit also included the National Robotarium, with a focus on healthcare and robotics, and comes amid wider interest from Rheinland-Pfalz in collaboration with Scottish partners.

Professor Maïwenn Kersaudy-Kerhoas, Professor in Microfluidic Engineering and co-academic lead of Heriot-Watt University’s Global Research Institute in Health and Care Technologies said: "The fast identification of pathogens remains one of the most complex challenges facing healthcare systems around the world. Our iSEP-SEQ approach can accelerate the identification of any pathogen in circulation in the patient and has the potential to make a meaningful difference by enabling better patient management and correct therapeutics.

“This award gives us the opportunity to take an established international collaboration into its next phase and continue bringing engineering and clinical expertise together around a significant challenge in intensive care. Our work with colleagues in Mainz has already created valuable links between the laboratory and the clinical environment. The next stage will allow us to build on that foundation, with accelerating clinical validation for patient benefits at the heart of the collaboration.

"We are also proud to welcome Minister Hoch to Edinburgh. His visit is an opportunity to show the work firsthand and to demonstrate what can be achieved when researchers and clinicians across Scotland and Rheinland-Pfalz are connected around shared challenges."

Clemens Hoch, Minister for Science, Further Education and Health, said: “This flagship project marks another important step in strengthening our cooperation with Scotland, and I am grateful to the senior faculty members of Heriot-Watt University for sharing their valuable expertise.

“Scotland is recognised internationally for its success in combining public awareness with coordinated healthcare strategies to improve outcomes for patients with sepsis. Early and accurate diagnosis is essential to ensuring timely, targeted treatment and improving survival.

“Through the GeSIC project, we aim to understand the factors behind Scotland’s success and translate these insights into meaningful, measurable improvements for patients in Germany. By bringing together Scotland’s expertise in sepsis care and innovative genomic technologies developed through our research collaboration, we hope to generate robust evidence that can inform clinical practice, strengthen treatment guidelines and deliver lasting benefits for patients in Rheinland-Pfalz and Scotland.

Professor Nana-Maria Wagner, Director of the Department of Anaesthesiology in Mainz, said: “It is a wonderful opportunity for us as intensivists and critical care physicians to be in touch with cutting edge techniques that have the potential to fasten and ease diagnostics and clinical management of patients that are severely ill. Bringing together the clinical aspects of patient care and technology in such an international team can truly move our field forward.”

Professor Chris Turney, Deputy Principal for Research and Innovation at Heriot-Watt University, said: “This project is an excellent example of the type of research Heriot-Watt wants to accelerate through our Global Research Institute in Health and Care Technologies.

“Our strength lies in bringing engineering expertise together with clinicians and international partners to tackle significant healthcare challenges and turn innovative research into solutions that can ultimately make a difference to patients. The collaboration with Mainz also reflects our ambition to work across disciplines and borders, building the partnerships needed to move promising health technologies towards real-world application and, importantly, ensure their benefits can be delivered at scale.”

Heriot-Watt University’s Global Research Institute in Health and Care Technologies brings together more than 300 academics, researchers and professional staff to develop practical solutions to some of the world’s most pressing health challenges. Working across engineering, healthcare and the social sciences, the Institute focuses on accelerating new technologies from research into real-world care, with patients at the heart of the process. Its work spans areas including cancer, healthy ageing, chronic disease management, mental health and global health, supported by partnerships with clinicians, industry, charities and researchers around the world.

New Test Could Speed Diagnosis of Hard-to-Detect Lung Infections

2026-10-13T03:36:56.180-04:00

Image created by Dr. Michael J. Miller

A little-known chronic lung disease is silently emerging as a growing public health threat in the U.S. and around the world. Nontuberculous mycobacteria, a group of 200 known environmental bacteria found in soil and water, including household plumbing, can cause chronic, tuberculosis-like lung infections. NTM lung disease afflicts as many as 300,000 Americans a year.

Now, researchers at Tulane University have developed a new diagnostic platform that can identify the precise species of bacteria causing a patient's infection in just a few hours, a dramatic improvement over the current standard, which can take four to six weeks.

"Diagnosing NTM lung disease is very slow, and patients are left with a significant physical and emotional burden because they don't know the answer or what the treatment plan should be," said lead investigator Bo Ning, assistant professor of molecular biology at Tulane University School of Medicine. "Clinicians need a faster test that will allow them to accurately identify the specific type of bacteria that is causing the patient's lung disease."

The team's diagnostic platform, called CANDI or CRISPR-assisted Nanodroplet Differential Identification, was described in a new study published in Science Translational Medicine.

Misdiagnosis delays proper treatment

NTM lung disease is closely associated with its more notorious relative, tuberculosis. Symptoms are similar enough that it is often misdiagnosed as TB. The problem is that treatments for TB and NTM lung disease differ significantly, and a misdiagnosis can put patients on an incorrect treatment plan for months. This is particularly concerning for a disease that has a five-year mortality rate approaching 18% and is becoming so prevalent that it outpaces TB infections in many developed countries, including the U.S., England and Japan.

"NTM lung disease is becoming more and more common, and the current detection approach is so difficult that patients end up suffering," Ning said. "This new method will help clinicians get patients started on the right treatment faster."

CANDI uses mucus from the lungs and other respiratory fluids to test for up to 15 clinically relevant species and subspecies of NTM bacteria at once. In a study of 230 clinical samples, the platform correctly identified NTM species and subspecies with 97.3% accuracy.

Who faces the greatest risk

NTM bacteria occur naturally in the environment and typically pose little threat to healthy people. But the infection can hit certain groups much harder—including older adults, those with underlying lung conditions and people with weakened immune systems, such as those undergoing cancer treatment or with HIV.

Trends also point to the disease becoming more prevalent in the South, where cases are already higher than in other parts of the U.S., as rising temperatures and humidity create more favorable conditions for the bacteria.

Potential beyond NTM infections

The test Tulane researchers developed was designed to specifically target NTM infections, but the novelty of the method may allow for expansion into other difficult-to-diagnose diseases.

"Think of this as a platform technology," Ning said. "We can do multiplex bacterial identification for other infections as well."

Reference

Hongquan Gou et al, A rapid CRISPR-based nanodroplet assay enables direct clinical identification of mycobacteria species, Science Translational Medicine (2026). DOI: 10.1126/scitranslmed.aef2648. www.science.org/doi/10.1126/scitranslmed.aef2648

Abstract

The global incidence and mortality of nontuberculous mycobacterial infections have risen sharply with population aging. In some regions, they are now surpassing Mycobacterium tuberculosis complex infections, imposing a substantial clinical and economic burden. Because nontuberous mycobacteria exhibit species-level heterogeneity and require prolonged culture for identification, their diagnosis remains slow and is frequently inaccurate. Here, we describe a multiplexed clustered regularly interspaced short palindromic repeats (CRISPR)–assisted nanodroplet differential identification (CANDI) diagnostic platform that integrates species-agnostic target amplification with species-specific CRISPR-associated protein 12a (Cas12a) detection in fluorescence-barcoded nanodroplets. By spatially compartmentalizing CRISPR reactions into color-encoded nanodroplets, CANDI overcomes the multiplexing limitations of conventional CRISPR diagnostics and enables simultaneous interrogation of multiple mycobacterial targets in a single assay. We designed a 16-plex panel that distinguishes 15 clinically relevant Mycobacterium species and subspecies. CANDI achieved high analytical sensitivity and accurate discrimination in samples containing coinfections with multiple species or subspecies. When applied to 230 clinical specimens, including sputum, tracheal aspirates, and other respiratory fluids, CANDI delivered subspecies-level results within 3.5 hours, achieving 97.08% sensitivity and 99.7% specificity relative to culture-based identification. By combining multiplexed, high-specificity CRISPR detection with scalable droplet-based engineering, CANDI has the potential to overcome the culture dependency of current diagnostics and enable species- and subspecies-level identification across the genetically complex Mycobacterium genus, offering a clinically adaptable framework for rapid, precision diagnosis of mycobacterial infections.

Bacterial Invasion Proteins Could Improve Detection of Live Crop Pathogens

2026-10-13T03:31:45.854-04:00

Image created by Dr. Michael J. Miller

Scientists, clinicians, seed companies and others may soon have a new tool in their toolkit for detecting bacterial pathogens, thanks to a new study by researchers in Penn State's College of Agricultural Sciences. The research, published in Journal of Microbiological Methods, details a new method for detecting Pseudomonas syringae, a common bacterium that infects a wide variety of crops, vegetables and woody ornamentals.

The cost is similar to that of the traditional method, called enzyme-linked immunosorbent assay, or ELISA. However, the researchers' new method, called enzyme-linked chaperone assay, or ELCA, has the potential to detect only living bacteria, making it more sensitive and accurate.

Rachel Herschlag, lead author on the paper who earned her doctorate in plant pathology at Penn State, said the study is a proof of concept, with an opportunity in the future to adapt the method for detecting other pathogens.

"Now that we've documented this method, researchers can adapt it for other bacteria that have a similar system," she said. "This could include some animal and human pathogens in addition to those affecting plants, expanding its potential utility in clinical, agricultural, environmental and biotechnology applications."

Borrowing bacterial invasion machinery

Carolee Bull, professor of bacterial systematics and plant pathology and co-author of the paper, said many current diagnostic tests, like ELISA, work by using animal-derived antibodies produced to fight an infection. However, the new method uses a different mechanism.

"I asked Herschlag to think creatively about bacterial pathogen detection to develop novel strategies for detecting Pseudomonas strains and, potentially, other types of bacteria," Bull said. "She went on to develop this technique by using the same mechanism that many pathogens use to invade their hosts to allow us to recognize them."

When bacteria attack plants, animals or people, they deliver specialized proteins called effectors into the host's cells, she explained. These proteins help the bacteria overcome the host's immune system and invade the organism.

To do this efficiently, many bacteria rely on helper proteins called chaperones that recognize and bind—like a lock and key—to these effectors with high specificity. The chaperones protect the effectors, which are vulnerable to degradation.

The new method relies on this process rather than the labor-intensive process of harvesting antibodies in a lab.

"Unlike other diagnostics, this new method uses a bacterial chaperone to recognize and bind to its matching effector protein," Herschlag said. "This then triggers a colorless solution to turn yellow, providing an easy visual signal that the target is present. And because this interaction is highly specific, the method can reliably detect its intended target."

Distinguishing live bacteria

Other diagnostic approaches, the researchers explained, work by detecting specific parts of the cell, such as DNA. However, because these parts exist even after bacteria have died, this can produce false positives. The researchers said what is most helpful about this new technique is that effectors are only produced by living bacteria.

"Because effectors are thought to degrade quickly, effectors from dead bacteria likely do not survive very long," Herschlag said. "Thus, ELCA can detect living bacteria, or in other words, those that are actually capable of causing infections. Indeed, we showed that living bacteria had higher levels of detection than dead bacteria."

In the future, the researchers said the next steps will be to optimize the test by reducing background signals and adapt the concept for other pathogens that have comparable chaperone-effector systems, including some relevant to agriculture, veterinary science and human health.

Reference

Rachel Herschlag et al, Enzyme-linked chaperone assay: a novel bacterial detection method for viability discrimination, Journal of Microbiological Methods (2026). DOI: 10.1016/j.mimet.2026.107681

Abstract

This study introduces enzyme-linked chaperone assay (ELCA), a novel antibody-free biosensor targeting Pseudomonas syringae effector-chaperone interactions for diagnostic purposes. Validated via a linear mixed effects model across three independent experiments, ELCA achieved a 102 CFU/mL limit of detection, successfully discriminating live target cells from dead populations and non-target strains.

Graphical Abstract

Respirometric Sensor Sachets Detect Surface Bacteria in Just 10 Hours

2026-09-17T05:09:17.247-04:00

Image created by Dr. Michael J. Miller

Surface hygiene monitoring traditionally requires up to 72 hours of laboratory incubation, limiting rapid microbial assessment. In light of this, recent work has introduced a field-deployable respirometric sensor sachet designed to detect and quantify bacterial loads directly from environmental swabs.

This device reduces the detection period to under 10 hours and covers a microbial detection range of 0-6 log₁₀ CFU/cm², supporting on-site microbial monitoring without complex laboratory infrastructure. These findings were published in Biosensors.

Oxygen Consumption Reveals Bacterial Activity

When bacterial biofilms form on abiotic surfaces, they create contamination risks in healthcare, pharma, and food production environments. Conventional hygiene monitoring often relies on culture-based agar methods, which require lengthy incubation periods before microbial loads can be determined. These delays hinder timely decisions regarding surface cleanliness.

Micro-respirometry (µR) provides an alternative approach by estimating microbial activity through changes in oxygen consumption.

Optical oxygen sensors use phosphorescent dyes whose emission is quenched by oxygen. As bacteria respire and consume oxygen in a contained environment, researchers can optically detect the resulting changes. The measured oxygen response serves as an indicator of microbial activity and, with appropriate calibration, can be transformed into bacterial load estimates.

Turning Surface Swabs Into Rapid Sensor Tests

Researchers constructed an integrated swab-to-sachet workflow designed to replicate standard microbiological testing conditions. The system utilized a planar optical oxygen sensor based on a Pt(II)-benzoporphyrin dye embedded in a polystyrene matrix.

The sensing material was deposited onto microporous polyvinylidene fluoride (PVDF) membranes and thermally stabilized for operational use.

The sensor patches were incorporated into Mylar film sachets with a transparent front window for optical measurements and a metalized backing to regulate heat transfer. Each sterile sachet contained a sampling sponge and plate count broth, forming a sealed environment in which microbial respiration could be monitored.

For validation, stainless-steel surfaces were inoculated with Escherichia coli (E. coli), while natural microflora were collected from raw meat and fresh vegetables. Surface sampling adhered to ISO 18593 swabbing guidelines. After sealing the swabs inside the sachets and incubating them at 30°C, a FireStingGO2 handheld optical reader recorded phase-shift fluorometric measurements. The sensors were scanned hourly for 10 hours, allowing researchers to track oxygen depletion without opening the sachets.

Validating Quantitative Metrics and Kinetic Data

Empirical testing demonstrated that oxygen depletion measured by the sensor sachets correlated well with bacterial concentration on the sampled surfaces. The time-to-threshold (TT) response exhibited a linear relationship with total viable counts (TVC), enabling microbial load estimation from the respirometric signal.

Compared with the ISO 4833:2013 reference method, the platform achieved Pearson correlation coefficients above 0.94 and quantified microbial loads across a range of 0-6 log₁₀ CFU/cm². The median limit of detection (LOD50) was 2.69 log₁₀ CFU/cm², with no false-positive results. Bland-Altman analysis indicated a mean difference of +0.06 log CFU/cm² between the sensor and the reference method, suggesting minimal systematic bias.

The sensor maintained consistent optical responses when tested with both E. coli and mixed microbial communities. A phase-shift threshold of 25 ° balanced signal-to-noise characteristics with detection time, enabling quantitative measurements during the monitoring period.

Putting Sensor Sachets to the Test: Pilot Deployment in Meat Processing

The practical use of the optical sensor system was evaluated in a pilot deployment at a commercial meat-processing facility. Operators used the technology to assess post-cleaning hygiene and in-process contamination on high-touch surfaces, including stainless-steel cutting equipment.

This approach could prove relevant for hygiene monitoring in healthcare and pharmaceutical manufacturing, where faster on-site measurements could facilitate timely sanitation decisions. With an estimated operating cost of approximately €6.50–7.50 per sample, the system presents a cost-effective option for repeated environmental monitoring.

However, further testing across different facilities and surface types is necessary to establish its scalability and effectiveness.

Toward Faster On-Site Hygiene Monitoring

The study developed a field-deployable respirometric sachet that enables quantitative bacterial monitoring within 10 hours, providing a significant alternative to conventional culture-based methods.

By measuring oxygen consumption through optical sensing, the platform effectively links microbial respiration with surface bacterial load and shows strong agreement with the ISO 4833:2013 reference method. Its portable format and relatively low per-sample cost support its potential for on-site hygiene monitoring.

Future work could integrate the optical readouts with automated data acquisition and machine learning to enhance anomaly detection. Additionally, the sachet could be adapted with selective growth media or biological recognition elements to target specific foodborne pathogens. Such modifications could transition the platform from general microbial load assessment toward more selective pathogen detection, although their performance would require further validation.

Reference

Ferraro, V., et al. (2026). Rapid Bacterial Detection on Surfaces by Field-Deployable Respirometric Sensor Sachets. Biosensors, 16(9), 503. DOI: 10.3390/bios16090503, https://www.mdpi.com/2079-6374/16/9/503.

Abstract

Monitoring bacterial contamination on surfaces is critical for hygiene and safety assurance in food, healthcare, and pharmaceutical settings, although routine methods still remain slow and laboratory dependent. Here, we report a portable respirometric platform based on sealed sensor sachets incorporating optical oxygen sensors to rapidly detect and quantify total aerobic viable counts (TVC) from swabbed surfaces. Following standardized surface swabbing, samples were incubated in the sachets and oxygen depletion kinetics were recorded with a handheld reader and microbial activity was inferred from oxygen consumption. Reference quantification was obtained by serial dilution and aerobic plate counting, enabling direct benchmarking of the respirometric readout against an industry-accepted culture method such as ISO 4833:2013. The platform demonstrated strong agreement with plate counts (R2 > 0.94), achieving a median detection limit of 2.69 log₁₀ CFU/cm² and a dynamic range of 0–6 log₁₀ CFU/cm² for surface-associated microbial loads. The sensor system was also applied in a semi-industrial setting in a meat processing plant. Across replicate measurements, the assay provided consistent kinetic signatures and quantitative outputs, suitable for rapid and practical decision-making. Compared with traditional culture-based approaches (requiring up to 72 h), the respirometric sachets delivered actionable results within 10 h using a portable, low-infrastructure workflow, supporting rapid on-site hygiene verification and sanitation control.

Korean Scientists Plate Bacteria with Metal to Diagnose Infections Within 1 Hour

2026-09-11T07:32:02.797-04:00

Image created by Dr. Michael J. Miller

An analytical technology has been developed that can detect ultra-trace amounts of bacteria within one hour, helping enable rapid response to severe infectious diseases such as sepsis. Because the technology can also assess how bacteria respond to antibiotics within two hours, it is expected to support more precise prescription of drugs.

The Korea Institute of Materials Science (KIMS) announced on the 9th that a research team led by Senior Researcher Minyoung Lee and Principal Researcher Sungkyu Park of the Bio-Health Materials Research Division has developed a label-free surface-enhanced Raman scattering (SERS) analytical technology that rapidly detects ultra-low concentrations of live bacteria. The research results were published in the August 16 issue of the international journal 'Small Methods'.

Viruses that invade the body multiply exponentially and can adversely affect the entire body within a few hours. It is crucial to quickly identify the causative pathogen and administer antibiotics. However, in the early stages of infection, the amount of bacteria in the blood is so low that it is difficult to directly detect them.

Currently, bacteria are detected by collecting blood samples and then culturing the bacteria present in them. This approach has the limitation that it takes tens of hours to several days to determine the causative pathogen and identify the appropriate antibiotic.

Molecules that make up bacteria emit a 'Raman signal' when they interact with externally applied light. Raman signals appear differently depending on the type of bacteria.

The research team designed a 'plasmonic metal nanostructure' with the ability to amplify Raman signals so that it spontaneously grows while enveloping the surface and surroundings of bacteria. In practice, they flowed an electric current through a solution containing both bacteria and metal nanomaterials, effectively 'plating' the bacteria with the metal nanomaterials.

The team implemented on a single substrate all the functions of plating the bacteria, filtering and concentrating the plated bacteria in one place, and detecting the Raman signals emitted from the concentrated bacteria. As a result, they were able to detect even very small bacterial loads of 1–10 CFU (colony-forming units; 1 CFU corresponds to one bacterium capable of forming a colony) per 1 mL within one hour.

They also verified the detection performance using simulated samples in which live bacteria were added to serum and urine.

The researchers noted that by treating live bacteria with antibiotics and then reading changes in the Raman signals, it is possible to evaluate bacterial responses to antibiotics within two hours.

Senior Researcher Lee said, “The key to this study is that we overcame the limits of detecting ultra-trace bacteria by growing nanostructures directly where the bacteria are,” adding, “In the future, we aim to contribute to faster treatment decisions for sepsis by rapidly detecting the causative pathogen in the blood of actual infected patients and assessing antibiotic responses.”

Reference

E.-B. Jang, J. Y. Lee, H. Chung, and M.-Y. Lee, Filtration-Based SERS With In Situ Gold Growth in Bacterial Microenvironment for Rapid Detection of Low-Level Bacteria. Small Methods (2026): e70972. https://doi.org/10.1002/smtd.70972

Abstract

Rapid bacterial detection and timely antimicrobial susceptibility assessment are essential for effective infection management, yet remain challenging under clinically relevant low-concentration conditions. Here, we present a filtration-based surface-enhanced Raman scattering (SERS) platform based on a microenvironment engineering strategy, in which plasmonic nanostructures are dynamically formed within the bacterial microenvironment. Using a gold nanostructured glass microfiber membrane, the platform enables large-volume sample processing through size-selective bacterial capture, followed by in situ gold growth that generates bacteria-centered plasmonic hotspots directly around bacterial cells. This architecture fundamentally overcomes the intrinsic hotspot–bacteria mismatch in conventional substrate-based SERS systems, enabling highly sensitive and selective detection while effectively suppressing nonspecific interference in complex biological matrices. To further address sampling limitations at low concentrations, a minimal pre-culture strategy was introduced to enhance bacterial surface occupancy, improving the detection limit from 103 to 101 CFU/mL. In addition, SERS signal intensity directly reflects bacterial viability, allowing rapid differentiation of antimicrobial responses and enabling susceptibility assessment within 2 h. By integrating physical enrichment with microenvironment-driven signal amplification, this platform simultaneously achieves low-level detection and rapid antimicrobial susceptibility testing within a single system. These results highlight its potential as a next-generation integrated diagnostic platform for clinical sample analysis.

Dried-Blood Method Enables Faster Diagnosis of Zika and Hepatitis C

2026-09-09T10:55:00.051-04:00

Image created by Dr. Michael J. Miller

A new diagnostic method enables the detection of hepatitis C and Zika virus directly from dried whole blood, potentially bringing faster testing to locations where conventional laboratory diagnostics are difficult to access. In a study published in Science Advances, the new approach could overcome a major barrier to accessibility by eliminating the need for complex sample preparation steps.

“In many parts of the world, delayed diagnosis undermines the impact of effective treatments,” said Rashid Bashir, PhD, professor of bioengineering at the University of Illinois Urbana-Champaign. “Despite substantial advancements of molecular diagnostics, clinical outcomes are often not improved because test results arrive after the critical window for intervention has passed, rendering the diagnosis too late to matter. This failure highlights a global need for diagnostics that are not only accurate but also rapid, accessible, and deployable.”

The need is particularly urgent for hepatitis C virus, which can be cured in more than 95% of cases when antivirals are administered in time. However, only about 20% of infected people in high-income countries are diagnosed, and just seven percent receive treatment. In low-income regions, which account for nearly 80% of global hepatitis C infections, fewer than one percent of people are diagnosed or treated. Zika virus presents another equally pressing challenge. Detecting infection early can help identify pregnancies at risk of severe congenital complications, including microcephaly.

Bashir’s team sought to address a longstanding bottleneck in point-of-care diagnostics. While molecular detection technologies have progressed rapidly, sample preparation has remained heavily dependent on centralized laboratories. Diagnosing viral infections from whole blood samples typically requires centrifugation, extraction and purification steps requiring specialized equipment and trained personnel, followed by cold storage and transport. For techniques such as RT-PCR, preparation can account for up to 90% of hands-on processing time.

The new method bypasses these steps by drying the blood sample in a way that captures and stabilizes the target RNA molecules. RNA amplification can then be performed directly on the sample using dried primers and reagents, eliminating the need for cold-chain logistics.

“Throughout the entire diagnostic workflow, only a simple heater is needed for both preparation and amplification, along with a compact, low-cost fluorescence reader to detect the signal,” said Bashir. “This platform maintains accuracy comparable to standard-of-care methods while substantially improving accessibility and availability.”

The system can also support multiplexed detection, allowing multiple viral targets to be identified from a single sample. In laboratory tests, the researchers reported sensitivity down to 1 IU per microliter for hepatitis C virus and 10 copies per microliter for Zika virus, with performance comparable to that of more intensive sample preparation methods.

“Our dried blood–based diagnostic platform addresses the long-standing bottleneck of sample preparation by enabling direct RNA detection from whole blood, combining simplicity, sensitivity and deployability to realize the full potential of true POC diagnostics in both high-resource and low-resource settings,” said Bashir.

Reference

Jongwon Lim et al., Amplification of RNA for identification of Zika and HCV in whole blood. Sci. Adv., 2026, Vol. 12, Issue 6. DOI:10.1126/sciadv.aeb6129

Abstract

Direct RNA amplification from whole blood is fundamentally limited by rapid enzymatic degradation and inhibitory matrix effects. Here, we present a blood drying protocol that enables sensitive and robust RNA detection without the need for extraction, purification, or cold-chain logistics. Using whole blood, the platform achieves high detection sensitivity, down to 10 copies per microliter for Zika virus and 1 international unit per microliter for hepatitis C virus (HCV). We further demonstrate that the protocol can be scaled to larger blood volumes and achieve single-copy sensitivity without any sample loss. This is accomplished through thermal treatments of the sample combined with a primer-limited reverse transcription step, which together stabilize RNA within a dried blood matrix and permit spatially resolved enzymatic amplification. The system supports multiplexed detection from a single sample, enabling simultaneous identification of multiple targets. Separately, we introduce a concept wherein the very few copies of the preserved RNA within the matrix can be accessed repeatedly for molecular analysis. Furthermore, we demonstrated the detection of Zika and HCV using a portable fluorometer for point-of-care (POC) uses. With lyophilized reagents and minimal instrumentation such as a heater and an inexpensive portable fluorometer, this platform enables robust, reusable, and field-deployable diagnostics, advancing toward truly accessible on-site RNA testing in urgent care or low-resource settings from whole blood.

Ancient Dujiangyan Water Wisdom Inspires AI Microfluidic Chip to Detect Bacteria

2026-09-09T10:44:04.495-04:00

Image created by Dr. Michael J. Miller

Antimicrobial resistance is one of the most urgent threats to global public health. In clinical practice, antibiotic treatment sometimes fails even when bacteria are classified as susceptible by conventional antimicrobial susceptibility testing. One important reason is that traditional tests mainly rely on the minimum inhibitory concentration, or MIC, and may overlook bacterial subpopulations that survive antibiotic exposure, including persistent, tolerant, and heteroresistant populations. These hidden populations can survive under drug pressure and may contribute to treatment failure and the later emergence of stable resistance.

To address this challenge, a research team led by Professor Bi-feng Liu developed a deep learning-based microfluidic rapid phenotypic AST system, named DP-AST. The system was inspired by the “six-four water diversion” principle of the ancient Dujiangyan irrigation system in China, which has long been known for its efficient and self-regulated water distribution. By translating this principle into microfluidic chip design, the team created a hand-driven concentration-gradient generator that can automatically produce multiple antibiotic concentrations in a portable format.

Unlike many existing rapid AST platforms that rely on colorimetric reagents, professional instruments, or high-end microscopes, DP-AST uses bacterial micro-enrichment area as a phenotypic readout. This design enables the system to calculate bacterial growth activity and generate concentration-effect curves, which describe how bacterial growth changes across different antibiotic concentrations. By combining MIC, growth activity, and concentration-effect curve analysis, DP-AST provides a more refined view of bacterial drug response and can identify sub-resistant bacterial populations that are difficult to detect using conventional MIC-based testing alone.

The platform also integrates smartphone-based signal acquisition with deep learning image analysis. Bacterial growth signals can be captured using a portable smartphone imaging setup, and the deep learning algorithm automatically analyzes the images and classifies the drug susceptibility results. This combination improves portability and supports low-instrumentation testing, which is especially important for resource-limited clinical settings where access to large laboratory instruments may be restricted.

Beyond platform development, the team further investigated the biological mechanisms underlying sub-resistance. Using integrated proteomic and transcriptomic analyses, they compared a sub-resistant Escherichia coli strain with a sensitive strain. Although the two strains showed highly similar genomic sequences, they displayed marked differences in gene and protein expression. The sub-resistant strain showed increased expression of several antibiotic response-related genes and proteins, including outer membrane protein OmpA and efflux system-related components, suggesting that expression-level regulation may help bacteria survive antibiotic pressure even without clear conventional resistance markers.

Further multi-omics analysis indicated that many of the transcriptional and proteomic changes in the sub-resistant strain were associated with intracellular acid regulation. The team experimentally identified gadE, a central regulator of acid tolerance in E. coli, as an important factor involved in bacterial survival under antibiotic stress. This finding suggests that acid tolerance-related regulatory networks may contribute to bacterial adaptation during antibiotic exposure and provides a new direction for studying sub-resistance mechanisms.

Overall, this study establishes a portable, AI-assisted microfluidic platform for rapid phenotypic antibiotic susceptibility testing. DP-AST can provide susceptibility results within 3 hours and, more importantly, enables multi-parameter profiling for the identification of hidden sub-resistant bacteria. The work offers a new technical strategy for more precise antibiotic treatment, improved resistance-risk assessment, and future studies on the early evolution of antimicrobial resistance.

Reference

Shunji Li, et al., Deep learning-based microfluidic rapid phenotypic AST uncovers early resistance trajectories, Science Bulletin, 2026, ISSN 2095-9273, https://doi.org/10.1016/j.scib.2026.08.079.

New Test Could Identify Sepsis-causing Bacteria in Just Seven Hours

2026-09-09T10:25:58.296-04:00

Image created by Dr. Michael J. Miller

Sepsis, a life-threatening condition that stems from the body trying to fight off an infection, plagues over 1.5 million patients a year in the U.S. alone, with one in three deaths recorded in the hospital attributed in part to sepsis. Despite the severity of the condition, which can kill in as little as 12 hours, it takes between two and seven days for most traditional approaches to definitively identify sepsis-causing bacteria in blood infections.

A team led by an engineer at Penn State has developed a way to condense the days-long diagnosis timeline to just hours. The new approach rapidly grows the bacteria present in collected blood samples, intermittently analyzing the samples with advanced techniques that help scientists identify the specific pathogens causing infection. The researchers reported in a paper published in Science Advances that their approach facilitated faster diagnosis and could help clinical decision-making that avoids worsening antibiotic resistance in the bacterial strains causing infections.

“For a physician, ‘what actually caused this infection and how should it be treated?’ are the most important questions when it comes to the timely management of a bloodstream infection,” said corresponding author Pak Kin Wong, professor of biomedical engineering and of mechanical engineering. “We are developing a comprehensive diagnostic platform that rapidly tells physicians both the specific bacteria causing a bloodstream infection, as well as the ideal antibiotic to treat the infection, before sepsis ever sets in.”

With a bloodstream infection, it is critical to find and neutralize the cause as quickly as possible, before it triggers a septic response from the body, Wong explained. Physicians must not only detect the presence of bacteria — they must also identify the specific pathogens, as well as the best antibiotic for treatment. The dire stakes of a false positive or negative complicate this further, as every hour counts when treating a bloodstream infection, Wong said.

“This is not like a COVID test, where we are checking to see a specific virus is present in a patient’s system,” Wong explained. “Many different bacteria can cause sepsis, and they may respond differently to treatment. Therefore, analysis must be thorough to ensure the best treatment is prescribed.”

Bloodstream infections are responsible for about 40% of all sepsis cases that lead to hospitalization. The complex biological makeup of the blood and the low pathogen loads needed to trigger sepsis make pinning the cause of a bloodstream infection time-consuming. To identify the bacteria causing a bloodstream infection, current best practices require bacterial culturing: Blood samples are enriched over a few days so that present bacteria grow to measurable levels. Then, technicians further analyze the samples to identify the specific bacteria, a process that adds another day or two to diagnosis.

Streamlining bloodstream infection diagnosis is not a novel idea, with several commercial products offering culture-free blood testing already on the market. However, to reduce diagnostic time, these products provide less comprehensive and less sensitive readings, Wong said.

To accelerate diagnosis without sacrificing accuracy, the team had to rethink culturing. Traditionally, bacterial growth in a cultured blood sample is measured through the carbon dioxide released by the bacteria. When this change in carbon dioxide levels confirms the presence of pathogens, bacteria are separated from the blood sample and analyzed. The team’s new approach, called STREAM, fast-tracks this culturing by facilitating rapid bacterial growth, while isolating and analyzing the pathogens inside simultaneously — blood samples are mixed in a specialized “broth” that separates whole blood cells from the individual bacteria found in the sample during culturing.

Molecular analysis, a process known formally as barcoding, allows the team to detect tiny fragments of genetic information from isolated bacteria. From these smaller samples collected intermittently during culturing, the researchers can name the specific bacterial species present.

“Instead of sampling at a particular endpoint after culture, we collect samples at multiple time points throughout the culture process,” Wong said. “This approach maintains robust bacterial detection while minimizing the time to results.”

These smaller samples are then subjected to a series of new, single-cell-based techniques that allow researchers to analyze a bacterium with microscopic imaging. These images are then analyzed by computer algorithms the team developed to eliminate visual clutter from the images, helping physicians determine the specific bacteria causing infection. These analyses also suggest which antibiotics the strain is susceptible to and any existing antibiotic resistance the strain may have.

The team tested their approach with about 100 positive bloodstream infection samples donated by patients and stored at Penn State Hershey Medical Center’s clinical microbiology laboratory. The researchers found that combining these techniques offered comprehensive diagnosis in as little as seven hours, enabling confident identification of the infection causing pathogen from whole blood.

“Using single-cell analysis technologies developed by our team, we can accurately identify pathogens in blood even when they are present at very low concentrations,” Wong said. “Identifying the pathogen alone is not enough; we must also determine which antibiotics are effective against it. This led us to integrate antibiotic susceptibility testing into the same process, providing physicians with the information needed to select the most appropriate treatment.”

Wong said it is important to note that just over 4% of these samples were classified as "very major errors," meaning a bacterium was inaccurately identified as resistant or susceptible to an antibiotic, which could cause the prescribed treatment to be ineffective. The team acknowledged that although some large hurdles need to be addressed before clinical implementation — including this relatively high major error rate and difficulties that could arise when testing blood from patients who had already received antibiotics — the framework offers a promising foundation for detecting and treating a leading cause of death around the world faster than ever before.

“We are integrating artificial intelligence and lab automation to make the entire process even more efficient and accurate,” Wong said. “This framework is scalable, so the list of pathogens we can detect could feasibly be expanded. We believe we could adapt this approach to identify infections originating from sources other than bacteria, like fungal infections. We hope to work closely with physicians at Penn State College of Medicine to move towards more clinical studies and, eventually, clinical adoption.”

Other co-authors affiliated with Penn State include April M. Bobenchik, associate professor of pathology and laboratory medicine; Siew Mei Chin, a biomedical engineering doctoral candidate at the time of work who has since graduated and is now a postdoctoral researcher at Stanford University’s School of Medicine; Evgenii Kovtunov, a postdoctoral researcher at the time of the work who is now a medical and public health microbiology fellow at the University of Rochester; and Emma Epiphaniou, an undergraduate researcher at the time of the work who earned her bachelor’s degree in biomedical engineering from Penn State.

Additional co-authors include Samuel Yang, professor of emergency medicine; Joseph C. Liao, professor of urology; and Kathleen E. Mach, a senior research scientist, all with Stanford University’s School of Medicine.

This work was supported by the One Health Microbiome Center Interdisciplinary Innovation Fellowship and the Leighton Riess Graduate Fellowship, as well as the National Institutes of Health’s National Institute of Allergy and Infectious Diseases under award number R01AI153133. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders.

Reference

Rapid and robust diagnosis of bloodstream infections by single-cell analysis. Siew Mei Chin, et. Al. Sciences Advances, 26 Aug 2026, Vol 12, Issue 35, DOI: 10.1126/sciadv.aeh3580

Abstract

Sepsis, a common consequence of bloodstream infection (BSI), is a leading cause of global mortality and morbidity. Diagnosis is challenging and slow due to extremely low pathogen loads and reliance on time-consuming blood culture, necessitating empiric therapy that worsens outcomes and antimicrobial resistance. Here, we introduce sedimentation-assisted tandem rocking and enrichment for analysis and monitoring (STREAM), a platform that efficiently isolates and enriches pathogens near their natural doubling times directly from whole blood. By integrating STREAM with single-cell analysis, we achieved rapid and robust diagnosis of BSI from whole-blood samples. Using molecularly barcoded sequential fluorescence in situ hybridization, pathogen identification in 104 positive blood culture samples showed 96.15% concordance with clinical laboratory results. Gel-based single-cell antimicrobial susceptibility testing yields 97.96% essential agreement and 93.9% categorical agreement across 219 drug-dose combinations. The platform enabled comprehensive BSI diagnosis directly from whole blood, achieving a detection limit as low as 0.1 to 1 CFU/ml and delivering full diagnostic results within 6.75 to 17 hours.

A New Rapid Test Helping Ethiopian Researchers Stay Ahead of a Wheat Disease Threat

2026-09-09T10:01:27.963-04:00

Image created by Dr. Michael J. Miller

Ethiopian researchers are using a new diagnostic test created by John Innes Centre researchers to identify wheat blast, a growing wheat disease threat. The system provides a simple test that can confirm within just 90 minutes whether the infected material contains wheat blast, a devastating disease that spreads on the wind and can cause up to 100% losses in severe outbreaks.

Wheat blast is a fungal disease (Magnaporthe oryzae pathotype Triticum) that spreads on the wind over great distances and thrives in warm, humid conditions. Infection causes wheat grains to become shrivelled or absent within a week. The disease is a particular concern for Ethiopia, the largest producer of wheat in sub-Saharan Africa, providing a major food source and livelihood for millions of people.

While wheat blast has not been found in Ethiopia yet, the disease has spread from South America into southern Africa through changing weather patterns and trade and needs careful monitoring. The new tests provide Ethiopian researchers a fast tool to confirm wheat blast should it enter the country, allowing control measures to be deployed as early as possible to stop further disease spread.

A challenge for detection is that the symptoms of wheat blast can look similar to another disease, Fusarium head blight, which is commonly found in Ethiopia. The potential to confuse these two diseases in the field raises the likelihood that an introduction of wheat blast could be missed, as farmers and researchers generally identify wheat blast or Fusarium head blight by eye. The longer the disease is left unnoticed, the more chance the disease has to develop and spread, inhibiting the effectiveness of control measures. Therefore, it is vital to be able to quickly and accurately confirm the presence of wheat blast in the field.

A more accurate alternative is for researchers to use lab-based techniques, but previously these techniques have been slow, requiring several days and specialist lab equipment. These demands restrict the number of samples researchers can run and give the disease more time to develop and spread.

Because of these challenges, researchers in the Saunders lab at the John Innes Centre developed the new diagnostic tests that can confirm the presence of fusarium head blight or wheat blast within 90 minutes using easily accessible equipment. This same group also manages MARPLE Diagnostics, a mobile system to identify wheat rust strains.

Dr Dejene Girma, Head of Biotechnology at the Ethiopian Institute of Agricultural Research (EIAR), said: “It is wonderful to have this collaboration between UK and Ethiopian research, bringing new tools from discovery science into tools for impact on the ground.”

The first trial of the new diagnostic tests took place in June 2026 at the Ambo research centre, a location specialised in studying wheat diseases.

Dr Jemal Tola, Centre Director of the Ambo research centre for EIAR, said: “Ambo research station has a long history of collaboration with the John Innes Centre for disease diagnostics and surveillance. This new technique further supports our capacity to identify and research wheat diseases to protect Ethiopian farmers.”

Liam Butler, Research Assistant at the John Innes Centre, said: “Our visit to Ambo highlighted the importance of international collaboration in addressing emerging crop disease threats. It was exciting to see the method in the hands of Ethiopian research colleagues, where it can help strengthen diagnostic and surveillance capacity and support timely responses should wheat blast enter the country.”

This new diagnostic test for works through a Loop-Mediated Isothermal Amplification (LAMP) system – a rapid, nucleic acid amplification technique. The system works by using a few DNA primers and a displacing DNA polymerase to copy DNA. Unlike PCR, it occurs at a constant temperature, allowing the tests to run with more basic equipment, making the test more widely accessible for field teams.

Professor Saunders concluded: “We’ve designed this test to be usable by labs globally, irrespective of access to specialist equipment and resources. The next step is to further simplify these assays so they can be used directly in the field.”  

Robot uses Sense of Touch to Classify Bacteria Label-Free

2026-08-23T19:58:17.934-04:00

Image created by Dr. Michael J. Miller

A team from National Taiwan University has created a robotic sensing platform capable of identifying bacteria through touch.

The team explained that rapid identification of bacteria is critical in the healthcare, food safety, environmental monitoring and infection control fields, with the most common first steps being gram classification, wherein bacteria is separated into gram-positive and gram-negative groups. While this data can reportedly help guide early treatment decisions and safety responses, gram staining involves many chemical steps, trained personnel and manual interpretation.

As such, the team developed its robotic sensing platform, which features a flexible sensor mounted on a robotic gripper, noting that when the robot gently encounters a bacterial sample, the surface of the bacteria creates a small electrical signal. Thanks to their different cell wall structures, gram-positive and gram-negative bacteria generate different signal patterns.

In the lab, the team tested representative bacteria — Escherichia coli, Staphylococcus aureus, Staphylococcus epidermidis and Pseudomonas aeruginosa. When they combined signals from two sensing materials and analyzed the patterns with a computer model, the system achieved roughly 90.93% accuracy in terms of distinguishing gram-positive and gram-negative bacteria with a response time of 620 milliseconds.

This approach, according to its developers, does not require staining reagents or additional labels. Further, the robotic platform lessens the need for direct human handling of bacterial samples.

The team suggests that this new, nondestructive touch-based sensing strategy could one day contribute to expedited point-of-care diagnostics, automated microbiology workflows and safer bacterial monitoring in healthcare and environmental settings.

Additional development could potentially enhance the platform to include broader pathogen panels, such as antibiotic-resistant bacteria and other clinically important microorganisms.

"By turning a simple touch into an electrical fingerprint, our system offers a faster and safer way to identify bacteria without chemical labels," the team concluded.

An article detailing the system, “Triboelectric nanosensor-based robotic platform for rapid label-free discrimination of Gram-positive and Gram-negative bacteria,” appears in the journal Nano Energy.

Reference

Fu-Cheng Kao, Wei-Zan Hsu, Arshad Khan, Sheng-Chun Hung, Tupan Das, Ravindra Joshi, Parag Parashar, Ming-Kai Hsieh, Arnab Pal, Zong-Hong Lin, Triboelectric nanosensor-based robotic platform for rapid label-free discrimination of Gram-positive and Gram-negative bacteria, Nano Energy, Volume 152, 2026, 111879, ISSN 2211-2855, https://doi.org/10.1016/j.nanoen.2026.111879.

Abstract

Rapid and reliable identification of bacterial contaminants is essential for safeguarding public health, particularly in the face of rising antimicrobial resistance and emerging infectious disease outbreaks. Conventional Gram staining is time-consuming, operator-dependent, and relies on hazardous chemical reagents. Here, we present a triboelectric nanosensor (TENS)-based robotic platform capable of rapid, label-free, and non-destructive discrimination between Gram-positive (G+) and Gram-negative (G-) bacteria through contact electrification. The system integrates multiple triboelectric materials onto a robotic gripper to enable automated sensing while minimizing operator exposure risk. Fundamental differences in bacterial cell wall architecture generate distinct surface charging behaviors, which are captured as unique triboelectric signatures. Comprehensive characterization using X-ray photoelectron spectroscopy (XPS) and Fourier-transform infrared spectroscopy (FTIR) confirms the underlying chemical distinctions between G+ and G- bacteria, while micro-modified Kelvin probe force microscopy (KPFM) validates the material-dependent surface potential responses. Coupled with machine learning analysis, the platform achieves 90.93% classification accuracy, with a rapid response time of 620 ms. The robotic integration demonstrates strong potential for clinical application, offering reagent-free, automated, and operator-safe bacterial identification capability. This work establishes contact electrification as a new physical sensing modality for bacterial classification and opens new directions for point-of-care diagnostics and automated microbiological analysis.

Novel Colorimetric Nanosensor Enables Rapid Detection of Microbial Contamination on Medical Equipment

2026-08-23T19:45:13.754-04:00

Image created by Dr. Michael J. Miller

Researchers at the University of Social Welfare and Rehabilitation Sciences in Tehran have successfully designed an ultra-sensitive colorimetric nanobiosensor capable of providing rapid, visual detection of microbial growth on medical surfaces and equipment. The technology aims to streamline the monitoring of microbial contamination and significantly reduce hospital-acquired infections (HAIs).

The presence and proliferation of pathogenic microorganisms in healthcare facilities, food processing plants, restaurants, and other sensitive environments pose severe public health risks. Consequently, timely identification of microbial loads on surfaces and equipment—enabling prompt disinfection—remains one of the most critical strategies for preventing pathogen transmission.

Hospital-acquired infections represent a major challenge for healthcare systems globally. These infections, which are not present at the time of patient admission and develop after several days of hospitalization, lead to extended hospital stays, increased treatment costs, prolonged patient disability, and secondary complications.

A primary vector for HAIs is the contamination of medical surfaces and equipment. Current assessment methods rely heavily on microbial culturing—a process that is time-consuming, expensive, and requires specialized laboratory equipment and technical expertise for interpretation.

To address these limitations, the research team developed an ultra-sensitive colorimetric nanobiosensor to quantify microbial loads on surfaces. Constructed from an electrospun pad and a specialized indicator polymer, the sensor operates by undergoing a visible color change when exposed to varying levels of microbial contamination.

Under this protocol, the thin sensor pad is applied directly to a surface suspected of contamination. Initially a faint light blue, the pad gradually shifts along a color spectrum toward red in the presence of microbial activity. This transition is easily viewed through a transparent protective cover on the pad.

The color spectrum generated by the sensor was calibrated and standardized under laboratory conditions, providing users with printed reference charts. Without requiring complex equipment, personnel can simply observe the sensor's color shift after a few hours to a few days—depending on the level of contamination—and compare it against the standard chart to estimate the microbial load and determine whether immediate disinfection is required.

By making microbial monitoring simpler, faster, and more cost-effective, this technology offers a practical tool for infection control in hospitals, clinical centers, food industries, laboratories, and other high-risk environments.

Infection control remains a critical priority in healthcare settings, where dedicated "Infection Control Units" operate under hospital leadership and nursing management. These units coordinate health protocols, report to infection control committees, and enforce approved guidelines alongside infection control physicians and clinical microbiologists.

The project was led by Dr. Seyed Mohammad Ali Hosseini, Associate Professor at the University of Social Welfare and Rehabilitation Sciences in Tehran and Knowledge Translation Fellow in Cardiac Rehabilitation at the University of Technology Sydney (UTS), Australia. Mohammad Ekerami, a PhD candidate in Food Industry at the University of Tehran, served as a member of the R&D team, while Ali Ekerami, a Master's student in Community Health Nursing at the University of Social Welfare and Rehabilitation Sciences, contributed to the project's execution.

Predicting Microbial Antibiotic Resistance with AI

2026-08-23T19:40:28.376-04:00

Image created by Dr. Michael J. Miller

Have you ever wondered how researchers use artificial intelligence (AI) in environmental science? Certain AI computer models can learn from scientific data and operate without manual programming, in a process called machine learning. Machine learning models can identify patterns in environmental datasets and help scientists assess public health risks linked to environmental conditions.

One well-known public health risk is the rise in antibiotic-resistant bacteria. These bacteria carry genes called antibiotic resistance genes that protect them from antibiotic medicines. As the number of these genes increases, the medicines become less effective, and diseases spread faster.

Agricultural soils are the largest reservoirs of antibiotic resistance genes, because manure from livestock like cows and pigs often contains leftover antibiotics fed to them. When used as fertilizer, this manure transfers antibiotics into the agricultural soil, where bacteria develop antibiotic resistance genes.

To better understand how antibiotic-resistant bacteria will affect global health in the future, scientists want to develop faster ways to detect antibiotic resistance genes. However, current methods of detection are slow and fail to predict future trends. To address this problem, a group of scientists from Algeria used machine learning models to predict the prevalence of antibiotic resistance genes in soil microbes under future climate scenarios, and to identify the key environmental drivers and high-risk areas.

The team compiled existing microbial data from 3 public databases: the National Center for Biotechnology Information’s Sequence Read Archive, the Metagenomic Rapid Annotations using Subsystems Technology database, and the Joint Genome Institute’s Integrated Microbial Genomes & Microbiomes database. Their final dataset contained about 2,000 agricultural soil samples from 67 countries across 6 continents. These samples represented a wide range of soil characteristics, climate conditions, and different types of antibiotic resistance genes.

They integrated the microbial dataset with climate data, including global temperature and precipitation measurements from WorldClim, and land use data, including crop types, irrigation systems, and livestock density from the European Space Agency Climate Change Initiative Land Cover maps. The researchers also included data on future climate projections for 2050 and 2070 from the World Climate Research Programme based on low, medium, and high global greenhouse gas emission scenarios.

After preparing the datasets, the team set up 6 machine learning models that differ in terms of their data size, model complexity, analytical speed, and customization. These models included Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Deep Neural Network (DNN), and Logistic Regression (LR).

They ran each model using different combinations of variables from their datasets, including soil properties, microbial community metrics, climate variables, and land use characteristics. To validate the models, the team separated the full dataset into 10 random subsets. They trained the models on 9 of the subsets, then recorded the result of the 10th subset. Each model repeated this process 10 times in a different order so that each subset was evaluated once in the results, using a technique known as stratified 10-fold cross-validation. The researchers scored each model on 5 performance metrics, including precision, sensitivity, and predictive power. Based on these results, they determined that LightGBM was the most accurate model.

They found that the LightGBM model identified soil temperature as the strongest environmental predictor of antibiotic resistance genes. At soil temperatures above 18°C (about 64°F), the number of antibiotic resistance genes increased dramatically. The model also identified soil acidity, organic carbon content, moisture, and annual precipitation as influential variables, accounting for 71% of the increase in antibiotic resistance genes under future climate scenarios. Likewise, the LightGBM model predicted that the number of high-risk areas would increase by 35% under the high-emissions scenario, with South Asia, Sub-Saharan Africa, and Mediterranean Europe as the most vulnerable regions.

Based on the LightGBM model output, the team concluded that soil conditions will change to favor antibiotic resistance genes under all future climate scenarios. They also suggested that LightGBM is a fast and effective model for environmental predictions that could be used to assess other climate change risks such as natural disasters. They recommended that future researchers explore how to apply machine learning methods to early warning systems and utilize machine learning results in environmental management decisions.

Reference

Meriem Kenzi, Meriem Benbernou, Hadjer Khelifa, Hadja Fatima Tbahriti, Machine learning-based prediction of antibiotic resistance gene distribution in agricultural soils under different climate change scenarios, Science of The Total Environment, Volume 1042, 2026, 181905, ISSN 0048-9697, https://doi.org/10.1016/j.scitotenv.2026.181905.

Abstract

Antibiotic resistance genes (ARGs) in agricultural soils represent a major public health concern, as climate change is believed to augment their dissemination and abundance. Understanding the impact of future climate change scenarios on ARG abundance is essential to implement predictive and proactive One Health strategies. In this study, a total of 2301 soil samples from 67 countries across six continents were compiled from three global metagenome databases, namely NCBI SRA, MG-RAST, and JGI IMG/M. Six machine learning models, namely LightGBM, XGBoost, Random Forest, Support Vector Machines, Deep Neural Networks, and Logistic Regression, were used to predict ARG distribution patterns in agricultural soils, and their performance was evaluated using stratified 10-fold cross-validation with metrics such as AUC-ROC, precision, recall, F1 score, and Matthews Correlation Coefficient. WorldClim 2.1 and CMIP6 models were used to project ARG distribution under three Representative Concentration Pathway scenarios, namely RCP 2.6, RCP 4.5, and RCP 8.5, for the years 2050 and 2070. The LightGBM model achieved the best predictive performance, with an AUC-ROC of 0.957 (95% CI: 0.951–0.963), substantially higher than that of the other models, while the Deep Neural Networks model achieved an AUC-ROC of 0.891. The LightGBM model demonstrated high stability across cross-validation folds, with minimal fold-to-fold variance, defined as the standard deviation of AUC-ROC scores across the 10 folds (SD = 0.008). SHAP feature importance analysis identified soil temperature, pH, and organic carbon content as the top three factors influencing ARG relative abundance, with SHAP values of 0.342, 0.287, and 0.251, respectively. Annual precipitation and soil moisture level were also identified as significant contributors to ARG distribution. SHAP dependency plots revealed critical thresholds for ARG relative abundance, with a sharp increase observed independently when soil temperature exceeds 18 °C and when soil pH drops below 6.5. Furthermore, a non-linear accelerating increase in ARG abundance risk was observed as climate change intensity worsened across scenarios. Projections for future climate change scenarios indicate a potential 34.7% increase in high-risk ARG zones by the year 2070, with the largest changes expected in South Asia, Sub-Saharan Africa, and Mediterranean regions. Paired t-tests revealed significant differences in performance among all models (p < 0.001). These findings demonstrate that gradient-boosting methods such as LightGBM outperform deep learning approaches for ARG prediction from soil microbiome data, offering higher accuracy and interpretability. As climate change is projected to increase ARG risks in a non-linear manner, the development of climate-adaptive agricultural practices and global surveillance systems is urgent. This framework provides actionable risk-mapping tools to support precision farming and region-specific policy interventions within the One Health approach.

AirPods-sized Fluorescence Analytical Device Holds Promise for Timely Home Molecular Testing

2026-08-23T19:30:31.728-04:00

Image created by Dr. Michael J. Miller

Advances in medical technology have improved health in part by bringing key aspects of care, once difficult to access, into the home. Tracking symptoms and even screening for certain types of illness outside of a laboratory or clinical setting puts more control into the hands of patients.

New research from the University of Illinois is helping to provide a practical answer to the question, what does a holistic system need to look like in order to work outside the laboratory?

Research led by Han Keun Lee in the laboratory of electrical and computer engineering professor Brian Cunningham, in collaboration with bioengineering professor Xing Wang, was published in the IEEE Sensors Journal.

Lee, Cunningham, Wang, and their co-authors shared the design and validation of a device that offers the capability to read and compare results from a variety of sensitive tests for pathogens or cancer-associated biomarkers, all within a housing similar in size and shape to an AirPods case. The study was supported by the National Institutes of Health and the U.S. Department of Veterans Affairs.

Limits of today's home tests

"I have an ambition that we can bring cancer detection to the home. Currently, detecting cancer often requires going to a hospital and having blood drawn," requirements that can become barriers to accessing timely care, Lee said. "My job here is to be able to bring those tests out to the world so people can start utilizing them and have better access to the state-of-the-art technologies."

Many home tests work similarly to pregnancy tests or infectious diseases tests such as COVID-19 antigen tests; labeled molecules generate a visible line when a substance of interest is present in the sample, producing a simple positive or negative result. This type of test is relatively affordable and easy to use, but it can have limited sensitivity and often provides only qualitative or semi-quantitative results.

A new version of the test strip must also be developed for each new pathogen or biological molecule of interest.

Another type of test, commonly used in the laboratory, can be much more accurate and quantitative because it relies on the measurement of a fluorescent dye whose signal can be amplified. But many existing instruments used to read fluorescent signals are not practical for home or point-of-care settings; they are large, expensive, and often require training to use correctly.

A smaller reader for paired samples

"There are many different ways of quantifying fluorescent assays. One example is using a camera to capture the whole reaction area. . . but this requires sophisticated instrumentation," Lee said.

"That was deviating from our point of view where we wanted something that's very portable, small and inexpensive. We decided to stick with a photodetector," a simple component that detects the intensity of light without capturing spatial information.

Using a simple light detector has its own drawbacks; a single detector does not allow a clear comparison between the sample that comes from the individual being tested and a control sample that provides a baseline negative result.

Lee and his colleagues previously designed a compact, user-friendly device they called the VPod that was able to detect fluorescence signals, but could measure only one sample at a time. Their present publication showcases their improved device, the VPodDuo, which supports a paired test-and-control workflow: this means that it is able to measure the two samples simultaneously.

VPodDuo, like the VPod before it, is also suited to point-of-care settings because of its compatibility with different molecular-assay formats. The device measures green-emitting fluorescence signals generated by several types of detection chemistry, allowing the same reader to be used with tests for different targets.

The research team demonstrated that VPodDuo could accurately detect and measure quantities of genetic material from Zika virus, HIV, and methicillin-susceptible Staphylococcus aureus bacteria, as well as human genetic markers indicating the possible presence of cancer cells.

Designing the full testing workflow

"There is a famous saying that medicine is blind without diagnostics," Lee said. "The purpose of testing at the point-of-care is not necessarily to give a definitive diagnosis, but rather to allow more frequent testing so that someone has a better chance of receiving timely treatment."

The VPodDuo's versatility and accuracy make it a valuable piece of scientific equipment. To be practical for point-of-care applications, it also needed to be portable and easy to use.

Lee and his co-authors designed the device to connect wirelessly to a mobile device, designed a software application with an intuitive user interface to aid with operation and result interpretation, and built in safeguards to protect against accidental misuse. All of these features are directed toward their overall goal of creating a point-of-care testing workflow that can be used outside of centralized laboratories.

"It's not just about the individual test for point-of-care use, it's about the entire system," Lee said. "We wanted to address this from a system-level engineering perspective for fluorescent molecular testing. That's where I wanted to go, so that I can help bring all these great technologies out into the world to provide real benefit."

Reference

Han Keun Lee et al, A dual-port, smartphone-linked, pocket-size fluorimeter for rapid molecular diagnostic assays at point-of-care, IEEE Sensors Journal (2026). DOI: 10.1109/jsen.2026.3693175

Abstract

We present the design, testing, and demonstration of a portable, pocket-size, smartphone-linked fluorimeter called the “VPodDuo.” The instrument is capable of reading the output of several fluorescence-generating biomolecular detection assays with sensitivity that is similar to larger and more expensive laboratory-based instruments. In this work, we focus on demonstrating the capability for readout of assays used to detect target nucleic acid sequences associated with infectious pathogens and cancer with incubation times of approximately 10 min. The VPodDuo features a dual-port configuration that allows simultaneous measurements of a negative experimental control (CTRL) in parallel with the test sample. We demonstrated compatibility with several assay protocols, including reverse transcription loop-mediated isothermal amplification (RT-LAMP), recombinase polymerase amplification (RPA), clustered regularly interspaced short palindromic repeats (CRISPR)/Cas, and the target recycling amplification process (TRAP). Benchmarking against three commercially available fluorimeters showed comparable detection limits for clinically relevant nucleic acid sequences, including Zika virus (ZIKV, $10^{{4}}~\text {copies}/\mu \text {L}$ ), methicillin-susceptible Staphylococcus aureus (MSSA, $10^{{2}}~\text {copies}/\mu \text {L}$ ), human immunodeficiency virus (HIV, 6.83 pM), a lung cancer-associated circulating tumor DNA sequence [L858R point-mutated epidermal growth factor receptor (EGFR) gene, 29.2 pM], and a microRNA biomarker associated with lung cancer (miR-375-3p, 500 pM). We further validated the VPodDuo’s performance under varying ambient temperatures through in-lab simulations and real-world outdoor testing using the TRAP assay for miR-375-3p detection, demonstrating cancer-associated biomarker detection at point-of-care (POC) settings. With its compact form-factor, low cost, portability, and real-time data transmission and analytical capabilities, the VPodDuo represents a promising solution for expanding access to rapid, on-site molecular diagnostics in diverse clinical and field settings.

New Chip-based Sensor Could Let Consumers Test Food for Contaminants at Home

2026-08-23T19:12:44.415-04:00

Image created by Dr. Michael J. Miller

Food safety concerns are becoming too-frequent headlines. Right now, it's a cyclosporiasis outbreak possibly linked to tainted lettuce. But the nation has also dealt with E. coli, Salmonella, and Listeria – a long list of threats.

Consumers already know to thoroughly wash fresh fruits and vegetables. But is it enough? And how would you know? Many have wondered what's next.

Researchers at the University of Texas at Dallas are exploring a quick, cost-effective way to test for food contaminants at home using technology most people already carry in their pockets: computer chips.

"We have re-engineered those devices to be able to have what is called a 'single capture measurement,'" said Shalini Prasad, PhD, department head of bioengineering and a Cecil H. and Ida Green Professor in Systems Biology Science in the Erik Jonsson School of Engineering and Computer Science at UTD.

"Say, it's a lettuce sample, or it's a strawberry," Prasad said. "And you take it, mush it with your hand, and drop it on the sensor.

"The sensor is engineered so that it can measure any of these 16 things. So, a panel of antibiotics, a panel of pesticides, a panel of bacteria, a panel of parasites, and a panel of fungi. It can do all of this."

The device, called READ – Rapid Electro Analytical Device – can detect contaminants in food and water in just a couple of minutes. Prasad said the research grew out of her curiosity as a mother trying to better protect her two sons.

"As a mom, you're a problem solver, right? And you don't know what's coming at you at any given point of the day. But what is important is how do I make people's lives easier, better, healthier, and more accessible," she said.

The technology is nimble, allowing researchers to tweak and respond to outbreaks as they happen.

"That is the idea," Prasad said, "because we don't know what's the next epidemic, and we cannot wait to have it all built."

The research team is preparing for pilot testing, with hopes of having the technology in consumer kitchens within the next year or so

"What worries us most is that which we cannot see," Prasad said. "It is the invisible that I am making visible through this technology. That is the hope. And that is the dream."

Osaka University, DNAFORM and INRB Develop Portable Rapid Ebola Diagnostic System

2026-08-23T19:08:27.014-04:00

Image created by Dr. Michael J. Miller

In May 2026, the World Health Organization declared an international public health emergency over an outbreak of Ebola virus disease (EVD) caused by Bundibugyo virus (BDBV) in the Democratic Republic of the Congo and Uganda. For clinicians and public-health teams working under acute constraints, fast confirmation of suspected cases—and equally fast isolation and contact tracing—can determine whether transmission is contained.

Osaka Metropolitan University (OMU) is therefore developing a portable, rapid diagnostic platform aimed specifically at BDBV. The project brings together OMU, K.K. DNAFORM, and the National Institute for Biomedical Research (INRB) in the Democratic Republic of the Congo, and was selected for funding by Japan’s Global Health Innovative Technology Fund (GHIT Fund). The work is designed to function near outbreak sites where laboratory infrastructure and reliable electricity may be limited.

A key development feature is the project’s alignment with the “100-Day Mission,” an international goal to enable diagnostic tools, therapeutics, and vaccines to be available within 100 days of identifying a new infectious threat. Following the PHEIC declaration, the research team moved rapidly, using DNAFORM’s existing portable nucleic-acid platform technology and established field collaboration capacity at INRB.

The prototype system, called the GenPad Smart BDBV portable rapid diagnostic system, is based on a GenPad point-of-care testing platform that uses SmartAmp isothermal PCR chemistry with Eprobe detection. Rather than requiring full thermal cycling or extensive lab equipment, the system is intended for battery-powered operation and supports cartridge-based sample processing designed to simplify handling and reduce exposure risk after appropriate training.

Initial laboratory evaluation reports a limit of detection (LOD) of ≤200 copies per cartridge, consistent with WHO emergency-use-related performance targets. The prototype also showed no cross-reactivity against a panel of 86 microbial species, supporting the specificity needed to distinguish BDBV in settings where other pathogens may be present.

Operationally, the device weighs about 460 g and runs on a 7.2 VDC battery (21.6 Wh), enabling up to eight tests on a single charge. Results are projected in roughly 30 minutes from blood collection, with multiple devices clusterable for parallel testing during surges.

Development is continuing in phases, beginning with performance validation using non-replicating “armored RNA” virus mimics, followed by clinical validation with real samples in the Democratic Republic of the Congo in collaboration with INRB. A multiplex direction is also underway to differentiate BDBV, Ebola virus (EBOV), and Sudan virus (SUDV), though BDBV-specific performance remains the immediate priority.

The platform development strategy is positioned as a practical template for rapid outbreak R&D that leverages a network across industry, academia, and international partners. By coordinating with WHO-linked emergency pathways, the program aims to generate the evidence needed for potential emergency use listing while maintaining a focus on immediate field readiness.

Mizzou Innovation Speeds up Detection of Deadly Tick-Borne Disease

2026-07-22T11:32:22.493-04:00

Image created by Dr. Michael J. Miller

A breakthrough at the University of Missouri transforms how Rocky Mountain spotted fever is diagnosed. The new method detects the deadly tick-borne disease in just 40 minutes — a leap that could help patients get lifesaving treatment sooner.

Early diagnosis of Rocky Mountain spotted fever is critical. Patients can go from a tick bite to a severe fever, headache and rash within days. Yet existing diagnostic tools can be expensive, hard to access or unable to detect small amounts of the bacteria in a blood sample.

Mizzou’s new method changes that, delivering clearer answers when they matter most.

The test, which involves mixing DNA from a sample with specialized reagents in two quick, 20-minute steps, can be completed at room temperature without the need for complex and costly laboratory equipment. After 40 minutes, a change in liquid color indicates a positive detection of the bacteria.

The antibiotic doxycycline is used to treat the disease, and clinicians often prescribe it while waiting on confirmatory testing. Right now, that can take too long.

“Unfortunately, this disease can be particularly deadly in children if diagnosis is delayed, and often patients may not realize they have been bitten by a tick until they feel extremely ill,” Roman Ganta, a Curators’ Distinguished Professor in the College of Veterinary Medicine and investigator in the Bond Life Sciences Center, said. “Quicker diagnosis leads to quicker treatment with doxycycline, so we are hopeful our research can improve outcomes for those impacted.”

Ganta hopes this test will eventually be used in rural or under-resourced clinics around the world where the advanced laboratory equipment currently needed to detect Rocky Mountain spotted fever is inaccessible. He is in discussions with the Veterinary Medical Diagnostic Laboratory at Mizzou to see whether this test may be utilized in clinical applications in the future.

“By making this test more accessible, more patients can benefit from our work one day,” Ganta said.

The study, “A rapid isothermal RPA–CRISPR/Cas12a assay for detection of Rickettsia rickettsii,” was published in Frontiers in Microbiology.

Reference

Ozubek S, Liu H and Ganta RR (2026) A rapid isothermal RPA–CRISPR/Cas12a assay for detection of Rickettsia rickettsii. Front. Microbiol. 17:1823193. doi: 10.3389/fmicb.2026.1823193

Abstract

Introduction: Rocky Mountain spotted fever (RMSF) resulting from the tick-borne Rickettsia rickettsii infections is a potentially fatal tick-borne disease affecting humans and dogs in the Americas. It is difficult to confirm the diagnosis early in the laboratory on account of low-level and inconsistent rickettsemia in whole blood.

Methods: Here we established a fast, fully isothermal RPA–CRISPR/Cas12a assay that targeted the vitamin uptake transporter (vut) gene of R. rickettsii. Two independent crRNA primer sets (104 bp and 92 bp) were developed to independently amplify the gene target to enhance the reliability and specificity of the assay.

Results: Using quantified synthetic DNA (gBlock) standards derived from the R. rickettsii Sheila Smith vut target region, the assay was able to detect target DNA in about 40 min at 37 °C with an observed analytical detection limit of 60–70 copies per reaction. Specificity testing on four R. rickettsii strains and a panel of non-target spotted fever group rickettsiae and tick-borne bacteria of clinical importance demonstrated no cross-reactivity of the assay with these pathogen nucleic acids. Applied to archived longitudinal canine whole-blood DNA extracts from experimentally infected dogs (n = 6), the performance was intermittent for known limitations of whole blood testing in RMSF and limited to specific post-infection days.

Discussion: The findings of this study support the feasibility of RPA–Cas12a as a rapid molecular workflow for R. rickettsii detection, while indicating that broader validation in additional clinically relevant and removed tick sample types is still needed.

Phages Light Up Klebsiella in Rapid Diagnostic Platform

2026-07-16T10:56:33.032-04:00

Image created by Dr. Michael J. Miller

The capsule of Klebsiella pneumoniae is more than a protective coat. It is a major virulence factor, a useful epidemiological marker, and an important clue for selecting future vaccines, antimicrobial strategies, and phage therapies. However, capsule typing can be difficult to perform rapidly. Serological methods depend on costly antisera and can suffer from cross-reactivity, while PCR or sequencing methods cannot distinguish between live and dead bacteria.

A new study describes a modular reporter phage platform that turns capsule recognition into a measurable light signal. The researchers first engineered the K2-targeting bacteriophage RCIP0109 by inserting the NanoLuc luciferase reporter gene, creating ΦRCIP0109::nluc. When the reporter phage infects its matching K. pneumoniae hosts, it produces bioluminescence that can be measured in a standard microplate reader.

The key innovation is modularity. Because receptor-binding proteins (RBPs) help determine which capsule a Klebsiella phage recognizes, the team used RBP swapping to redirect the host range of the reporter phage chassis. This strategy generated reporter phages targeting K1, K47, K57, and K64 strains, adding to the K2-specific chassis and covering several clinically important capsular types associated with hypervirulence or carbapenem resistance.

“By swapping receptor-binding proteins, we can turn the natural specificity of phages into an adaptable diagnostic signal,” said Jie Feng, corresponding author of the study. “This work suggests a path toward rapid, scalable capsule typing that could be expanded as more phage-host recognition modules become available.”

The platform showed strong performance in multiple testing settings. For K2 detection, high bacterial concentrations were detected within 0.5 hours, and the limit of detection reached 10 CFU/mL after 2.5 hours. The RBP-swapped reporter phages selectively identified their corresponding capsular types with no detectable cross-reactivity. In synthetic urine, reporter phages detected K1, K2, K47, K57, and K64 K. pneumoniae strains at clinically relevant bacterial loads, with signals appearing within 0.5 hours at ≥105 CFU/mL while consistent luminescence kinetics were also observed at 103-104 CFU/mL. In polymicrobial urine-like samples containing other common uropathogens, the reporter phage panel still identified target strains within about 3 hours.

The researchers also demonstrated that directed evolution can tune reporter phage performance. An evolved K2 reporter phage produced 10- to 100-fold higher luminescence than its ancestral strain and shortened detection times at several bacterial concentrations. A separately evolved K57 reporter phage showed improved adsorption, higher titer, and a broader linear detection range, supporting the idea that phage evolution can be used to improve assay sensitivity.

Together, the findings present a scalable route for rapid K. pneumoniae capsular typing in near-clinical matrices. Because the assay can be read on standard microplate instruments and may also support visual inspection, it offers practical flexibility for future point-of-care development. Future integration with machine-learning prediction of phage-host interactions, AI-assisted protein design, and targeted mutagenesis could extend the approach to additional capsular types and other priority pathogens. This work was conducted by researchers at Shandong First Medical University and Shandong Academy of Medical Sciences; the Institute of Microbiology, Chinese Academy of Sciences; the University of Chinese Academy of Sciences; Yunnan University; and Peking University Third Hospital. Support was provided by the National Key Research and Development Program of China under Grant/Award 2024YFA0919400.

Reference

Yiyao Song, Shisong Jing, Yi Li, Yinglu Guo, Jiangqing Huang, Xianbiao Bi, Dawei Wei, Chao Wang, Gang Zhang, Jiajia Zheng, Zhongrui Ma, Jie Feng, A phage-based luminescent reporter platform for rapid typing of multiple capsular types of Klebsiella pneumoniae, hLife, 2026, ISSN 2949-9283, https://doi.org/10.1016/j.hlife.2026.06.001.

Abstract

Klebsiella pneumoniae is an important opportunistic pathogen, and its capsular polysaccharides are key virulence factors. Capsular typing is important for epidemiological surveillance, clinical diagnosis, and therapy development; however, methods for capsule-targeted detection remain limited. Here, we engineered a modular bioluminescent reporter phage platform leveraging bacteriophage receptor-binding proteins for specific K. pneumoniae capsular typing. Initially, the K2-specific bacteriophage ΦRCIP0109 was engineered with the nluc reporter gene to generate ΦRCIP0109::nluc for K2 detection. Using ΦRCIP0109::nluc as the chassis, we engineered reporter phages by swapping receptor-binding proteins (RBPs), thereby expanding the detection range to four additional clinically important K types (K64, K47, K1, and K57). Reporter phage specificity and sensitivity were evaluated against clinical isolates and in simulated polymicrobial environments. Directed evolution was employed to introduce RBP mutations for enhanced phage adsorption. These five reporter phages achieved 100% specificity against clinical isolates, detecting concentrations as low as 10 CFU/mL within 3.5–5.5 h and successfully differentiating host strains in polymicrobial synthetic urine. Directed evolution of an RBP yielded a 10- to 100-fold increase in luminescence compared to that of the wild-type phage. Together, these advances establish a scalable platform that can be expanded to additional capsular types and the detection of other pathogens, representing a platform with clear potential for integration into point-of-care diagnostics, guiding targeted antimicrobial therapy and precision phage therapy.

Forget Gram-Staining; This Robot Has It Covered

2026-07-16T10:49:01.003-04:00

Image created by Dr. Michael J. Miller

Researchers have developed a robotic platform that identifies Gram-positive and Gram-negative bacteria through touch-based electrical signals, without staining or chemical labels. The study achieved rapid classification within 0.62 seconds and 90.93% accuracy.

Fast identification of bacteria is important in healthcare, food safety, environmental monitoring, and infection control. One of the most common first steps is Gram classification, which separates bacteria into Gram-positive and Gram-negative groups. This information can help guide early treatment decisions and safety responses. However, conventional Gram staining requires several chemical steps, trained personnel, and manual interpretation.

This study was recently selected as a cover article in Nano Energy, highlighting its potential impact in rapid bacterial sensing and automated biomedical analysis.

A research team led by Prof. Zong-Hong Lin at National Taiwan University has developed a robotic sensing platform that identifies bacteria through touch. The system uses a flexible sensor mounted on a robotic gripper. When the robot gently contacts a bacterial sample, the surface of the bacteria produces a small electrical signal. Because Gram-positive and Gram-negative bacteria have different cell wall structures, they generate different signal patterns.

The team tested representative bacteria including Escherichia coli, Staphylococcus aureus, Staphylococcus epidermidis, and Pseudomonas aeruginosa. By combining signals from two sensing materials and analyzing the patterns with a computer model, the system achieved 90.93% accuracy in distinguishing Gram-positive and Gram-negative bacteria. The response time was only 620 milliseconds.

This approach offers several practical advantages. It does not require staining reagents or additional labels, and the robotic platform reduces the need for direct human handling of bacterial samples. The method is also non-destructive, meaning it may be useful for future systems that need repeated or automated monitoring.

The researchers envision that this touch-based sensing strategy could contribute to faster point-of-care diagnostics, automated microbiology workflows, and safer bacterial monitoring in healthcare and environmental settings. Further development could expand the platform to broader pathogen panels, including antibiotic-resistant bacteria and other clinically important microorganisms.

“By turning a simple touch into an electrical fingerprint, our system offers a faster and safer way to identify bacteria without chemical labels,” says co-corresponding author Zong-Hong Lin, professor and vice chair in the Department of Biomedical Engineering at National Taiwan University.

Source: National Taiwan University

Reference

Abstract

Rapid Food Poisoning Detection Created at U of Nevada School of Medicine

2026-06-30T08:29:39.800-04:00

Image created by Dr. Michael J. Miller

A simple paper strip can now detect a toxin found in contaminated food in 15 minutes.

Vanessa Berner, Ph.D., a research faculty scientist in the Department of Microbiology and Immunology in the laboratory of David AuCoin, Ph.D. worked with third-year medical student Peter Asimenios to create this test. The AuCoin Laboratory at the University of Nevada, Reno School of Medicine (UNR Med) focuses on developing cost-effective tests used for at-home diagnosis of dangerous infectious agents and biological toxins.

The strip detects staphylococcal enterotoxin B, which commonly causes food poisoning that results in vomiting and diarrhea. Faster detection can help reduce the spread of the toxin while also providing a quicker diagnosis and treatment.

“For this particular project, Asimenios was a great fit as he had quite a bit of prior research experience and a really keen interest in the toxin biology and clinical impact,” said Berner. “He not only helped design the experiments to construct and optimize the tests, but he also performed toxin testing under rigorous biosafety conditions. Not all students are able to work with biological toxins and other select agents; only those with extensive experience and specialized training can perform this type of research.”

This test can identify contaminated foods at the source before they are distributed and is more sensitive than previous tests. Current detection methods are also time-consuming and laborious.

“The most surprising finding has been how well the test works across a wide range of foods,” said Asimenios. “Many other tests require lots of sample preparation, while ours is relatively ‘plug-and-play.’ This makes it more practical for real-world use.”

Contaminated food products can also be used in bioterrorism. Staphylococcal enterotoxin B has been classified by the U.S. Centers for Disease Control and Prevention as a select agent, meaning it has high potential to be used as a biological weapon. With this test, public health authorities could screen for the toxin more quickly to reduce outbreaks.

“I do think it’s important for college students to participate in research,” said Asimenios. “It builds critical thinking, adaptability and teamwork while promoting engagement with the latest scientific knowledge. It allows students to contribute meaningfully and advance their field by discovering new information.”

A 2019 survey by the Association of American Medical Colleges found that nearly 60 percent of medical students had participated in laboratory research. The organization also identified research as an effective way to hone critical analysis skills while preparing students for medical school.

“Being part of a student’s research journey is very fulfilling because I get to watch them make significant growth in their abilities and understanding,” said Berner. “When a student contributes something important to a project for the first time, you can see a fire ignite in them for science. It’s a privilege to be a part of that moment and to help get them there.”

A medical student’s application includes the Medical College Admission Test (MCAT) scores as well as experiential factors – students who participate in research can enhance their competitiveness.

“It provided valuable experience in interdisciplinary collaboration and how to adapt on the fly when experiments don’t go as planned,” said Asimenios. “It also exposed me to many important laboratory techniques, many of which are used extensively in other projects.”

MHRA and NHS Enhance Bacterial Infection Diagnosis with DNA Sequencing

2026-06-30T08:13:00.175-04:00

Image created by Dr. Michael J. Miller

The Innovation Platform spoke with Saba Anwar, Senior Scientist at the Medicines and Healthcare products Regulatory Agency (MHRA), about a pilot study with Barts Health National Health Service (NHS) Trust that reduced bacterial infection diagnosis time using DNA sequencing technology.

A recent collaboration between the MHRA and the NHS represents a significant advancement in the UK’s management of infectious diseases. Utilising innovative DNA sequencing technology, they have reduced the diagnosis time for bacterial infections from weeks to just 48 hours. This rapid diagnostic capability enhances clinical assessments and enables quicker initiation of optimal antimicrobial therapy. Through a pilot study, the MHRA supported Barts Health NHS Trust by providing a highly characterised reference material established by the WHO as International Reference Reagents. Using them, Barts Health NHS Trust demonstrated the technology’s potential and suitability for widespread adoption across the NHS underpinned by these reference reagents. This will help address the urgent issue of antimicrobial resistance, which contributes to about one million deaths globally each year.

The Innovation Platform spoke with Saba Anwar, Senior Scientist, Research & Development at the MHRA, about the technology, pilot study findings, and the pathway toward standardisation of DNA sequencing diagnostic technologies within the NHS, showcasing their potential impact on the future of clinical diagnostics.

Can you provide a brief overview of the DNA sequencing diagnostic technology developed by the collaboration and the study conducted with Barts Health NHS Trust? What impacts did the pilot study find?

A major challenge for clinical management of patients already receiving antibiotic treatment is ongoing monitoring of bacteria present in ‘culture-negative’ samples. Barts NHS Health Trust wished to introduce a diagnostic service to analyse these culture-negative samples. The goal was to apply Oxford Nanopore technology to characterise 16S RNA gene sequences and so detect and identify the bacterial pathogen unequivocally. Moreover, the method had the potential to be run rapidly and directly in a hospital lab, allowing for near-patient, real-time diagnostics.

A pilot study was undertaken at Barts Health NHS Trust and involved close collaboration with scientists at the National Measurement Laboratory at the Laboratory of the Government Chemist (LGC-NML) and my team at the Science Campus of the Medicines and Healthcare products Regulatory Agency. We developed WHO Gut Microbiome DNA and Whole-cell reference reagents, which were used to validate every step of the sequencing workflow, enabling faster and more accurate diagnoses for severe infections, enabling clinicians to tailor antibiotic treatment earlier and replace broad-spectrum antibiotics with more effective treatments. It also identified pathogens in culture-negative cases that would otherwise have remained undetected: vital clinical information that guided more effective clinical care.

The study demonstrated that nanopore sequencing developed by Barts with support from MHRA and LGC-NML can be successfully established and embedded within NHS hospital laboratories, allowing for broad adoption across the NHS.

In your experience, through this study and previous case studies, what are the key advantages of DNA technology compared to traditional diagnostic methods?

Direct DNA sequencing offers several advantages over traditional/existing diagnostic tests, in terms of speed, precision, and its ability to detect pathogens that conventional culture-based approaches often miss. Sequencing can return results within 48 hours compared with the seven days typically required to identify a bacterial infection by culture, or the many weeks needed for slow-growing organisms. The rapid turnaround of results is crucial for life-threatening conditions such as sepsis or meningitis, when early, accurate treatment can be lifesaving.

Sequencing identifies pathogens with greater precision, allowing clinicians to optimise treatment plans more quickly, reducing the use of broad-spectrum antibiotics, leading to better antimicrobial stewardship. Importantly, diagnostic methods based on direct sequencing can still provide a diagnosis when clinical samples cannot be cultured because of prior antibiotic treatment, low bacterial numbers, or slow-growing organisms.

The laboratories at the MHRA Science Campus do not undertake clinical diagnostics. One of our roles is to develop robust physical standards that assure complex diagnostics, such as sequencing, and promote their adoption and use to support complex clinical diagnostics, such as DNA sequencing, by clinical laboratories such as the NHS.

Could you discuss the steps the MHRA is taking towards standardising DNA sequencing technologies within the NHS? Since the release of the study, what progress has been made towards this?

The current work at the MHRA Science Campus builds on a 100-year history of developing physical standards and reference materials that harmonise measurement of complex assays. With regards to clinical diagnostics, we work closely with multiple stakeholders, including the NHS, to ensure that tests that rely on new technologies, such as the direct sequencing of amplified nucleic acid, provide results that are reproducible, comparable, and accurate. We achieve this by applying the principles of biological standardisation, first developed by Sir Henry Dale 100 years ago. This involves the preparation of extremely well-characterised materials that need to be co-processed along with clinical samples. By comparison with these reference materials, the performance of each run of the assay can be established, and the quality and quantity of the resulting data can be assessed. These materials contribute to the standardisation of technologies such as the new methods of sequencing applied at Barts NHS Trust, without being prescriptive about the method. By incorporating data generated across multiple laboratories, we can determine which approach is most suitable to ensure reproducibility, comparability, and ultimately reliable clinical outcomes based on decisions informed by the data. The use of these standards ultimately supports better clinical outcomes for patients.

What challenges do you anticipate in this process, and what timeline do you foresee for its implementation across more hospitals?

One of the main challenges is the current variability seen in Next Generation Sequencing (NGS) results between different laboratories. Without a consistent framework, differences in sample preparation, sequencing platforms, bioinformatic pipelines, and interpretation criteria can all lead to inconsistent outputs. To address this, appropriate standards are essential. Applying robust, widely adopted standards will allow results to be compared reliably across sites, which is crucial if sequencing-based diagnostics are to be implemented safely and confidently within the NHS. Establishing and applying the right standards is the key step that will enable wider implementation across the NHS.

The technology is also promising for tackling AMR. Can you summarise how DNA sequencing technologies can help combat antimicrobial resistance and prevent hospital outbreaks? Following the November 2024 pilot study on this topic, what progress has been made towards implementing and regulating this technology for AMR applications?

Direct DNA sequencing is rapidly becoming a critical tool in the fight against antimicrobial resistance. It offers a level of speed and acuity in data that existing culture-based methods cannot match. The analysis of bacterial DNA amplified directly from a patient sample enables the precise identification of the organism causing an infection, how this organism is genetically related to those recovered from other patients exhibiting the same symptoms, and can establish whether the organism carries genes that increase resistance against potential drugs (AMR genes). All these pieces of information allow clinicians to select the most appropriate treatment more quickly and avoid the broad-spectrum antibiotics that drive resistance. This not only protects patients from avoidable side effects but also contributes to better long-term clinical outcomes by slowing the development of resistance.

Defining AMR genes present in bacteria detected provides clinical value even when bacteria are present in low numbers or are difficult to culture, since sequencing can identify resistance markers that guide more precise therapy. This provides clinicians with actionable information and ensures patients receive the most effective care.

Sequencing also provides hospitals with a powerful surveillance tool. By comparing AMR genes from different patients, teams can identify transmission pathways and intervene early to control hospital outbreaks.

To support and strengthen efforts against AMR, the MHRA science campus is developing candidate reference reagents for AMR gene detection. These reagents are intended to establish the accuracy of AMR gene detection tools, define the resistome within samples, and harmonise DNA sequencing methods and analytical pipelines.

Strawberry Producers Nationwide Rely on UF Plant Pathologists for Fast, Accurate, Disease Diagnoses; Fusarium Wilt Is Latest

2026-06-30T05:58:45.526-04:00

Image created by Dr. Michael J. Miller

Strawberry growers across the country aren’t just tending fields – they’re often running a quiet race against fast-moving plant diseases. When trouble strikes, their first call is often to the plant pathologists at the Gulf Coast Research and Education Center (GCREC), the mighty hub where suspicious leaf spots and wilting plants are treated like high-stakes mysteries.

The latest culprit has entered the scene in the form of Fusarium wilt disease, which is popping up along the East Coast.

Fusarium wilt can survive in soil for years and spread through infected plant material and equipment, said Natalia Peres, professor of plant pathology at GCREC. Current strawberry varieties show limited resistance to the disease, and there are few effective fungicides, making the disease especially difficult to control.

Peres and Marcus Marin recently discovered the disease in Florida, North Carolina, New York, Connecticut and Virginia. They confirmed their findings through testing at the Plant Disease Diagnostic Clinic at GCREC, part of the University of Florida Institute of Food and Agricultural Sciences (UF/IFAS).

Strawberry farmers from 22 states send their samples to the lab in Balm (in Hillsborough County), Marin said. They know they can count on the expertise and technology at GCREC.

“Because our program is largely focused on strawberries, we have been able to develop and implement molecular methods that allow for rapid diagnosis,” said Marin, an assistant professor of plant pathology at GCREC. “That way, we can provide preliminary results to growers within 24 to 48 hours after receiving a sample, while it would normally take seven to 10 days, using traditional methods.”

Marin boils the diagnostic process down to simple language.

“When sick plants arrive at our clinic, we first look for the parts that seem sick,” he said. “Then we test those plant pieces in the lab to find out what is making the plant sick.”

The current clinic was established in Balm in 2005, but plant pathologists at the former site in Dover diagnosed crop diseases going back through the 100-year history of GCREC.

Peres likens the clinic to “an X-ray of what is going on in growers’ fields.”

“I would say it pretty much helps us guide the priorities of our research program,” Peres said. “Every season is different, and we never know what diseases to expect, so we have worked on them all, from Botrytis to anthracnose, crown rots, Neopestalotiopsis and now the threat of Fusarium wilt.”

Marin, a former doctoral student and post-doctoral researcher under Peres’ supervision, said he has worked on developing diagnostic tools for Neopestalotiopsis.

“Early detection and accurate diagnosis are critical for managing this devastating pathogen.” Marin said. “I have also been expanding our rapid diagnostic services and implementing new assays for Fusarium wilt and Verticillium wilt of strawberry. Although neither disease had previously been reported in commercial strawberry fields in Florida, our proactive efforts and the establishment of these diagnostic systems enabled the detection of Fusarium wilt during the 2025–26 season in multiple farms along the eastern seaboard.”

The clinic at GCREC is among the five diagnostic labs of UF/IFAS Extension serving different regions of the state. The main clinic is in Gainesville, but plant pathologists statewide help growers of many crops find out what’s ailing their produce. In addition to Gainesville and GCREC, other clinics are at the North Florida Research and Education Center (NFREC), Southwest Florida Research and Education Center (SWFREC) and the Tropical Research and Education Center (TREC).

Indeed, last fall, UF/IFAS made plant pathology history. Its Plant Diagnostic Center — along with the NFREC Plant Disease Diagnostic Clinic — became the first university-based lab in the nation to earn accreditation from the prestigious National Plant Diagnostic Network (NPDN) — a recognition likened to receiving a “gold-medal seal of approval” for plant health.

“UF/IFAS plant diagnostic labs are considered among of the best, globally, due to the high level of expertise in diagnosing hundreds of plant pathogenic fungi, bacteria and viruses,” said Mathews Paret, chair of the plant pathology department. “When coupled with expertise in predictive tools (e.g. the Strawberry Advisory System developed at GCREC) and rigorous field-testing of new approaches, this provides the much-needed knowledge supporting Florida and U.S. agriculture.”

Osaka Researchers Use 10nm Gold-Coated Fiber to Capture 100,000 Bacteria in One Minute

2026-06-30T04:45:18.384-04:00

Image created by Dr. Michael J. Miller

Researchers at Osaka Metropolitan University have developed a light-driven detection technique that concentrates thousands of bacteria into a single point in under a minute — a tenfold improvement over existing methods that could significantly accelerate diagnosis of dangerous pathogens like E. coli O157.

The study was led by Takuya Iida, professor at the Graduate School of Science and the Research Institute for Light-induced Acceleration System at Osaka Metropolitan University. It uses a standard commercial multimode optical fiber stripped of its polymer cladding and coated at the tip with a 10-nanometer gold film applied via ion sputtering — a process that transforms an ordinary fiber into a precision bacterial gathering tool.

The problem with current methods

Many conventional techniques are time consuming, require complex instrumentation, or are limited to collecting targets only near a surface or within a narrow focal region, Iida said. Growing bacteria colonies in culture takes days. Even faster antibody-based immunoassays — the kind used in rapid antigen tests — still require several hours. Many harmful bacteria, including E. coli O157, can trigger severe illness at extremely low concentrations, meaning they are often too sparse in a sample to detect reliably without first concentrating them.

How the gold fiber works

When a laser is beamed into the fiber, the gold-coated fiber tip absorbs the light and converts it into heat. This localized heating induces fluid motion and microscopic bubble formation in the surrounding liquid.

That bubble is the functional core of the technique. The fiber hangs in the liquid at any chosen depth, and the resulting convection sweeps in from the sides and from above and below simultaneously.

Earlier photothermal approaches placed gold-coated flat substrates at the base of a sample and heated from below — pulling bacteria only horizontally, along a surface, where friction slowed the flow. The fiber geometry removes that constraint entirely, enabling three-dimensional optical condensation from any position within the liquid sample.

What it achieves

When positioned away from the substrate, the module assembled 10³–10⁵ bacteria and microparticles from a 20-microliter sample within 60 seconds. This approach increased assembly efficiency by more than tenfold compared with conventional two-dimensional photothermal methods, concentrating over 10% of all target objects through combined horizontal and vertical convection.

In practical terms: a device that previously gathered perhaps 1,000 bacteria from a sample can now gather up to 100,000 from the same volume in the same time. That concentration step is what makes downstream detection feasible at trace quantities — once the targets are packed into a single spot, standard optical sensors or spectroscopy tools can reliably identify them.

The technique is also not limited to bacteria. It could also identify nanoparticles and other micro- and nanoscale entities that are affecting the immune system and making disease worse.

Practical constraints and trade-offs

The research team plans to integrate the optical condensation module with downstream analytical tools including optical sensing and spectroscopy, and to test the approach across a broader range of target materials and sample conditions.

Ultimately, we aim to develop a versatile and reliable approach for rapid, sensitive analysis in small-volume liquid samples, contributing to future advances in bioanalytical research, environmental monitoring, and related analytical technologies, Iida said.

The 20-microliter sample volume is notable — roughly the amount in a single small drop of liquid — suggesting a path toward point-of-care diagnostic tools compact enough for field use in clinical settings or environmental monitoring, where sending samples to a laboratory and waiting days for culture results carries real consequences for disease control outcomes.

The study was first published in the journal Communications Physics.

Reference

Hayashi, K., Tamura, M., Fujiwara, M. et al. Highly efficient three-dimensional optical condensation of nano- and micro-particles using a gold-coated optical fibre module. Commun Phys 9, 68 (2026). https://doi.org/10.1038/s42005-025-02480-9

Abstract

Detecting trace amounts of harmful bacteria and nanoscale biomarkers is essential for early diagnosis and disease prevention. However, conventional methods, such as cultivation and immunoassays, are time-consuming and suffer from limited biological sensitivity. To address these limitations, we developed a rapid and highly sensitive detection method based on optical condensation using a metallic thin-film-coated optical fibre module. Acting as a photothermal source, this module induces convection and bubble formation at the fibre tip, enabling efficient three-dimensional condensation of targets within liquid samples. When positioned away from the substrate, the module assembled 103–105 bacteria and microparticles from a 20 μL sample within 60 s. This approach increased assembly efficiency by more than ten-fold compared with conventional two-dimensional photothermal methods, concentrating over 10% of all target objects through combined horizontal and vertical convection. These findings highlight the potential of this technique for advancing bioanalytical detection, drug delivery and material assembly technologies.