Turnaround Time Research Articles

Evaluating metagenomics and targeted approaches for diagnosis and surveillance of viruses

BackgroundMetagenomics is a powerful approach for the detection of unknown and novel pathogens. Workflows based on Illumina short-read sequencing are becoming established in diagnostic laboratories. However, high sequencing depth requirements, long turnaround times, and limited sensitivity hinder broader adoption. We investigated whether we could overcome these limitations using protocols based on untargeted sequencing with Oxford Nanopore Technologies (ONT), which offers real-time data acquisition and analysis, or a targeted panel approach, which allows the selective sequencing of known pathogens and could improve sensitivity.MethodsWe evaluated detection of viruses with readily available untargeted metagenomic workflows using Illumina and ONT, and an Illumina-based enrichment approach using the Twist Bioscience Comprehensive Viral Research Panel (CVRP), which targets 3153 viruses. We tested samples consisting of a dilution series of a six-virus mock community in a human DNA/RNA background, designed to resemble clinical specimens with low microbial abundance and high host content. Protocols were designed to retain the host transcriptome, since this could help confirm the absence of infectious agents. We further compared the performance of commonly used taxonomic classifiers.ResultsCapture with the Twist CVRP increased sensitivity by at least 10–100-fold over untargeted sequencing, making it suitable for the detection of low viral loads (60 genome copies per ml (gc/ml)), but additional methods may be needed in a diagnostic setting to detect untargeted organisms. While untargeted ONT had good sensitivity at high viral loads (60,000 gc/ml), at lower viral loads (600–6000 gc/ml), longer and more costly sequencing runs would be required to achieve sensitivities comparable to the untargeted Illumina protocol. Untargeted ONT provided better specificity than untargeted Illumina sequencing. However, the application of robust thresholds standardized results between taxonomic classifiers. Host gene expression analysis is optimal with untargeted Illumina sequencing but possible with both the CVRP and ONT.ConclusionsMetagenomics has the potential to become standard-of-care in diagnostics and is a powerful tool for the discovery of emerging pathogens. Untargeted Illumina and ONT metagenomics and capture with the Twist CVRP have different advantages with respect to sensitivity, specificity, turnaround time and cost, and the optimal method will depend on the clinical context.

Integrated Electrochemical Aptamer Biosensing and Colorimetric pH Monitoring via Hydrogel Microneedle Assays for Assessing Antibiotic Treatment.

Current methods for therapeutic drug monitoring (TDM) have a long turnaround time as they involve collecting patients' blood samples followed by transferring the samples to medical laboratories where sample processing and analysis are performed. To enable real-time and minimally invasive TDM, a microneedle (MN) biosensor to monitor the levels of two important antibiotics, vancomycin (VAN) and gentamicin (GEN) is developed. The MN biosensor is composed of a hydrogel MN (HMN), and an aptamer-functionalized flexible (Flex) electrode, named HMN-Flex. The HMN extracts dermal interstitial fluid (ISF) and transfers it to the Flex electrode where sensing of the target antibiotics happens. The HMN-Flex performance is validated ex vivo using skin models as well as in vivo in live rat animal models. Data is leveraged from the HMN-Flex system to construct pharmacokinetic profiles for VAN and GEN and compare these profiles with conventional blood-based measurements. Additionally, to track pH and monitor patient's response during antibiotic treatment, an HMN is developed that employs a colorimetric method to detect changes in the pH, named HMN-pH assay, whose performance has been validated both in vitro and in vivo. Further, multiplexed antibiotic and pH detection is achieved by simultaneously employing the HMN-pH and HMN-Flex on live animals.

A comprehensive approach to continuous quality improvement of massive transfusion by developing key performance indicators.

To develop key performance indicators (KPI) for use in quality assessment of our institutional goal-directed massive transfusion (GDMT). A team comprising our transfusion and emergency medicine departments carried out a cross-sectional data analysis of GDMT in adult patients from January 2021 to December 2022. The study was rooted in the Define, Measure, Analyse, Improve, Control (DMAIC) approach. Features of KPIs were (a) importance, (b) scientific soundness and (c) feasibility. Study parameters were defined and analysed using measures of central tendencies and benchmark comparison. Ninety-two massive transfusion events occurred and 1405 blood components were used. Trauma was the leading cause, followed by postpartum haemorrhage and upper gastrointestinal bleeding. Appropriate GDMT activation was observed only in 43.47% of events. The turnaround time (TAT) was within the benchmark in 85.8% of events with an average of 16 ± 10 min. The average utilization of blood components was 20.5 (interquartile range [IQR] = 11.3) in the appropriate group and 5.5 (IQR = 4.25) in the inappropriate group with a wastage rate of 3.5%. Duration of activation was 6.19 ± 4.59 h, and the adherence to thromboelastography was 66.3%. Overall mortality was 45.65%, and the average duration of hospital stay was 6.1 ± 5.9 days. The KPIs developed were easy to capture, and the analysis provided a comprehensive approach to the quality improvement of the GDMT protocol.

Prosigna Assay for Treatment Decisions in Early Breast Cancer: A Decision Impact Study.

Introduction: Therapeutic decisions in early breast cancer are based on clinico-pathological features which are subject to intra- and inter-observer variability. This single-center decision impact study aimed to evaluate the effects of the Prosigna assay on physicians' adjuvant treatment choices. Methods: Between 09/2017 and 02/2018, formalin-fixed tumor samples from 52 newly diagnosed, postmenopausal, hormone receptor-positive, HER2-negative breast cancer (T1-T2; pN0-N1a) patients were analyzed. Pre-test clinical judgements and Prosigna test results were compared. Results: The mean age was 59 (42-77). Invasive ductal carcinoma (79.2%), grade 2 (52.8%) and T1c-N0 tumors (43.4%) were represented. There was 40.4% discordance between the pre- and post-test risk of recurrences. No significant change was observed in the clinical intermediate risk category, while there was a net reclassification of low-risk patients into a high Prosigna recurrence risk group. In addition, clinically determined intrinsic subtypes were 34.6% discordant with the Prosigna results, which is largely driven by the reclassification of the luminal A tumors into the Prosigna-assessed luminal B group. Before the Prosigna test, endocrine treatment was the primary choice in 20 patients (39.2%), and chemotherapy was recommended to 31 patients (60.8%). Overall, the Prosigna assay led to a change in treatment choice for one patient. Conclusions: Although conventional risk assessment methods are relatively inexpensive with shorter turnaround times, their accuracy and value for risk reduction are suboptimal. According to our results, the Prosigna assay was found to be a relevant tool for the clinical decision making process. Long-term follow-up of these patients will elucidate the potential benefits of using multigene molecular tests as biomarkers for treatment.

Primed and ready: nanopore metabarcoding can now recover highly accurate consensus barcodes that are generally indel-free

BackgroundDNA metabarcoding applies high-throughput sequencing approaches to generate numerous DNA barcodes from mixed sample pools for mass species identification and community characterisation. To date, however, most metabarcoding studies employ second-generation sequencing platforms like Illumina, which are limited by short read lengths and longer turnaround times. While third-generation platforms such as the MinION (Oxford Nanopore Technologies) can sequence longer reads and even in real-time, application of these platforms for metabarcoding has remained limited possibly due to the relatively high read error rates as well as the paucity of specialised software for processing such reads.ResultsWe show that this is no longer the case by performing nanopore-based, cytochrome c oxidase subunit I (COI) metabarcoding on 34 zooplankton bulk samples, and benchmarking the results against conventional Illumina MiSeq sequencing. Nanopore R10.3 sequencing chemistry and super accurate (SUP) basecalling model reduced raw read error rates to ~ 4%, and consensus calling with amplicon_sorter (without further error correction) generated metabarcodes that were ≤ 1% erroneous. Although Illumina recovered a higher number of molecular operational taxonomic units (MOTUs) than nanopore sequencing (589 vs. 471), we found no significant differences in the zooplankton communities inferred between the sequencing platforms. Importantly, 406 of 444 (91.4%) shared MOTUs between Illumina and nanopore were also found to be free of indel errors, and 85% of the zooplankton richness could be recovered after just 12–15 h of sequencing.ConclusionOur results demonstrate that nanopore sequencing can generate metabarcodes with Illumina-like accuracy, and we are the first study to show that nanopore metabarcodes are almost always indel-free. We also show that nanopore metabarcoding is viable for characterising species-rich communities rapidly, and that the same ecological conclusions can be obtained regardless of the sequencing platform used. Collectively, our study inspires confidence in nanopore sequencing and paves the way for greater utilisation of nanopore technology in various metabarcoding applications.

Urine Sample Handling in Laboratory—Process Reengineering is the Need of the Hour

Background and Aims: Monitoring and a continual improvement of turnaround time (TAT) in urine examination and reporting is essential for improved management of laboratory quality as well as ensuring patient satisfaction. TAT can be impacted by multiple factors, some of which appear to be beyond the control of laboratory, especially the preanalytical phase of urine examination such as specimen collection, transportation and sample preparation. The aim of the study was to compare the preanalytical variables like TAT for transportation and incidences of spill between our current urinalysis workflow process and the new urinalysis workflow process using a pneumatic chute-compatible closed urine collection system. Materials and Methods: This observational prospective study was conducted on 4/9/23 from 9 am to 4 pm at the laboratory of Apollo Main Hospital, Chennai. Pneumatic chute compatible closed urine collection cup was given to the patients in Group-A and normal screw cap containers which we are currently using were given to the patients in Group-B. Once the urine samples were collected in the respective containers, they were transported to the laboratory using pneumatic chutes for Group-A samples and manual carry for Group-B samples. Analysis of transportation time using the new collection method (Group-A) when compared with the current method of practice (Group-B) was done. The incidence of spillage was also monitored. Results: A significant improvement was noticed in the transportation process and spill incidence with closed collection devices through the pneumatic chute. Conclusion: To conclude, the implementation of pneumatic chute compatible closed collection tubes for urine improves the transportation and processing time, eliminates the manual carry of urine samples, eliminates the incidences of leakage and spillage, reduces errors and reduces the exposure of urine to healthcare workers, thereby leading to significant improvement in patient care, patient experience and staff satisfaction.

Nanoparticle-Supported, Rapid, and Electronic Detection of SARS-CoV-2 Antibodies and Antigens at Attomolar Level.

A gold nanoparticle (AuNP)-supported rapid, electronic detection (NasRED) platform is demonstrated with high specificity to differentiate SARS-CoV-2 from human coronaviruses while achieving high sensitivity. Uniquely, active fluidic force is exerted on AuNPs in a microcentrifuge tube, through engineered centrifugation and vortex agitation, to accelerate signal transduction from biochemical protein binding to physical AuNP aggregation and precipitation and finally to electronic signals in a customized, stabilized circuitry. The limits of detection are found <80 aM (12 fg/mL) and ∼700 aM (105 fg/mL) to detect antibodies in human pooled serum and 20% diluted whole blood, and <900 aM (45 fg/mL) and 4.1 fM (200 fg/mL) to detect nucleocapsid proteins in human saliva and nasal fluid, respectively. With a small test volume (6 µL), a rapid turnaround time (as fast as <15 min), low reagent cost (estimated ∼$2), and a small system footprint, this novel digital platform can be utilized for accurate diagnosis and screening of a wide range of infectious and chronic diseases.

CHAMP: A Centrifugal Microfluidics-Based CRISPR/Cas12b-Combined Real-Time LAMP One-Pot Method for Mycoplasma pneumoniae Infection Diagnosis.

The Mycoplasma pneumoniae outbreak poses health risks to community residents. However, it still has limitations for current clinical diagnostic methods (qPCR nucleic acid assay or IgM immunoassay), including specialized handling, expensive equipment, prolonged turnaround time, and false positives and negatives, highlighting the need to improve clinical diagnostic methods. Herein, we present a novel centrifugal microfluidics-based method for rapidly diagnosing M. pneumoniae infections (CHAMP system). This user-friendly method combines CRISPR/Cas12b and real-time loop-mediated isothermal amplification (LAMP) in a one-pot reaction, offering high sensitivity, specificity, and simplicity for methodology. By adding fully automated nucleic acid magnetic bead-extracted samples to a prepackaged centrifugal microfluidics chip, 48 samples can be automated tested simultaneously within 15 to 60 min at 60 °C. 427 clinical nasopharyngeal swab specimens were used for validation, demonstrating good positive and negative predictive values and good diagnostic sensitivity, specificity, and significant time savings. This method is particularly suitable for detecting low nucleic acid copies of M. pneumoniae samples.

Microbiological Evaluation of Two Mexican Artisanal Cheeses: Analysis of Foodborne Pathogenic Bacteria in Cotija Cheese and Bola de Ocosingo Cheese by qPCR.

Cotija and Bola de Ocosingo are artisanal ripened cheeses produced in Mexico. Both are made with raw bovine milk from free-grazing cows and with no starter cultures. Unlike culture-based techniques, molecular methods for pathogen detection in food allow a shorter turnaround time, higher detection specificity, and represent a lower microbiological risk for the analyst. In the present investigation, we analyzed 111 cheese samples (95 Cotija and 16 Bola de Ocosingo) by qPCR (TaqMan®) after an enrichment-culture step specific to each foodborne bacterium. The results showed that 100% of the samples were free of DNA from Listeria monocytogenes, Brucella spp., Escherichia coli enterotoxigenic (ETEC), and O157:H7; 9% amplified Salmonella spp. DNA; and 11.7%, Staphylococcus aureus DNA. However, the threshold cycle (Ct) values of the amplified targets ranged between 23 and 30, indicating DNA from non-viable microorganisms. Plate counts supported this assumption. In conclusion, 100% of the cheeses analyzed were safe to consume, and the enrichment step before DNA extraction proved essential to discern between viable and non-viable microorganisms. Hygienic milking, milk handling, cheese manufacturing, and ripening are crucial to achieve an adequate microbiological quality of cheeses made with raw milk.

Prospective Evaluation of Artificial Intelligence Triage of Intracranial Hemorrhage on Noncontrast Head CT Examinations.

Background: Retrospective studies evaluating artificial intelligence (AI) algorithms for intracranial hemorrhage (ICH) detection on noncontrast CT (NCCT) have shown promising results but lack prospective validation. Objective: To evaluate the impact on radiologists' real-world aggregate performance for ICH detection and report turnaround times for ICH-positive examinations of a radiology department's implementation of an AI triage and notification system for ICH detection on head NCCT examinations. Methods: This prospective single-center study included adult patients who underwent head NCCT examinations from May 12, 2021 to June 30, 2021 (phase 1) or September 30, 2021 to December 4, 2021 (phase 2). Before phase 1, the radiology department implemented a commercial AI triage system for ICH detection that processed head NCCT examinations and notified radiologists of positive results through a widget with a floating pop-up display. Examinations were interpreted by neuroradiologists or emergency radiologists, who evaluated examinations without and with AI assistance in phase 1 and phase 2, respectively. A panel of radiologists conducted a review process for all examinations with discordance between the radiology report and AI and a subset of remaining examinations, to establish the reference standard. Diagnostic performance and report turnaround times were compared using Pearson chi-square test and Wilcoxon rank-sum test, respectively. Bonferroni correction was used to account for five diagnostic performance metrics (adjusted significance threshold, .01 [α=.05/5]). Results: A total of 9954 examinations from 7371 patients (mean age, 54.8±19.8 years; 3773 female, 3598 male) were included. In phases 1 and 2, 19.8% (735/3716) and 21.9% (1368/6238) of examinations, respectively, were positive for ICH (P=.01). Radiologists without versus with AI showed no significant difference in accuracy (99.5% vs 99.2%), sensitivity (98.6% vs 98.9%), PPV (99.0% vs 99.7%), or NPV (99.7% vs 99.7%) (all P>.01); specificity was higher for radiologists without than with AI (99.8% vs 99.3%, respectively, P=.004). Mean report turnaround time for ICH-positive examinations was 147.1 minutes without AI versus 149.9 minutes with AI (P=.11). Conclusion: An AI triage system for ICH detection did not improve radiologists' diagnostic performance or report turnaround times. Clinical Impact: This large prospective real-world study does not support use of AI assistance for ICH detection.

Evaluation of Lyme serologic quantitative test indexes: High first-tier test index values predict positive second-tier result in standard and modified 2-tier Lyme testing algorithms.

In this study, we evaluated the potential utility of reporting a quantitative Lyme serologic test index to improve the utility of results from first-tier Lyme assays. Serum from consecutive samples sent to our laboratory for Lyme testing were tested on 2 commercial first-tier Lyme assays and evaluated to determine the probability of second-tier confirmation based on the serologic index value. For both assays, we identified an index value above which 100% of samples confirmed on second-tier testing using both standard and modified 2-tier testing algorithms. Lower rates of confirmation were observed for positive or equivocal samples with lower index values. The use of a Lyme test index value may eliminate the need for confirmatory testing on many positive first-tier samples, providing more rapid turnaround time to a definitive result. This practice would also increase efficiency in the clinical laboratory.

Task Scheduling Optimization in the Cloud Using Improved Heuristic Algorithm

Cloud Computing has become the most efficient and reliable technology in today’s era. Almost every organization and individual depend upon this technology to perform their task and even for storage purpose. As the number of users is growing, the complexity of this technology has also increased massively. Thus, for reliable and efficient use of cloud technology, the tasks, infrastructures, and load must be balanced in the system. Among different methods, one of the ways to efficiently manage the complexity of the system is task scheduling. Task scheduling helps to optimize CPU utilization and make the tasks done with minimum loss. Also, there are many task-scheduling algorithms which have been proposed and implemented to date. Every algorithm has its pros and cons too. Thus, this project aims to implement the proposed improved heuristic (B-Sufferage) Algorithm to schedule tasks in a cloud environment and compare the result with the existing PSO and Min-Min Task Scheduling Algorithm. The B-Sufferage Algorithm depends upon the sufferage value to schedule a particular task on a particular VM. The required infrastructure has been set up using CloudSim 3.0.3 and the implementation has been carried out by configuring respective algorithms. As a result, it has been found that the B-Sufferage algorithm in task scheduling works better than the existing one. Thus, the result has been compared based on metrics like makespan, resource utilization, turn-around time, and waiting time where there is a significant difference for scheduling tasks using this B-Sufferage; an improved heuristic algorithm.

Molecular identification of Malassezia from the skin scrapings of clinically suspected cases of pityriasis versicolor.

Pityriasis versicolor (PV) is caused by a lipophilic fungus belonging to the genus Malassezia. Potassium hydroxide (KOH) mount is most frequently used for screening of cases and culture is the gold standard. KOH lacks sensitivity, and culture is time-consuming and technically demanding. A cross-sectional study was conducted at a tertiary care teaching institution. We aimed to use multiplex-PCR for faster and accurate identification of Malassezia spp directly from skin scrapings of suspected cases of PV. The study was conducted on suspected cases of PV over a period of 12 months. The clinical and demographic details were recorded. The skin scrapings were subjected to KOH mount and cultured on Sabouraud's dextrose agar with an olive oil overlay. Multiplex-PCR targeting 11 Malassezia spp was performed on DNA extracted from skin scrapings. A total of 69 suspected cases of PV were studied. Most patients belonged to metro cities and worked in hot and humid climates. The mean duration of lesions was 18 months, and most had macular and patchy lesions. The sensitivity of KOH and culture was found to be 82.6% and 91.3%, respectively. M. globosa (n = 60, 87%) and M. restricta (n = 3, 4.3%) were isolated in culture. Multiplex PCR detected 85.5% of M. globosa, 5.8% of M. restricta, and 8.7% of mixed infection with M. globosa and M. restricta. M-PCR detected Malassezia in all the samples. M-PCR could identify Malassezia species directly from skin specimens, eliminating the need for culture. M-PCR was accurate, dependable, and exhibited a rapid turnaround time.

Decoding the hidden realm: Molecular pioneering unravelling osteoarticular tuberculosis diagnosis

Osteoarticular tuberculosis (TB), a form of extrapulmonary tuberculosis, refers to the involvement of Mycobacterium tuberculosis (M.tb) in the bones and joints. While pulmonary tuberculosis is the most common form, osteoarticular TB represents a relatively rare but significant manifestation, accounting for approximately 1–3% of all TB cases. Accurate and timely diagnosis plays a pivotal role in the management of osteoarticular TB. Conventional diagnostic methods for osteoarticular TB, such as acid-fast bacilli smear microscopy and culture, have limitations in terms of sensitivity, specificity, and turnaround time. The purpose of this review is to comprehensively evaluate and synthesize the existing literature on molecular pioneering in osteoarticular TB diagnosis. Molecular techniques, such as nucleic acid amplification tests and gene-based assays, have emerged as promising tools for diagnosing TB. These techniques target specific genetic sequences of M.tb, enabling rapid and sensitive detection of the pathogen. However, the diagnostic accuracy, advantages, and limitations of these molecular techniques in the context of osteoarticular TB diagnosis require further investigation and consolidation of evidence. Furthermore, this review aims to identify areas for future research and development in the field of molecular diagnostics for osteoarticular TB.

Real-world assessment of comprehensive genome profiling impact on clinical outcomes: A single-institution study in Japan.

Comprehensive genome profiling (CGP) has revolutionized healthcare by offering personalized medicine opportunities. However, its real-world utility and impact remain incompletely understood. This study examined the extent to which CGP leads to genomically matched therapy and its effectiveness. We analyzed data from advanced solid tumor patients who underwent CGP panel between December 2019 and May 2023 at the Osaka International Cancer Institute. Patient demographics, specimen details, and expert panel assessments were collected. Turnaround time (TAT) and genomically matched therapy outcomes were analyzed. Gene alterations and their co-occurrence patterns were also assessed. Among 1437 patients, 1096 results were available for analysis. The median TAT was 63 [28-182] days. There were 667 (60.9%) cases wherein recommended clinical trials were presented and there were 12 (1.1%) cases that could be enrolled in the trial and 25 (2.3%) cases that could lead to therapies under insurance reimbursement. The median progression free survival of the trial treatment was 1.58 months (95% CI: 0.66-4.37) in clinical trials and 3.66 months (95% CI: 2.14-7.13) in treatment under insurance. Pathologic germline variants were confirmed in 15 patients (1.3%). Co-alteration of CDKN2A, CDKN2B, and MTAP was significantly observed in overall population. The effectiveness of the genomically matched therapy based on the CGP panel was unsatisfactory. Expansion of clinical trials and utilization of remote clinical trials are required to ensure that the results of the CGP panel can be fully returned to patients.

Ouroboros: cross-linking protein expression perturbations and cancer histology imaging with generative-predictive modeling.

Imagine if we could simultaneously predict spatial protein expression in tissues from their routine Hematoxylin and Eosin (H&E) stained images, and create tissue images given protein expression profiles thus enabling virtual simulations of how protein expression alterations impact histology in complex diseases like cancer. Such an approach could lead to more informed diagnostic and therapeutic decisions for precision medicine at lower costs and shorter turnaround times, more detailed insights into underlying disease pathology as well as improvement in predictive and generative performance. In this study, we investigate the intricate correlation between protein expressions obtained from Hyperion mass cytometry and histopathological microstructures in conventional H&E stained glioblastoma (GBM) samples, unveiling morphological patterns and cellular-level spatial alterations associated with protein expression changes. To model these complex relationships, we propose a novel generative-predictive framework called Ouroboros for producing H&E images from protein expressions and simultaneously predicting protein expressions from H&E images. Our comprehensive sample-independent validation over 9920 tissue spots from 4 GBM samples encompassing visual image analysis, quantitative analysis, subspace alignment and perturbation experiments shows that the proposed generative-predictive approach offers significant improvements in predicting protein expression from images in comparison to baseline methods as well as accurate generation of virtual GBM sample images. This proof of concept study can contribute to advancing our understanding of histological responses to protein expression perturbations and lays the foundations for further developments in this area. Implementation and associated data for the proposed approach are available at the URL: https://github.com/Srijay/Ouroboros.

A Systematic Review of Real-Time Deep Learning Methods for Image-Based Cancer Diagnostics.

Deep Learning (DL) drives academics to create models for cancer diagnosis using medical image processing because of its innate ability to recognize difficult-to-detect patterns in complex, noisy, and massive data. The use of deep learning algorithms for real-time cancer diagnosis is explored in depth in this work. Real-time medical diagnosis determines the illness or condition that accounts for a patient's symptoms and outward physical manifestations within a predetermined time frame. With a waiting period of anywhere between 5 days and 30 days, there are currently several ways, including screening tests, biopsies, and other prospective methods, that can assist in discovering a problem, particularly cancer. This article conducts a thorough literature review to understand how DL affects the length of this waiting period. In addition, the accuracy and turnaround time of different imaging modalities is evaluated with DL-based cancer diagnosis. Convolutional neural networks are critical for real-time cancer diagnosis, with models achieving up to 99.3% accuracy. The effectiveness and cost of the infrastructure required for real-time image-based medical diagnostics are evaluated. According to the report, generalization problems, data variability, and explainable DL are some of the most significant barriers to using DL in clinical trials. Making DL applicable for cancer diagnosis will be made possible by explainable DL.

New methods to detect bacterial or viral infections in patients with chronic obstructive pulmonary disease.

Patients with chronic obstructive pulmonary disease (COPD) are frequently colonized and infected by respiratory pathogens. Identifying these infectious etiologies is critical for understanding the microbial dynamics of COPD and for the appropriate use of antimicrobials during exacerbations. Traditional methods, such as bacterial and viral cultures, have been standard in diagnosing respiratory infections. However, these methods have significant limitations, including lack of sensitivity and prolonged turnaround time. Modern molecular approaches offer rapid, sensitive, and specific detection, though they also come with their own challenges. This review explores and evaluates the clinical utility of the latest advancements in detecting bacterial and viral respiratory infections in COPD, encompassing molecular techniques, biomarkers, and emerging technologies. In the evolving landscape of COPD management, integrating molecular diagnostics and emerging technologies holds great promise. The enhanced sensitivity of molecular techniques has significantly advanced our understanding of the role of microbes in COPD. However, many of these technologies have primarily been developed for pneumonia diagnosis or research applications, and their clinical utility in managing COPD requires further evaluation.

Assessing Diagnostic Performance of Molecular Culture for Neonatal Sepsis: Protocol of the CHAMPIONS Study.

Managing neonatal sepsis is challenging due to nonspecific clinical signs, hematological markers with poor accuracy, and a lengthy turnaround time for the identification of microorganisms. Delaying the initiation of antibiotics in truly infected infants can lead to severe morbidity and mortality. Therefore, decisions regarding empiric antibiotic treatment are risk stratified, which exposes many uninfected infants to antibiotics. This causes gut microbiota perturbation, unnecessary hospital admissions, and the generation of multi-resistant organisms. High-speed diagnostic assays could expedite discontinuation or avert the initiation of antibiotics in uninfected infants. This study will evaluate the diagnostic performance of molecular culture (MC), a rapid broad-range PCR-based bacterial profiling technique, for diagnosing neonatal sepsis in infants below 90 days old. A multi-center prospective observational cohort study will include infants evaluated for early and late-onset sepsis. Routine evaluation for suspected sepsis includes microbiological cultures of blood. Additionally, blood for MC will be collected. For early-onset sepsis, umbilical cord blood may be used alternatively. Primary outcome is the agreement between MC and conventional blood culture results. Secondary outcome is the agreement of both assays with clinical sepsis using four different, commonly used definitions. Faster diagnostic pathways for sepsis may reduce antibiotic exposure time. Broad-range molecular assays may identify pathogens undetectable by conventional methods. Employment of umbilical cord blood samples for early-onset sepsis diagnosis can resolve challenges in collecting adequate blood volume and could further expedite treatment decisions.

Clinical and cost-effectiveness of clopidogrel resistance genotype testing after ischaemic stroke or transient ischaemic attack: a systematic review and economic model.

Stroke or transient ischaemic attack patients are at increased risk of secondary vascular events. Antiplatelet medications, most commonly clopidogrel, are prescribed to reduce this risk. Factors including CYP2C19 genetic variants can hinder clopidogrel metabolism. Laboratory-based or point-of-care tests can detect these variants, enabling targeted treatment. To assess the effectiveness of genetic testing to identify clopidogrel resistance in people with ischaemic stroke or transient ischaemic attack. Specific objectives: Do people tested for clopidogrel resistance, and treated accordingly, have a reduced risk of secondary vascular events? Do people with loss-of-function alleles associated with clopidogrel resistance have a reduced risk of secondary vascular events if treated with alternative interventions compared to clopidogrel? Do people with loss-of-function alleles associated with clopidogrel resistance have an increased risk of secondary vascular events when treated with clopidogrel? What is the accuracy of point-of-care tests for detecting variants associated with clopidogrel resistance? What is the technical performance and cost of CYP2C19 genetic tests? Is genetic testing for clopidogrel resistance cost-effective compared with no testing? Systematic review and economic model. Objective 1: Two studies assessed secondary vascular events in patients tested for loss-of-function alleles and treated accordingly. They found a reduced risk, but confidence intervals were wide (hazard ratio 0.50, 95% confidence interval 0.09 to 2.74 and hazard ratio 0.53, 95% confidence interval 0.24 to 1.18). Objective 2: Seven randomised controlled trials compared clopidogrel with alternative treatment in people with genetic variants. Ticagrelor was associated with a lower risk of secondary vascular events than clopidogrel (summary hazard ratio 0.76, 95% confidence interval 0.65 to 0.90; two studies). Objective 3: Twenty-five studies compared outcomes in people with and without genetic variants treated with clopidogrel. People with genetic variants were at an increased risk of secondary vascular events (hazard ratio 1.72, 95% confidence interval 1.43 to 2.08; 18 studies). There was no difference in bleeding risk (hazard ratio 0.98, 95% confidence interval 0.68 to 1.40; five studies). Objective 4: Eleven studies evaluated Genomadix Cube accuracy; no studies evaluated Genedrive. Summary sensitivity and specificity against laboratory reference standards were both 100% (95% confidence interval 94% to 100% and 99% to 100%). Objective 5: Seventeen studies evaluated technical performance of point-of-care tests. Test failure rate ranged from 0.4% to 19% for Genomadix Cube. A survey of 8/10 genomic laboratory hubs revealed variation in preferred technologies for testing, and cost per test ranging from £15 to £250. Most laboratories expected test failure rate to be < 1%. Additional resources could enhance testing capacity and expedite turnaround times. Objective 6: Laboratory and point-of-care CYP2C19 testing strategies were cost-saving and increase quality-adjusted life-years compared with no testing. Both strategies gave similar costs, quality-adjusted life-years and expected net monetary benefit. Our results suggest that CYP2C19 testing followed by tailored treatment is likely to be effective and cost-effective in both populations. Accuracy and technical performance of Genedrive. Test failure rate of Genomadix Cube in a NationalHealthServicesetting. Value of testing additional loss-of-function alleles. Appropriateness of treatment dichotomy based on loss-of-function alleles. Lack of data on Genedrive. No randomised 'test-and-treat' studies of dipyramidole plus aspirin. This study is registered as PROSPERO CRD42022357661. This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135620) and is published in full in Health Technology Assessment; Vol. 28, No. 57. See the NIHR Funding and Awards website for further award information.