Rapid Micro Methods Blog

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.

Oxford University Develops Faster Way to Treat Sepsis

2026-06-30T04:37:37.920-04:00

Image created by Dr. Michael J. Miller

The method uses rapid DNA sequencing to identify organisms causing the infections and predict antibiotic resistance. This advancement is expected to improve care for sepsis patients and help reduce unnecessary antibiotic use. Currently, up to 245,000 people develop sepsis each year in the UK, with 48,000 of those dying as a result.

The current practice involves growing bacteria from a blood sample, a process that takes between one to three days. Once the bacteria are grown, they are tested against different antibiotics to determine which ones are effective.

The new method, developed by a team supported by the NIHR Biomedical Research Centre (BRC): Oxford, uses genetic sequencing to identify bacteria in real time.

This allows doctors to know within hours which antibiotics will be effective.

The study was led by Professor David Eyre, Professor of Infectious Diseases at the University of Oxford’s Big Data Institute.

Professor Eyre explained: “We know that treating these bloodstream infections rapidly is vital to increase the chances of survival.

“But clinicians often need to start giving antibiotics before the lab results are available.

“All too often, the first antibiotics given to a patient do not kill the bacteria, so we need to change antibiotics, which delays patients getting the most effective treatment.”

The team utilised an Oxford Nanopore sequencing device to read all the DNA in a positive blood sample.

This enabled them to identify the infecting organism and look for known genetic markers of antimicrobial resistance.

Dr Kumeren Govender, co-author and BRC Oxford researcher, said: “In nearly 300 samples, we showed that this was a highly accurate method for finding the bacteria that standard lab testing found, and it did this much quicker than standard testing –around three and a half hours after we first detected bacteria growing, instead of 12.

"We were also able to identify resistant infections 20 hours faster than the current standard testing.”

This study was the largest of its kind to use metagenomic sequencing on routine blood samples in a clinical setting.

In addition to the common pathogens found by routine testing, this sequencing approach identified 18 additional bacteria that standard testing had not previously found.

Professor Eyre, who also co-leads the NIHR BRC: Oxford’s Modernising Medical Microbiology and Big Infection Diagnostics Theme, said these findings demonstrate the potential of sequencing to improve diagnosis of bloodstream infections and inform decisions about the most effective antibiotic treatments.

His team will now conduct another BRC Oxford-supported study comparing this sequencing method with four other potential new methods for identifying pathogens or antibiotic resistance in bloodstream infections.

The team will also carry out a national survey of NHS hospitals to determine where these tests might have the most benefit for patients, alongside a health economics study to estimate which technology or technologies provide the best value for money for the NHS.

A New Way to Detect Harmful Bacteria via AI-powered Real-Time Sensing

2026-06-02T16:49:46.246-04:00

Image created by Dr. Michael J. Miller

Healthcare-associated infections remain a major global challenge, particularly as antimicrobial resistance continues to rise. Rapid and accurate detection of bacteria is essential, not only to treat patients effectively, but also to prevent the spread of infection.

INL researchers Susana Costa, Hedieh Mahmoodnia, Fábio Gonçalves, Adelaide Miranda and Pieter De Beule, in collaboration with INESC TEC, have developed a new approach that could transform how bacterial infections are identified. The research was published in Scientific Reports.

Instead of relying on traditional methods that can take days and require complex laboratory procedures, the team focused on something bacteria naturally produce: volatile organic compounds. These are small molecules released during bacterial metabolism, i.e. as bacteria live and grow, forming a unique chemical “fingerprint” for each species.

To capture these fingerprints, INL researchers developed a real-time sensing system based on a photoionization detector. By using multiple light sources, the system captures distinct signal patterns from each bacteria species.

Artificial intelligence then learns to recognise these patterns, allowing accurate identification of the bacteria.

Susana Costa explains that “by converting the signals into image-like representations, we trained a neural network to recognise and differentiate between bacterial species. This approach enables accurate recognition, while reducing the need for large training datasets, which are often difficult and time-consuming to obtain.”

The detection system was able to sense and distinguish clinically relevant bacteria, including Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Klebsiella pneumoniae, at low concentrations and in real time.

By combining advanced sensing with AI-driven analysis, this work opens new possibilities for faster, simpler, and more adaptable diagnostic tools. In a near future, such technologies could support earlier detection of infections, more targeted treatments, and improved patient outcomes.

Reference

Costa, S.P., Cardoso, A., Mahmoodnia, H. et al. Bacterial species differentiation via real-time detection of microbial volatile organic compounds using a wavelength multiplexed photoionization detector and AI image-based analysis. Sci Rep 16, 15924 (2026). https://doi.org/10.1038/s41598-026-46818-x

Abstract

Healthcare-associated infections (HCAIs) contribute significantly to global mortality, driven by the increasing antimicrobial resistance. Rapid, high-throughput bacterial detection is crucial for infection control and patient care. We report a real-time, multiplex lamp-based Photoionization Detector (PID) assisted by AI-image-based analysis for bacterial identification. Using four lamps with varying ionization energies, the sensor selectively ionizes VOCs emitted by bacteria, producing four distinct current curves for each target species (Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Klebsiella pneumoniae). These curves were transformed into image representations, capturing their spectral patterns for bacterial differentiation. A pre-trained ResNet-18 Convolutional Neural Network (CNN) within a Few-Shot Learning (FSL) framework extracted key features, enabling accurate (> 88%) bacterial differentiation even with limited labeled data. This sensor detected bacterial concentrations as low as 10² CFU and distinguished contamination levels. The synergistic integration of PID sensing with AI-driven analysis offers a powerful approach to rapid bacterial diagnostics, demonstrating strong potential for clinical implementation and improved patient care. This study marks an early step toward AI-based VOC sensing, where FSL acts as a proof-of-concept under data scarcity.

Bringing Bacteria into Better Focus

2026-06-02T16:41:31.285-04:00

Image created by Dr. Michael J. Miller

How to collect what you can barely find? Concentrate it.

Osaka Metropolitan University researchers have developed a light-driven technique that quickly amasses thousands of bacteria into a single spot, boosting detection speed and sensitivity. Their approach paves the way for earlier diagnosis of disease.

Many harmful bacteria, such as E. coli O157, can trigger severe ailments even at very low concentrations. Rapid detection of trace quantities of bacteria is essential to facilitate early diagnosis and prevent disease. The technique could also identify nanoparticles and other micro- and nanoscale entities that are also affecting the immune system and making the disease worse.

“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,” said Takuya Iida, professor at the Graduate School of Science and Research Institute for Light-induced Acceleration System (RILACS) at Osaka Metropolitan University and lead author of the study.

Cultivating bacteria in the lab can take days, and even faster antibody-based immunoassays still require several hours.

Looking for a fast yet sensitive alternative, the team turned to something else that has these properties: light.

The researchers created a metallic thin-film-coated optical fiber that acts as a localized photothermal source. When a laser is beamed into the fiber, the gold-coated fiber tip absorbs light and converts it into heat. This localized heating induces fluid motion and microscopic bubble formation in the surrounding liquid. Together, these effects create three-dimensional convection currents that transport bacteria and particles and concentrate them between the bubble and the fiber tip.

Credit: Osaka Metropolitan University

“Unlike conventional photothermal techniques that primarily operate in two dimensions along a surface, this system captures targets from all directions within the liquid,” Iida said.

As a result, it can assemble between thousands and hundreds of thousands of bacteria or microparticles from a 20-microliter sample in just 60 seconds. This is a more than tenfold improvement in efficiency compared to traditional approaches.

“Our results demonstrated that complex optical setups are not required to achieve high-efficiency concentration, and that a compact fiber-based approach can substantially enhance collection performance in liquid environments,” Iida explained.

The researchers plan to integrate this optical condensation technique with downstream analytical tools, such as optical sensing and spectroscopy, and to test it across a broader range of target materials and 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 study was published in Communications Physics.

Reference

Abstract

Tentacles in Solution: UCF Research Speeds Up DNA Biosensing

2026-06-02T16:31:19.952-04:00

Image created by Dr. Michael J. Miller

Detecting disease in a blood sample. Monitoring contaminants in drinking water. Identifying biological threats before they can spread. DNA biosensors play a critical role in each of these, but many rely on a slow process that can miss fleeting signals or delay results.

At the University of Central Florida (UCF), researchers are developing a new approach inspired by squids, octopuses and other cephalopods, one that doesn’t wait for targets to arrive, but actively reaches out to capture them. Led by Dmitry Kolpashchikov, a professor in UCF’s Department of Chemistry, the work introduces a DNA-based system designed to capture target molecules more efficiently by extending into the surrounding solution.

“One of the biggest challenges in biosensing is something surprisingly simple: molecules take time to move,” Kolpashchikov says. “Imagine trying to catch fish in a huge lake with a tiny net, most fish will never come close enough to be caught. Traditional sensors work the same way: they passively wait for target molecules (analytes) to randomly bump into them.”

The project, supported by a $272,000 award from the U.S. National Science Foundation, reframes how biosensors operate, shifting from passive detection toward active engagement.

Targeting Molecules Through DNA

Conventional biosensors rely on diffusion, meaning target molecules must randomly move through a solution before encountering a sensing surface. This process, known as mass transport limitation, can slow detection and limit performance in time-sensitive applications.

Kolpashchikov’s approach addresses this constraint by incorporating nanostructures composed of DNA strands that extend outward from the sensor. These flexible extensions function like molecular tentacles, weakly interacting with passing targets and increasing the likelihood that they will be captured.

Rather than waiting for signals to arrive, the system draws them closer.

Speeding Detection

The speed at which a sensor can detect its target is often as important as detection sensitivity and specificity. In contexts such as medical diagnostics, environmental monitoring and food safety, delays can reduce reliability or limit usefulness altogether.

By increasing the rate at which target molecules are gathered and concentrated near the sensing surface, the DNA cephalopod approach may enable faster, more responsive detection systems, particularly in applications that depend on real-time or near-real-time analysis.

“Slow sensors can miss short-lived biological signals, allow samples to degrade, and delay responses to threats,” Kolpashchikov says, “Faster detection reduces costs (less time, fewer reagents), improves accuracy, and enables real-time monitoring — something essential for healthcare, environmental safety, and biosecurity.”

DNA as Structure and Sensor

The system uses DNA not only as a recognition element but also as a structural material. Engineered strands extend from the sensor into the surrounding environment, forming a dynamic interface that interacts with nearby molecules.

These extensions do not bind targets permanently at first. Instead, they weakly capture and release them, effectively increasing the local concentration of target molecules near the sensor’s core detection region. This process improves detection efficiency without requiring additional mechanical or chemical input.

By designing DNA nanostructures that actively interact with nearby molecules, the system creates a sensing environment that is more responsive and efficient.

“DNA is uniquely suited for building nanoscale machines,” Kolpashchikov says. “It’s programmable, predictable and relatively inexpensive.”

In this system, DNA strands self-assemble into a structure resembling a microscopic octopus, what the team calls a “‘DNA cephalopod.’.” A central sensor is surrounded by long, flexible “‘tentacles”’ that extend into the solution. Each tentacle carries weak binding sites that briefly capture target molecules and pass them along from one site to the next, guiding them toward the center, where the sensor binds them more strongly and triggers detection.

Applications Across Fields

The improved speed and sensitivity of this approach expand the potential use of biosensors across multiple domains.

Possible applications include rapid detection of harmful bacteria in water and food systems, early-stage diagnosis through identification of DNA or RNA biomarkers, and forensic analysis requiring precise detection of biological material

By enabling sensors to detect smaller quantities of target molecules more quickly, the technology may support more timely and accurate decision-making in both clinical and field settings.

“This approach could dramatically improve how we detect biological molecules,” Kolpashchikov says. “The potential applications are broad: rapid disease diagnostics, including early cancer detection, real-time monitoring of pathogens in water and food. Perhaps most exciting is that this is a general strategy. The same ‘tentacle’ concept could be applied for detection of proteins and small biological molecules.”

A New Method of Rapid Analyte Detection

As with many emerging technologies, translating laboratory advances into real-world systems presents challenges. Performance in complex environments, where multiple substances interact simultaneously, remains an area for further study.

Scaling the technology and integrating it into existing diagnostic platforms will also be critical steps in determining its broader applicability.

Rather than treating biosensing as a passive process governed by chance encounters, Kolpashchikov’s work suggests a different model, one in which sensors actively engage with their environment, reaching into the surrounding space to capture what drifts.

Dubai Municipality Launches Advanced ViruGenetics Lab to Detect Foodborne Viruses

2026-06-02T16:24:47.789-04:00

Image created by Dr. Michael J. Miller

Dubai Municipality has launched the ViruGenetics Lab at Dubai Central Laboratory, using advanced digital PCR (dPCR) and genomic technologies to rapidly detect foodborne viruses including norovirus and hepatitis A and E.

Dubai Municipality has launched the ViruGenetics Lab, a new advanced testing facility dedicated to detecting foodborne viruses using cutting-edge genomic technologies. The lab is based at the Dubai Central Laboratory Department and marks the first facility of its kind in the UAE.

The initiative reflects Dubai Municipality’s ongoing commitment to strengthening food safety systems, safeguarding public health, and advancing laboratory innovation to enhance quality of life in Dubai.

The ViruGenetics Lab is designed to process around 60 samples per day, with capacity to scale up to 100 samples during emergencies. It delivers rapid turnaround times, supporting faster inspections and enabling more timely, data-driven regulatory decisions.

The facility represents a strategic expansion of Dubai Central Laboratory’s capabilities in line with international best practice in food control, while also supporting the development of globally recognised standards for laboratory testing and calibration.

At its core, the lab utilises digital PCR (dPCR) technology, one of the most advanced molecular diagnostic tools available. This enables highly sensitive and precise detection of viral pathogens, including norovirus, hepatitis A, and hepatitis E.

The system is particularly effective for complex food matrices such as dairy products, seafood, juices, and fresh produce, maintaining accuracy even in the presence of inhibitory substances. All testing is conducted in accordance with ISO/IEC 17025 standards, ensuring high levels of quality, reliability, and analytical integrity.

Commenting on the launch, Eng. Hind Mahmoud Ahmed, Director of the Dubai Central Laboratory Department at Dubai Municipality, said the facility forms part of a broader strategy to establish specialised laboratories equipped with advanced global technologies. She said the initiative reinforces Dubai’s position as a global leader in food safety and health oversight.

Beyond routine testing, the ViruGenetics Lab will also function as a scientific research platform, supporting collaboration with universities and research institutions. It aims to contribute to a national database of foodborne viruses and strengthen understanding of viral risks within the food chain.

By providing precise genetic-level data, the lab is expected to support more proactive, science-led decision-making across food safety authorities and further enhance the UAE’s health protection ecosystem.

WHO Launches New Global Toolkit to Expand Faster, Easier Tuberculosis Testing Closer to Patients

2026-06-02T16:23:02.386-04:00

Image created by Dr. Michael J. Miller

The World Health Organization (WHO) and the Stop TB Partnership have launched a major new implementation toolkit aimed at helping countries rapidly expand access to faster, simpler, and more accessible tuberculosis (TB) testing — particularly in primary healthcare and community settings where millions of cases continue to go undiagnosed.

The newly released TB Near Point-of-Care and Swab-Based Testing Toolkit is designed to support countries implementing WHO's latest recommendations on near point-of-care nucleic acid amplification tests (NPOC-NAATs) and tongue swab-based diagnostic methods.

Health experts say the initiative could significantly improve early TB detection, especially among vulnerable populations who struggle to access conventional diagnostic services or cannot produce sputum samples required for standard testing.

New Diagnostic Technologies Aim to Bring TB Testing Closer to Communities

The toolkit supports deployment of a new generation of molecular diagnostic technologies that can be used in:

Basic laboratories
Primary healthcare clinics
Community health settings
Decentralized testing facilities

WHO says these near point-of-care molecular tests complement existing laboratory-based TB diagnostics by enabling countries to expand accurate testing much closer to where patients first seek care.

Tuberculosis remains one of the world's deadliest infectious diseases, with millions of people still missing diagnosis and treatment each year despite being preventable and curable.

Public health experts warn that delayed diagnosis remains one of the biggest barriers to controlling transmission and reducing TB-related deaths globally.

Tongue Swab Testing Opens New Pathway for Vulnerable Patients

One of the most significant developments highlighted in the toolkit is WHO's new recommendation supporting the use of tongue swabs for TB diagnosis among adults and adolescents who are unable to produce sputum samples.

This is considered a major breakthrough for groups at particularly high risk of TB-related illness and death, including:

Severely ill patients
Individuals with advanced HIV
Elderly patients
People with difficulty expectorating sputum
Vulnerable or marginalized populations

Traditionally, TB diagnosis has relied heavily on sputum collection, which can be difficult or impossible for some patients.

The new guidance allows tongue swab specimens to be used with both:

Near point-of-care nucleic acid amplification tests (NPOC-NAATs)
Low-complexity automated molecular tests

WHO says this could substantially improve diagnostic access while reducing barriers to timely testing.

Toolkit Designed for Rapid Country-Level Adaptation

The WHO and Stop TB Partnership say the toolkit was specifically designed to be flexible and adaptable for use by national TB programmes in diverse healthcare settings.

The package includes a wide range of practical implementation resources, including:

Readiness assessment checklists
Training materials and slides
Competency assessment tools
Method verification protocols
Standard operating procedures (SOPs)
Job aids for specimen collection and testing
Site-level capacity calculators
Automated monitoring and evaluation spreadsheets

Officials say the toolkit is intended to help countries accelerate evidence-based and quality-assured rollout of the new testing approaches while aligning implementation with national guidelines and health priorities.

Expanding Molecular Testing Seen as Critical to Ending TB

WHO has increasingly prioritised molecular testing technologies as part of global efforts to improve TB diagnosis and treatment outcomes.

Nucleic acid amplification tests (NAATs) are capable of rapidly detecting TB bacteria and identifying drug resistance with significantly greater accuracy and speed than older diagnostic methods.

Expanding access to these technologies is considered especially important in low-resource settings where delays in diagnosis often contribute to continued disease transmission and preventable deaths.

By decentralising access to high-quality diagnostics, health agencies hope to close major gaps in TB detection that continue to undermine global eradication efforts.

TB Remains Major Global Health Threat

Tuberculosis continues to pose a major global public health challenge despite decades of international control efforts.

According to WHO:

TB remains among the leading infectious disease killers worldwide
Millions of new TB cases occur annually
Drug-resistant TB remains a growing threat
Large numbers of cases still go undetected or untreated

The disease disproportionately affects low- and middle-income countries, where healthcare infrastructure limitations, poverty, malnutrition, and HIV co-infection continue to drive transmission.

Global health agencies say improving early diagnosis is one of the most effective ways to reduce transmission, improve survival rates, and prevent drug resistance.

Toolkit Supports WHO End TB Strategy

The NPOC/swab toolkit forms part of broader efforts to achieve WHO's global End TB Strategy targets, which aim to dramatically reduce TB deaths, incidence, and catastrophic economic costs associated with the disease.

WHO says easier and more accessible testing strategies are essential if countries are to meet those goals.

"This NPOC/swab toolkit aims to accelerate evidence-based, quality-assured scale-up of these new TB diagnostic interventions by equipping countries with practical, ready-to-use tools," the organisations said.

Officials believe the combination of rapid molecular diagnostics and simplified specimen collection methods could significantly improve case detection rates, particularly in underserved populations.

Broad International Collaboration Behind Toolkit Development

WHO acknowledged the contributions of numerous technical experts and international organisations involved in developing and reviewing the toolkit.

Key contributors included:

R2D2 TB Network
Stop TB Partnership
Global Laboratory Initiative (GLI) Core Group
Technical specialists in TB diagnostics and programme implementation

The collaborative effort reflects growing international emphasis on accelerating innovation and implementation in global TB control strategies.

Community-Based Testing Becoming Increasingly Important

Public health specialists say community-based testing approaches are becoming increasingly critical as countries seek to identify TB cases earlier and reduce diagnostic delays.

Decentralized testing models can help:

Reach underserved populations
Reduce patient travel burdens
Improve treatment initiation speed
Lower healthcare system bottlenecks
Strengthen disease surveillance

Experts believe the new toolkit could play an important role in helping countries operationalise these approaches more quickly and consistently.

As countries continue modernising TB programmes, WHO says expanding accessible molecular diagnostics closer to communities will be essential to reducing the global burden of tuberculosis and moving closer toward eventual elimination targets.

Building a Biosensor to Detect Harmful Bacteria

2026-05-26T11:53:42.510-04:00

Image created by Dr. Michael J. Miller

Researchers at Worcester Polytechnic Institute (WPI) have developed a solid polymer coated with harmless viruses to detect the bacteria Salmonella enterica (S. enterica), an advance that could lead to new ways of finding contamination in the food supply.

The group, led by Yuxiang “Shawn” Liu, an associate professor in the Department of Mechanical and Materials Engineering, reports that the technology can rapidly capture and visualize foodborne bacterial contaminants in tiny fluid samples. With no need for incubation or complicated equipment in research centers, the technology has the potential to be used as a rapid biosensor in field applications and in areas with few resources.

“We have a solid surface that can be used anywhere in the food supply chain, from farm to fridge, to detect foodborne bacteria with minimum human intervention,” Liu says.

Foodborne diseases cause millions of illnesses and an estimated 420,000 deaths worldwide annually. S. enterica, a leading cause of foodborne illness, can spread through fecal matter and has been found in raw and undercooked foods, such as eggs, meat, milk, and fresh produce. The bacterium infects the intestines, causing diarrhea, fever, and abdominal cramps.

Conventional tests for foodborne bacteria typically involve lab techniques that require special equipment and training. Samples may need to be incubated to allow bacteria to grow so they can be counted, and tests at research centers can take 24 to 48 hours. Other approaches involve amplifying segments of genetic material in samples or detecting antibacterial antibodies in a sample, but those tests may not differentiate live pathogens from dead pathogens. Testing devices with antibodies also tend to have a limited shelf life at room temperature.

The WPI researchers went a different route, starting with a flat, textured, and flexible polymer. They attached bacteriophages (phages), which are viruses that develop through natural processes, to the polymer using a chemical process. Phages can identify and trap specific bacteria that are passing by.

The polymer, about the size of a small fingernail, was then placed on the bottom of a channel in a palm-size microfluidic device, and the channel was sealed from the top by a piece of biocompatible plastic tape. The researchers pumped small drops of fluid containing S. enterica through the channel, and the phages concentrated the bacteria on the solid polymer for detection.

As a final step, the researchers used a microscope and a light technique called fluorescence imaging to examine the polymers for bright spots where phages had concentrated the bacteria. Overall, the researchers found that the phage-coated polymers inside a portable device successfully captured S. enterica so the bacteria could be seen and assessed at a low concentration level that is still dangerous to human beings but challenging to detect when using existing in-field methods.

The research was published in ACS Applied Bio Materials, a peer-reviewed journal of the American Chemical Society. Co-authors with Liu were PhD student Seyed Hamed Ghavami; Teaching Professor Christopher R. Lambert from the Department of Chemistry and Biochemistry; and Jessica Drozd ’26. The work was supported, in part, with funding from the Gapontsev Family Collaborative Venture Fund.

Liu’s research focuses on applications that use light to interact with matter at microscopic and nano scales. Some of his work concentrates on positively impacting the quality and safety of food. In collaboration with other WPI researchers, Liu also has worked on using light to image blood clots with fiber-optic technology and a flexible endoscope that can bend in a patient’s voice box to reach and destroy tiny tumors.

Liu says the S. enterica detection device outlined in the paper will need more work but could be developed to detect multiple pathogens simultaneously and to detect pathogens in groundwater as well as food. Eventually, the technology might be incorporated into food packaging where it could detect contaminants by coming into direct contact with food.

“To translate these findings to practical use, more work is needed on the best ways to prepare samples for testing,” Liu says. “We also want to transition from microscopes to portable readers, such as smartphones, to simplify the process of detecting bacteria. The goal is to create a technology so simple and easy to use that inspectors, retailers, consumers, and others can simply use an app to scan a package and detect pathogens.”

Reference

Phage-Loaded Microfluidic Device for Selective Bacterium Detection with a High Potential for in-the-Field Applications. Hamed Ghavami, Christopher R. Lambert, Jessica Drozd, and Yuxiang Liu. ACS Applied Bio Materials 2026 9 (9), 4002-4012. DOI: 10.1021/acsabm.5c01652

Abstract

Detection of foodborne bacteria is critical because these pathogens cause foodborne outbreaks, which is a major public health concern worldwide. Conventional microbiological methods include plating and colony counting, molecular techniques such as polymerase chain reaction (PCR), and enzyme-linked immunosorbent assays (ELISA). These methods can be quite sensitive and specific, but they are also slow, require labor work, and often involve complex sample preparation. These limitations drive the development of faster and point-of-use detection techniques. In this study, we present a microfluidic biosensor platform based on P22 bacteriophage-loaded PDMS surfaces for rapid detection of Salmonella enterica. The PDMS surface had microscale topographical roughness, which helps improve immobilized phage concentration and promotes their capabilities to capture target bacteria. The system allows rapid bacteria detection with an experimentally demonstrated limit of detection of 9.15 × 103 cells/mL and a demonstrated specificity for Salmonella enterica over Staphylococcus aureus employed as a non-target control. Such detection was achieved under continuous flow conditions without the need of incubation, which implies its high potential for in-field applications and in resource-limited locations. This work demonstrates a rapid and selective approach for bacterial detection with strong potential for real-world food and water safety applications.

A Fast Method for Measuring How Well Air Disinfection Works: See How it Glows

2026-04-06T08:58:00.001-04:00

Image created by Dr. Michael J. Miller

The effectiveness of air disinfection devices may now be measured in minutes, rather than hours, with a new technique from University of Michigan Engineering. This is important for researchers developing better antiviral air purifiers, helping to mitigate outbreaks of viral respiratory diseases and prepare for the next pandemic.

The new method harnesses a property known as UV fluorescence, or how molecules absorb UV light, followed shortly thereafter by emission of energy at another wavelength. It turns out viral aerosols shine brighter before disinfection treatment than after. This finding offers the potential to indirectly but rapidly track the performance of air disinfection technologies and more.

“Our findings suggest that it may be possible to detect changes in aerosol infectivity in a rapid, real-time manner without tedious laboratory procedures,” said Zhenyu Ma, a U-M postdoctoral research fellow, and first author of the study in Plasma Chemistry and Plasma Processing. “As the field of application for this technology becomes clearer, we could use it to better understand the behavior of pathogenic aerosols and their infectivity, thereby providing essential information for public health guidelines.”

The speed of the new approach, developed in the lab of Herek Clack, a U-M associate professor of civil and environmental engineering, is key. The standard method of evaluating an air disinfection process requires collecting pathogen samples from air before and after treatment. For viruses, it involves exposing host cells to the pathogen sample so that the viruses have something to infect. Then, technicians look for signs of infection through a microscope, a labor-intensive process that yields just a single measurement of air disinfection performance.

In contrast, U-M’s approach yields results after several minutes of sampling a small portion of the air stream entering, and then exiting, an air disinfection device or chamber. The sampled air streams flow separately into a device that measures the size of each particle, exposes it to UV light and measures the intensity of its glow. With thousands of these measurements taken over a couple of minutes of sampling, naturally occurring particle-to-particle variations cause the fluorescence intensity measurements to take the shape of a bell curve.

This bell curve shifts to lower intensities as the fraction of viral aerosols inactivated by the disinfection process increases. As a result, researchers can measure the fluorescence intensity of the air sample before and after the disinfection process and compare them to figure out how well disinfection worked.

Once the expected shift in the bell curve is known for a particular pathogen, between treated and untreated viruses, the effectiveness determination takes just a few minutes. For researchers like Clack, who develop disinfection processes, this means faster prototyping and testing at different air flow rates, air temperatures, humidity levels and more.

“Even as the paradigm has shifted regarding the significance of airborne disease transmission, air disinfection technologies that do not rely on filtering air suffer slow development cycles because of how tedious it traditionally has been to prove how well the pathogens have been inactivated. Having an indirect indicator, properly calibrated, for pathogen infectivity could speed up that development process tremendously,” said Clack.

Fluorescence monitoring could also be effective for disinfection tools such as ozone and chlorine, the researchers suggest. But for techniques that disrupt the virus’ genome, such as ultraviolet light, fluorescence will not work. The genome is too deep inside the virus to be reached by these fluorescence detection methods, so their fluorescence signatures don’t change in the same way.

Clack’s group studies interactions between aerosols and strong electric fields. These fields produce non-thermal plasmas, or regions containing charged molecular fragments, which damage viruses and render them harmless. Their group has demonstrated that non-thermal plasmas are capable of reducing the number of infectious viruses in flowing air by 99.9% in lab testing as well as at enclosed livestock operations. Clack’s startup, Taza Aya, has prototyped plasma-based respiratory protective gear, currently being tested in a Michigan turkey processing plant.

Reference

Ma, Z., Clack, H.L. Using Viral Aerosol Fluorescence for Detection of Virus Infectivity Change Induced by Non-thermal Plasma. Plasma Chem Plasma Process 46, 27 (2026). https://doi.org/10.1007/s11090-026-10648-6

Abstract

Airborne transmission of infectious diseases poses a great threat to public health and the global economy, prompting increased interest in the detection and mitigation of infectious airborne pathogens. Development of air disinfection technologies – those that rely on pathogen neutralization or inactivation rather than particle filtration, such as non-thermal plasma (NTP) – can require extensive tests to determine how the degree of disinfection is affected by operational settings, environmental conditions, aerosol composition, pathogen type, among other factors. Such parametric evaluation is made much more tedious by the need to physically extract pre- and post-treatment aerosol samples for comparative microbiological assays in order to measure change in pathogen viability and the efficacy of the neutralization/inactivation process. UV aerosol fluorescence has been used to detect and characterize aerosols of biological origin, such as pollen. In this study, UV fluorescence of MS2 bacteriophage aerosol is studied for its potential to serve as an indicator of changing viral aerosol infectivity, one that can provide a quicker indication of a change in viral aerosol infectivity than conventional bioaerosol collection followed by microbiological assay. In the present study, infectivity assays and fluorescence measurements of viral aerosols are taken before and after non-thermal plasma treatment. Results indicate that NTP treatment induces infectivity loss and diminished fluorescence intensity. Diminished fluorescence intensity and reduced infectivity are positively correlated, both becoming more pronounced with increased intensity of non-thermal plasma treatment. These findings suggest UV aerosol fluorescence could serve as a fast indicator of airborne virus infectivity change during test, evaluation, and optimization of air disinfection processes like NTP.