Rapid Diagnostics Research Articles

Rapid detection of SARS-CoV-2 RNA using a one-step fast multiplex RT-PCR coupled to lateral flow immunoassay

BackgroundThe COVID-19 has put emphasis on pivotal needs for diagnosis and surveillance worldwide, with the subsequent shortage of diagnostic reagents and kits. Therefore, it has become strategic for the countries to access diagnostics, expand testing capacity, and develop their own diagnostic capabilities and alternative rapid accurate nucleic acid diagnostics that are at lower costs. Here, we propose a visual SARS-CoV-2 detection using a one-step fast multiplex reverse transcription-PCR (RT-PCR) amplification coupled to lateral flow immunoassay detection on a PCRD device (Abingdon Health, UK).MethodsWe developed various simplex fast-PCRs for screening sets of primer pairs newly designed or selected from literature or from validated WHO diagnostics, targeting S, N, E, RdRp or ORF1ab genes. We retained primers showing specific and stable amplification to assess for their suitability for detection on PCRD. Thus, fast RT-PCR amplifications were performed using the retained primers. They were doubly labeled with Fam and Biotin or Dig and Biotin to allow visual detection of the labeled amplicons on the lateral flow immunoassay PCR Detection (PCRD) device, looking at lack of interaction of the labeled primers (or primer dimers) with the test-lines in negative or no RNA controls. We set up all the assays using RNAs isolated from patients’ nasopharyngeal swabs. We used two simplex assays, targeting two different viral genomic regions (N and E) and showing specific detection on PCRD, to set up a one-step fast multiplex RT-PCR assay (where both differently labeled primer pairs were engaged) coupled to amplicons’ detection on a PCRD device. We evaluated this novel assay on 50 SARS-CoV-2 positive and 50 SARS-CoV-2 negative samples and compared its performance to the results of the quantitative RT-PCR (RT-qPCR) assays used for diagnosing the patients, here considered as the standard tests.ResultsThe new assay achieved a sensitivity of 88% (44/50) and a specificity of 98% (49/50). All patients who presented Ct values lower than 33 were positive for our assay. Except for one patient, those with Ct values above 33 returned negative results.ConclusionOur results have brought proof of principle on the usefulness of the one-step fast multiplex RT- PCR assay coupled to PCRD as a new assay for specific, sensitive, and rapid detection of SARS-CoV-2 without requiring costly laboratory equipment, and thus, at reduced costs in a format prone to be deployed when resources are limited. This assay offers a viable alternative for COVID-19 diagnosis or screening at points of need.

Single and dual RPA-CRISPR/Cas assays for point-of-need detection of Stewart's wilt pathogen (Pantoea stewartii subsp. stewartii) of corn and Maize dwarf mosaic virus.

Pantoea stewartii subsp. stewartii and Maize dwarf mosaic virus (MDMV) infections severely affect corn productivity worldwide. Rapid point-of-need diagnoses of quarantine pathogens P. stewartii subsp. stewartii and MDMV are required for early detection, timely disease management and ensuring phytosanitary regulations. Recombinase polymerase amplification (RPA) is an isothermal technique suitable for rapid diagnostics using minimally processed samples. Integrating CRISPR/Cas collateral activities with the RPA assays further enhances the specificity and sensitivity of molecular toolkits for diagnostic assays. RPA-CRISPR/Cas12a assay targeting the intergenic spacer region between capsular polysaccharide genes cpsA and cpsB of P. stewartii subsp. stewartii detected 1 × 10-6 ng DNA μL-1 using real-time fluorescence and blue light observation methods, and 1 × 10-4 ng DNA μL-1 using the lateral flow dipstick (LFD). Likewise, RPA-CRISPR/Cas13a assay detected MDMV coat protein (CP) gene with an ultrasensitive detection limit of 3.69 × 10-7 ng μL-1 using the real-time fluorescence and blue light observation methods, and 3.69 × 10-5 ng μL-1 using the LFD. The dual RPA-CRISPR/Cas assays detected both pathogens without compromising the speed and/or detection sensitivity of the single assays. The validated assays provide a useful and sensitive molecular tool for detecting two quarantine pathogens of maize within a minimal resource framework suitable for fast-tracking the containment strategies. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

RT-RPA as a dual tool for detection and phylogenetic analysis of epidemic arthritogenic alphaviruses

Chikungunya (CHIKV), o’nyong-nyong (ONNV), and Mayaro (MAYV) viruses are transmitted by mosquitoes and known to cause a debilitating arthritogenic syndrome. These alphaviruses have emerged and re-emerged, leading to outbreaks in tropical and subtropical regions of Asia, South America, and Africa. Despite their prevalence, there persists a critical gap in the availability of sensitive and virus-specific point-of-care (POC) diagnostics. Traditional immunoglobulin-based tests such as enzyme-linked immunosorbent assay (ELISA) often yield cross-reactive results due to the close genetic relationship between these viruses. Molecular diagnostics such as quantitative polymerase chain reaction (qPCR) offer high sensitivity but are limited by the need for specialized laboratory equipment. Recombinase polymerase amplification (RPA), an isothermal amplification method, is a promising alternative to qPCR, providing rapid results with minimal equipment requirements. Here, we report the development and validation of three virus-specific RT-RPA-based rapid tests for CHIKV, ONNV, and MAYV. These tests demonstrated both speed and sensitivity, capable of detecting 10–100 viral copies within 20 min of amplification, without exhibiting cross-reactivity. Furthermore, we evaluated the clinical potential of these tests using serum and tissue samples from CHIKV, ONNV, and MAYV-infected mice, as well as CHIKV-infected human patients. We demonstrate that the RPA amplicons derived from the patient samples can be sequenced, enabling cost-effective molecular epidemiological studies. Our findings highlight the significance of these rapid and specific diagnostics in improving the early detection and management of these arboviral infections, particularly in resource-limited settings.

Development of a portable multi-step microfluidic device for point-of-care nucleic acid diagnostics

BackgroundThe COVID-19 pandemic has significantly affected global health, economies, and societies, and highlighted the urgent need for rapid, sensitive, affordable, and portable diagnostic devices for respiratory diseases, especially in areas with limited resources. In recent years, there has been rapid development in integrated equipments using microfluidic chips and biochemical detection technologies. However, these devices are expensive and complex to operate, showing limited feasibility for in point of care tests (PoCTs). This study aims to develop a cost-effective, portable, and practical microfluidic nucleic acid PoCT device for rapid virus diagnosis. ResultsHere, we developed a device based on freeze-dried reverse transcription loop-mediated isothermal amplification (RT-LAMP) reagents for rapid nucleic acid diagnostics. Homebrew RT-LAMP reagents were optimized to eliminate non-specific amplification. The multi-step device combines nucleic acid extraction, RT-LAMP and fluorescence detection in an integrated microfluidic chip, enabling sample-in, result-out diagnosis. This device showed satisfactory sensitivity in detecting SARS-CoV-2 and Trichomonas vaginalis RNA samples, with a limit of detection (LOD) of 400 copies/μL and 80 copies/μL respectively in 45 minutes. The LOD of cultured Trichomonas vaginalis samples were 0.32 cells/μL in 50 minutes. Additionally, the freeze-dried homebrew RT-LAMP can be stored at 4 °C for up to 30 days while still maintaining high sensitivity and detection capabilities. The cost of diagnosis was reduced to as low as 0.45 $ per reaction. Significance and NoveltyOverall, by integrating freeze-dried homebrew RT-LAMP and microfluidic chip, the device achieves ready-to-use, laboratory-free, quick, resource-independent and cost-effective nucleic acid detection, and provides a feasible alternative to complex equipments. The device shows potentials for point-of-care testing of SARS-CoV-2 and other respiratory diseases in remote or resource-limited areas with proper implementation.

Miniaturized electrophoresis: An integrated microfluidic cartridge with functionalized hydrogel-assisted LAMP for sample-to-answer analysis of nucleic acid.

Accurate detection of pathogenic nucleic acids is crucial for early diagnosis, effective treatment, and containment of infectious diseases. It facilitates the timely identification of pathogens, aids in monitoring disease outbreaks, and helps prevent the spread of infections within healthcare settings and communities. We developed a multi-layered, paper-based microfluidic and miniaturized electrophoresis system for rapid nucleic acid extraction, separation, amplification, and detection, designed for resource-limited settings. Constructed from acrylic, transparency film, pressure-sensitive adhesion, and Whatman paper using a CO2 laser, the setup simplifies traditional methods and eliminates the need for complex equipment. DNA extraction and purification are achieved using Zweifach-Fung bifurcation and Fahraeus effect principles, with detection via a hydrogel-assisted colorimetric isothermal reverse transcriptase-loop-mediated isothermal amplification technique. The system accurately identified the SARS-CoV-2 N-gene and β-actin human gene, validated by a compact electrophoresis setup. In clinical validation with 12 patient specimens, the system demonstrated a positive predictive agreement of 83.0% and a negative predictive agreement of 100%. The system achieves a limit of detection of 1 copy/μl and can potentially transform nucleic acid detection assays in healthcare settings. This study addresses key challenges in nucleic acid detection, such as ensuring sample quality and quantity, reducing reliance on sophisticated equipment, preventing contamination, simplifying procedures, and providing rapid and accurate diagnostics for emerging pathogens.

Construction of a two-dimensional magnetic solid phase extraction microfluidic chip/tandem mass spectrometry platform for the determination of cytarabine in plasma

A novel concept of two-dimensional magnetic solid phase extraction microfluidic chip (2D-MSPE-chip) was proposed, in which two or even more different separation mechanisms of solid phase extraction units were designed and constructed on microfluidic chips in series. The synthesized Fe3O4-UiO-66-C18 was used in the first dimension to retain protein and phospholipid through hydrophobic effect and ZrOP coordination, which could almost eliminate the interference of the matrix effect on the mass spectrum. The second dimensional material utilized Fe3O4-UiO-66-NH2 to enable cytarabine enrichment via π-π and hydrogen bonding. The parameters of 2D MSPE-Chip/tandem MS method such as desorption solvent (5 % acetic acid acetonitrile), loading solvent (20 % acetonitrile), and flow rate for extraction and desorption (10 μL/min) were select. The LOD of the established method for cytarabine in plasma was 0.13 ng/mL, and the linear range was between 2.0 ng/mL and 1000 ng/mL with correlation coefficient (R2 = 0.9991). The matrix effect was −5.0 %. The recovery rate of real samples ranged from 95.5 % to 104.8 %, with RSD ≤ 5.2 %. The chip design was simple and efficient and could be reused, thereby decreasing analysis expenses. It held considerable potential for application in rapid clinical diagnostics and precision medicine.

Antifungal stewardship in Australian hospitals: defining the scope and future targets

AbstractBackgroundAntimicrobial stewardship (AMS) guidelines now recommend antifungal stewardship (AFS) interventions to improve the management of invasive fungal diseases (IFDs). AFS programmes have not been reported in Australia.AimsTo determine the monitoring of antifungal use, AFS strategies and targets, and barriers to AFS implementation in Australian hospitals.MethodsAn electronic quantitative cross‐sectional survey was developed and distributed to public and private hospitals in Australia in February 2018. Descriptive statistics were used to summarise the findings.ResultsEighty‐three Australian hospitals completed the survey with an overall response rate of 58% (83/143). Most hospitals monitored antifungal use (62/83, 75%). Frequently used AFS metrics included costs (48/60, 80%) and yearly point prevalence surveys (45/60, 75%). Core AFS strategies were commonly in place, including preauthorisation requirements (71/80, 89%) and expert antifungal post‐prescription review and feedback (PPRF) (63/80, 79%). Both these strategies were more strictly applied to high‐cost, intravenous agents. Formal education (44/79, 56%) and hospital‐endorsed guidelines (35/79, 44%) were modestly used. Fungal diagnostics and antifungal therapeutic drug monitoring (TDM) were utilised, largely off site. IFD surveillance was infrequently performed (9/77, 12%). Barriers to AFS identified included lack of staff time, prioritisation of AFS, and access to rapid diagnostics and TDM.ConclusionsAFS strategies utilised in Australian hospitals have focused on high‐cost, intravenous agents. Although expert oversight of antifungals is evident, many sites omit potentially important targets for AFS, including fluconazole and oral posaconazole. Identifying these gaps and barriers to AFS will guide the development of an AFS model for hospitals.

Diagnostic advances in sepsis: A comparative analysis of syndromic approaches and molecular biology techniques in clinical bacteremia

Sepsis remains a leading cause of morbidity and mortality. This retrospective study, conducted at Avicenne Military Hospital in Marrakech over ten years, analyzed 3721 blood cultures, identifying 420 cases of bacteremia (11.3% positivity rate). The predominant isolates included Staphylococcus aureus, coagulase-negative staphylococci, Escherichia coli, Klebsiella spp., and Acinetobacter baumannii, with significant resistance patterns observed. Notably, 50% of S. aureus were methicillin-resistant (MRSA), while extended-spectrum beta-lactamase (ESBL) production was found in 30% of E. coli and Klebsiella pneumoniae isolates. The FilmArray multiplex PCR system was utilized to enhance rapid pathogen identification, reducing turnaround time from 72 hours to under 2 hours, and demonstrated high concordance with conventional cultures. The molecular diagnostics identified various resistant strains, including carbapenemase-producing organisms in K. pneumoniae and multidrug-resistant A. baumannii, underscoring the critical need for effective antimicrobial stewardship. Overall, this study emphasizes the evolving epidemiology of bacteremia, the rising antimicrobial resistance, and the utility of rapid molecular diagnostics in guiding timely and appropriate treatment, ultimately aiming to improve patient outcomes in sepsis management.

Arcobacter species isolated from human stool samples, animal products, ready-to-eat salad mixes, and ambient water: prevalence, antimicrobial susceptibility, and virulence gene profiles

IntroductionArcobacter species are emerging foodborne pathogens increasingly associated with human illness worldwide. They are commonly found in the gastrointestinal tracts of animals and are frequently isolated from various food sources, including raw meat, poultry, and seafood. The aim of this study is to investigate the antimicrobial resistance patterns of Arcobacter spp. isolated from human stool samples, animal products, ready-to-eat salad mixes, and ambient water, assess the presence of resistance genes, and explore their potential implications for public health.MethodsIn this study, a total of 683 samples were collected from the Shahrekord area over a 12-month period. Samples were obtained from human stool, chicken meat, raw cow milk, RTE salad mixes, and environmental water sources. Two different methods were used to detect Arcobacter, depending on the sample type: bacteriological isolation and identification, and molecular identification. After identification, antimicrobial susceptibility testing was conducted. Polymerase chain reaction (PCR) was used to identify ten putative Arcobacter virulence and resistance genes.FindingsThe results revealed that Arcobacter spp. were present in 26.06% (178 out of 683) of the tested samples, with varying isolation rates across different sample types. A. butzleri being the most commonly isolated species across all sample types, while A. cryaerophilus was restricted to RTE salads, surface waters, and chicken meat. Notably, A. skirrowii was only isolated from chicken meat and environmental water. The differences of Arcobacter spp. in prevalence between the sample types were statistically significant (p < 0.05), and no significant seasonal variation was found across the sampling periods (p > 0.05). PCR analysis for ten putative virulence genes indicated that the cadF gene was present in all Arcobacter isolates. Similarly, 83.33% of the tested strains harbored the ciaB gene, while other genes were less frequently detected. Regarding resistance genes, tet(O) (7.69%) was the most identified gene, followed by blaOXA-61 (4.37%).ConclusionIn conclusion, this study highlights the alarming prevalence of antimicrobial resistance in Arcobacter spp. Monitoring Arcobacter spp. resistance can be achieved through surveillance, risk assessments, antibiotic stewardship in agriculture, public education, research collaborations, rapid diagnostics, and harmonized policies, all aimed at reducing contamination and safeguarding public health effectively.

Non-invasive in vivo sensing of bacterial implant infection using catalytically-optimised gold nanocluster-loaded liposomes for urinary readout

Staphylococcus aureus is a leading cause of nosocomial implant-associated infections, causing significant morbidity and mortality, underscoring the need for rapid, non-invasive, and cost-effective diagnostics. Here, we optimise the synthesis of renal-clearable gold nanoclusters (AuNCs) for enhanced catalytic activity with the aim of developing a sensitive colourimetric diagnostic for bacterial infection. All-atom molecular dynamics (MD) simulations confirm the stability of glutathione-coated AuNCs and surface access for peroxidase-like activity in complex physiological environments. We subsequently develop a biosensor by encapsulating these optimised AuNCs in bacterial toxin-responsive liposomes, which is extensively studied by various single-particle techniques. Upon exposure to S. aureus toxins, the liposomes rupture, releasing AuNCs that generate a colourimetric signal after kidney-mimetic filtration. The biosensor is further validated in vitro and in vivo using a hyaluronic acid (HA) hydrogel implant infection model. Urine samples collected from mice with bacteria-infected HA hydrogel implants turn blue upon substrate addition, confirming the suitability of the sensor for non-invasive detection of implant-associated infections. This platform has significant potential as a versatile, cost-effective diagnostic tool.

Microfluidic Technologies in Advancing Cancer Research

This review explores the significant role of microfluidic technologies in advancing cancer research, focusing on the below key areas: droplet-based microfluidics, organ-on-chip systems, paper-based microfluidics, electrokinetic chips, and microfluidic chips for the study of immune response. Droplet-based microfluidics allows precise manipulation of cells and three-dimensional microtissues, enabling high-throughput experiments that reveal insights into cancer cell migration, invasion, and drug resistance. Organ-on-chip systems replicate human organs to assess drug efficacy and toxicity, particularly in the liver, heart, kidney, gut, lung, and brain. Paper-based microfluidics offers an alternative approach to accomplish rapid diagnostics and cell- and tissue-based bioassays. Electrokinetic microfluidic chips offer precise control over cell positioning and behavior, facilitating drug screening and cellular studies. Immune response studies leverage real-time observation of interactions between immune and cancer cells, supporting the development of immunotherapies. These microfluidic advances are paving the way for personalized cancer treatments while addressing challenges of scalability, cost, and clinical integration.

Clinical performance of two commercially available rapid antigen tests for influenza, RSV, and SARS-CoV-2 diagnostics.

Rapid diagnostics of respiratory viruses is essential for clinical management, infection control, and epidemiological surveillance. Our results showed that due to the high specificity and still inadequate sensitivity, rapid antigen tests (RATs) might be useful for influenza, respiratory syncytial virus, and severe acute respiratory syndrome coronavirus 2 point-of-care diagnostics when correctly used. The results provide useful information for laboratory and infectious disease control professionals to support decision-making when implementation of the RATs in clinical use is planned.

Self-Interference Digital Optofluidic Genotyping for Integrated and Automated Label-Free Pathogen Detection.

Pathogen, prevalent in both natural and human environments, cause approximately 4.95 million deaths annually, ranking them among the top contributors to global mortality. Traditional pathogen detection methods, reliant on microscopy and cultivation, are slow and labor-intensive and often produce subjective results. While nucleic acid amplification techniques such as polymerase chain reaction offer genetic accuracy, they necessitate costly laboratory equipment and skilled personnel. Consequently, isothermal amplification methods like recombinase polymerase amplification (RPA) have attracted interest for their rapid and straightforward operations. However, these methods face challenges in specificity and automated sample processing. In this study, we introduce a self-interferometric digital optofluidic platform incorporating asymmetric direct solid-phase RPA for real-time, label-free, and automated pathogen genotyping. By integration of digital microfluidics with a DNA monolayer detection method using hyperspectral interferometry, this platform enables rapid, specific, and sensitive pathogen detection without the need for exogenous labeling or complex procedures. The system demonstrated high sensitivity (10 CFU·mL-1), specificity (differentiating four Candida species), detection efficiency (fully automated within 50 min for Gram-negative bacteria), and throughput (simultaneous detection of four indices). This integrated approach to pathogen quantitation on a single microfluidic chip represents a significant advancement in rapid pathogen diagnostics, providing a practical solution for timely pathogen detection and analysis.

Host-Pathogen Interactions in Mycobacterium tuberculosis: Molecular Mechanisms and Implications for Treatment

Worldwide, tuberculosis (TB) due to Mycobacterium tuberculosis is a major public health problem with high morbidity and mortality. This review reviewed the molecular mechanisms and their implications for treatment of M. tuberculosis infection, as well as the complex host pathogen interactions underpinning M. tuberculosis infection. The ability of the pathogen to avoid the host immune responses by utilizing critical strategies for the evasion, for instance, inhibition of phagosome-lyosome fusion and modulation of the host cell pathways are discussed first. T cells play a critical role in the adaptive immune response, and granulomas develop, which are then examined in detail. It importantly informs current treatment protocols which struggle to overcome issues of patient compliance, drug resistance and toxicity. Explored are novel therapeutic approaches targeting bacterial cell wall synthesis, virulence factors and the use of host directed therapies. Recent advances in the development of vaccines, including the innovative endeavors that hope to improve or replace the BCG vaccine, are also reviewed. The review ends by anticipating future directions in TB management, which could be facilitated through efforts related to personalized medicine, and combination therapies, as well as rapid diagnostics to change how we think about treatment. The insights from this study are important for developing novel strategies to fight TB and to bring down its burden globally.

Abstract A010: Biofluid-Ensemble Analysis through Multi-modal Spectroscopy (BEAMS): A deep learning architecture for rapid early-stage liquid biopsy cancer diagnostics

Abstract Introduction: Head and neck cancer (HNC) is one of the most common cancer types globally without an accessible rapid screening method. Conventional screening occurs at the clinician’s office, where grading and staging of the tumors are physically performed, and the clinician’s preliminary diagnosis is usually confirmed through laborious procedures of histology and CT/PET/MR scans. Patient’s quality of life and expected outcomes can be improved through early-stage screening, yet there is no existing technology on the market that tackles this dire need. As such, we propose a rapid liquid biopsy diagnostic platform empowered by deep learning algorithms that makes early-stage non-invasive screening possible. The platform utilizes the complementary vibrational spectral biomarkers information from Fourier Transform Infrared Spectroscopy (FTIR) and Raman spectroscopy. We coupled the techniques with two biofluids: saliva and plasma, which offered information on circulating and localized metabolites. Materials and Methods: The patient’s biofluid is first drop-casted on top of a substrate and air-dried at room temperature. Using an FTIR microscope or a Raman microscope, 25 spot measurements are made in a 5 by 5 grid pattern to produce a comprehensive scan of the sample. The measured spectra are subsequently fed into the deep learning framework to perform classification. The deep machine learning framework is composed of spectrum and subject-level models. Specifically, the spectrum-level model employs cascading convolutional layers to identify the prominent features within the spectrum. This is followed by a sequence of linear layers that further extract features from the convolutional layers and carry out binary classification. For each patient, preliminary classification results of the spectral level model are then evaluated on a per- modality basis across our four modalities (FTIR/Raman measurements made on saliva/plasma) where the patient is classified as positive if any of the modality models deem it so. This framework is applied to a cohort of 96 patients, with 36 non-cancerous patients, 28 early-stage patients, and 32 late-stage patients. Results and Discussion: Our model optimized for general cancer detection has a sensitivity of 91.7%, specificity of 77.8%, and accuracy of 86.5%. Late- stage cancer has proven to be less challenging to detect since our model optimized for late-stage cancer diagnosis has a sensitivity of 93.8%, a specificity of 83.3%, and an accuracy of 88.2%. Our best early-stage model has a sensitivity of 89.3%, a specificity of 69.4%, and an accuracy of 78.1%. Conclusions: Our proposed framework has demonstrated promising capability as the preliminary screening method for high-risk patients (e.g. tobacco users) while providing further interpretability of the deep learning model that is often characterized as a “black box”. The modular nature of our model structure offers substantial flexibility, enabling effective performance even when specific biofluids or spectroscopy modalities are unavailable. Citation Format: Kwan Lun Chiu, Antonio Guillen-Perez, Rebecca Mayer, Wesley Viner, Hanna Koster, Matthew Benson, Jeremy Rowe, Maria Navas-Moreno, Andrew Birkeland, Maria-Dolores Cano, J. Sebastián Gomez-Diaz, Randy Carney. Biofluid-Ensemble Analysis through Multi-modal Spectroscopy (BEAMS): A deep learning architecture for rapid early-stage liquid biopsy cancer diagnostics [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A010.

Rapid diagnosis of Mycoplasma pneumoniae and prediction of antibiotic resistance by nanopore adaptive sampling

AbstractBackgroundMicrobial infections, particularly in children, require rapid and accurate diagnostics. It is difficult to differentiate pathogens from commensal organisms, and it is impossible to identify antibiotic resistance genes that belong to pathogens with current methods. Third‐generation sequencing provides rapid library preparation and real‐time data acquisition. Nanopore normal sampling (NNS) enables unbiased sequencing of clinical samples without amplification, aiding pathogen identification and antimicrobial resistance gene prediction. However, clinical samples often contain a considerable amount of human DNA, potentially masking pathogen data. Nanopore adaptive sampling (NAS) aims to selectively enrich pathogens, promising improved diagnostics for acute infections and better treatment decisions in clinical practice. This study aimed to determine the utility of NAS in enhancing the real‐time detection of pathogens and predicting AMR in infectious disease outbreaks.MethodsThis study used NAS technology to rapidly and directly detect Mycoplasma pneumoniae infection in bronchoalveolar lavage fluid samples from 28 pediatric patients at Shenzhen Children's Hospital. We assessed the efficacy of NAS compared with that of NNS by evaluating the number of microbial reads and the amount of microbial DNA data. We then compared the accuracy of detecting pathogens between NNS and NAS and between NAS and real‐time polymerase chain reaction assays. Furthermore, we predicted antimicrobial resistance (AMR) and examined AMR genes associated with pathogens.ResultsNAS showed up to a 14.67‐fold increase in the amount of microbial DNA data from patients' samples compared with NNS within the initial 2.5 h of sequencing. Additionally, NAS reduced the amount of host DNA data by up to 6.67‐fold compared with NNS. Unlike TaqMan real‐time polymerase chain reaction assays, NAS technology identified dominant pathogens and provided detailed insight into the abundance of the microbial community. Furthermore, NAS was able to predict AMR profiles of microbial communities and attribute specific AMR traits to individual microbes within the samples.ConclusionThis study shows that NAS advances the clinical diagnosis because it can rapidly detect pathogens directly from patients' samples and provides antimicrobial resistance information for clinical guidance. These abilities further facilitate the application of NAS in personalized treatment, reduce the misuse of broad‐spectrum antibiotics, and promote patients' recovery.

A novel ionic liquid-based approach for DNA and RNA extraction simplifies sample preparation for bacterial diagnostics

DNA- and RNA-based diagnostics play a pivotal role in accurately detecting and characterizing health-relevant bacteria, offering insights into bacterial presence, viability and treatment efficacy. Herein, we present the development of a novel extraction protocol for both DNA and RNA, designed to enable simple and rapid molecular diagnostics. The extraction method is based on the hydrophilic ionic liquid (IL) 1-ethyl-3-methylimidazolium acetate and silica-coated magnetic beads. First, we developed an IL-based cell lysis protocol for bacteria that operates at room temperature. Subsequently, we established a magnetic bead purification procedure to efficiently and reproducibly extract DNA and RNA from the IL-lysates. The IL not only lyses the cells, but also facilitates the adsorption of nucleic acids (NAs) onto the surface of the magnetic beads, eliminating the need for a chaotropic binding buffer and allowing for purification of NAs without significant effort and materials required. Lastly, we combined the cell lysis step and the purification step and evaluated the novel IL-based extraction method on periopathogenic bacterial cultures, comparing it to commercial DNA and RNA extraction kits via (RT)-qPCR. In comparison to the reference methods, the IL-based extraction protocol yielded similar or superior results. Furthermore, costs are lower, required materials and equipment are minimal and the process is fast (30 min), simple and automatable. These characteristics favour the developed method for use in routine and high-throughput testing as well as in point-of-care, on-site and low-resource settings, thereby advancing the field of molecular diagnostics.Graphical

NOVEL DEVICE FOR ENHANCING TUBERCULOSIS DIAGNOSIS FOR FASTER, MORE ACCURATE SCREENING RESULTS

Tuberculosis (TB) continues to be a leading global cause of illness and death, highlighting an urgent need for rapid, precise diagnostic solutions. Conventional methods, such as chest X-rays and sputum smear microscopy, often lack adequate sensitivity and specificity, resulting in treatment delays and increased transmission. This research introduces a novel device leveraging deep learning to enhance TB diagnosis, utilizing a convolutional neural network (CNN) model designed to automatically detect TB-related abnormalities in chest X-ray images. The device, powered by a large annotated dataset, incorporates transfer learning to fine-tune pre-trained models, maximizing diagnostic performance. Key evaluation metrics—accuracy, precision, recall, and F1-score—demonstrate the model’s efficacy compared to traditional diagnostics. Preliminary findings show that the AI-driven approach significantly enhances diagnostic accuracy, reducing false negatives and facilitating faster screenings. These results suggest that embedding AI technology in clinical workflows can enable earlier TB detection, optimize healthcare resources, and improve patient outcomes. Future directions include testing the device across diverse clinical environments and exploring integration with mobile health technologies to broaden access to rapid TB diagnostics.

Rapid laboratory diagnosis of urinary tract infection, with or without antibiotic decision support-a small pilot study investigating accuracy and clinical impact.

The study evaluated the accuracy and clinical impact of rapid diagnostics (RD) with or without antibiotic decision support (ADS) for hospitalized patients with urinary tract infections. A two-centre prospective intervention was conducted with 230 patients divided into three groups: RD-only (n = 59), RD plus ADS (n = 56) and a control group (n = 115). Mean laboratory turnaround time for RD was 10 h and 50 min. Of 115 microorganisms, 108 were correctly identified. The error rate for rapid susceptibility determination was 0.85%. Total antibiotic consumption, measured in defined daily doses (DDD), was lower in the intervention groups compared to the control group (ADS: 10.3 DDD, p = 0.01; RD: 10.9 DDD, p = 0.06; control: 13.0 DDD). No significant differences were observed in the use of broad-spectrum antibiotics (p = 0.816). Adherence to antibiotic guidelines was significantly better in the ADS group compared to the control group (p = 0.015) (RD vs control; p = 0.261). The ADS group also received fewer doses of ineffective antibiotics (ADS: 1.8 doses, p = 0.012; RD: 2.4 doses, p = 0.195; control: 3.4 doses). Length of hospital and ICU stays or 30-day readmission rates did not differ across groups. No in-hospital mortality was observed in any group.

AI-Reinforced Wearable Sensors and Intelligent Point-of-Care Tests.

Artificial intelligence (AI) techniques offer great potential to advance point-of-care testing (POCT) and wearable sensors for personalized medicine applications. This review explores the recent advances and the transformative potential of the use of AI in improving wearables and POCT. The integration of AI significantly contributes to empowering these tools and enables continuous monitoring, real-time analysis, and rapid diagnostics, thus enhancing patient outcomes and healthcare efficiency. Wearable sensors powered by AI models offer tremendous opportunities for precise and non-invasive tracking of physiological conditions that are essential for early disease detection and personalized treatments. AI-empowered POCT facilitates rapid, accurate diagnostics, making these medical testing kits accessible and available even in resource-limited settings. This review discusses the key advances in AI applications for data processing, sensor fusion, and multivariate analytics, highlighting case examples that exhibit their impact in different medical scenarios. In addition, the challenges associated with data privacy, regulatory approvals, and technology integrations into the existing healthcare system have been overviewed. The outlook emphasizes the urgent need for continued innovation in AI-driven health technologies to overcome these challenges and to fully achieve the potential of these techniques to revolutionize personalized medicine.