Search Results (72)

Prenatal whole-genome sequencing (WGS) is a comprehensive genetic test for fetal anomalies, enabling simultaneous detection of aneuploidies, copy number variants (CNVs), single-nucleotide variants (SNVs), small insertions/deletions, structural variants, and regions of absence of heterozygosity. However, its clinical performance, optimal sequencing strategies, and implementation challenges remain incompletely defined. We conducted a narrative review of PubMed-indexed studies (1966–December 2025) evaluating prenatal WGS in fetuses with structural anomalies. Across 29 studies, diagnostic yield ranged from approximately 20% to 40%, influenced by phenotype complexity, sequencing depth, and study design. Low-coverage WGS (≤5×) reliably detected large chromosomal abnormalities with a performance comparable to chromosomal microarray analysis. Moderate-coverage WGS (20–40×) additionally enabled detection of SNVs and structural variants, providing up to 30% incremental diagnostic yield after uninformative standard testing. Turnaround times were typically 14–21 days. Higher sequencing depth increases detection of variants of uncertain significance (0.6% to 35.7%) and secondary/incidental findings (1.6–30.8%). Prenatal WGS offers meaningful diagnostic value but requires careful patient selection, multidisciplinary expertise, and structured pre- and post-test genetic counseling to ensure responsible integration into routine clinical practice, with careful consideration of clinical benefit and economic feasibility. Full article

Background: Drug-resistant tuberculosis remains a major challenge in resource-limited settings, particularly in rural regions of the Eastern Cape Province, where limited laboratory infrastructure, constrained access to advanced molecular diagnostics, shortages of specialized healthcare personnel, and prolonged diagnostic turnaround times can delay appropriate treatment initiation. This study examined whether routinely detectable genomic resistance markers could be integrated with parsimonious machine learning approaches to support early risk stratification for isoniazid (INH) and/or rifampicin (RIF) resistance and multidrug-resistant tuberculosis (MDR-TB). Methods: We conducted a retrospective analysis of clinical, demographic, and genomic data from 207 Mycobacterium tuberculosis isolates representing 207 unique patients. Resistance was classified as INH and/or RIF resistance or MDR-TB (concurrent resistance to both drugs). Predictors included age, sex, and canonical resistance-associated mutations (katG S315T, inhA −15C>T, and rpoB codon substitutions). Logistic regression was used to estimate adjusted odds ratios (aORs), while Random Forest models were applied to assess non-linear feature importance. Internal validation was performed using 10-fold cross-validation. A systems network analysis mapped the integration of model-derived risk bands into Clinical Governance structures and Community-Engaged Education pathways, including interventions delivered by Community Health Workers (CHWs). Results: INH and/or RIF resistance was identified in 58.9% of isolates, with 21.7% classified as MDR-TB. The most frequently detected mutations were katG S315T (29.0%) and rpoB S450L (26.6%). Logistic regression identified rpoB S450L (aOR 4.20; 95% CI: 2.10–8.45) and katG S315T (aOR 2.85; 95% CI: 1.40–5.80) as the strongest independent predictors, while age and sex were not statistically significant. Models demonstrated strong internal discrimination (AUCs of 0.96 for INH and/or RIF resistance and 0.99 for MDR-TB). Risk stratification categorized 18% of patients as high risk. Scenario-based modelling suggested that prioritizing high-risk patients for reflex Line Probe Assay testing could reduce the median time to appropriate treatment from 14 to 3 days and may reduce progression from isoniazid-resistant TB to MDR-TB under specified operational assumptions. Conclusions: Mutation-informed predictive modelling demonstrates strong internally validated discrimination and provides a structured framework for risk-stratified intervention. Integrating probability-based risk thresholds within Clinical Governance systems and community-level support structures, including CHW-led adherence and education strategies, may support earlier treatment optimization in high-burden rural settings. External validation and prospective implementation studies are required before broader programmatic adoption. Full article

Fascioliasis, a globally prevalent zoonosis, severely threatens public health and livestock security. Current diagnostic approaches, hindered by the need for sophisticated instrumentation and specialized expertise, are inadequate for on-site surveillance in resource-constrained settings. This study developed a rapid, visual detection assay for Fasciola hepatica via recombinase-aided amplification (RAA) integrated with CRISPR/Cas12b, addressing critical equipment and operational constraints. Targeting a specific mitochondrial DNA fragment of F. hepatica, recombinant plasmid standards were constructed, RAA primers and sgRNA optimized, and three detection modalities (real-time fluorescence, UV lamp, test strip) integrated. Clinical validation against PCR demonstrated 45 min turnaround time, F. hepatica-specific positivity, and real-time fluorescence sensitivity of 2.6 copies/μL. Results showed high concordance with PCR and qPCR, with substantially reduced assay duration and streamlined workflow. This highly sensitive, specific, multi-visualized method overcomes limitations of conventional techniques, offering an efficient, field-deployable tool for fascioliasis surveillance and control in grassroots and pastoral regions. Full article

The design day flight schedule (DDFS) plays a pivotal role in airport simulation and infrastructure planning. Despite its importance, previous studies and global guidelines offer only broad recommendations for DDFS preparation, lacking detailed methodologies and empirical validation. This study proposes a systematic, data-driven approach for generating a future DDFS that accounts for projected demand, airline behavior, and regional traffic characteristics. Leveraging historical flight operation data and probabilistic distributions, the proposed method captures existing patterns and anticipated market changes comprehensively. To realistically define each flight’s operational characteristics, a structured 10-step procedure is employed to generate and assign attributes—such as aircraft type, origin/destination airport, and turnaround time—based on empirical patterns and logical constraints. The proposed approach is applied to Incheon International Airport as a case study, demonstrating its practical utility and scalability. The generated DDFSs are shown to be consistent with target-year forecasts in terms of peak-hour operations and fleet composition, with deviations remaining within a small error range. Additional validation confirms that key operational characteristics, including airline shares, connection patterns, and turnaround times, are reproduced with acceptable accuracy. By bridging the gap between high-level guidance and implementable practice, this study contributes a replicable framework for future DDFS generation and provides actionable insights for airport planners aiming to better anticipate operational demands. Full article

Loop-mediated isothermal amplification (LAMP) is an innovative nucleic acid amplification technique that operates under isothermal conditions and is distinguished by its high analytical efficiency, cost-effectiveness, and operational simplicity. Unlike conventional molecular assays, LAMP does not require sophisticated instrumentation or highly specialized personnel, rendering it particularly suitable for diagnostic deployment in resource-limited settings. Reaction outcomes are typically determined through direct visual inspection, often via colorimetric readouts, further enhancing its applicability in decentralized and point-of-care contexts. Owing to these attributes, LAMP has emerged as a valuable tool for the diagnosis of infectious diseases, particularly in regions with constrained laboratory infrastructure. Its affordability, rapid turnaround time, and ease of implementation support large-scale testing during public health emergencies, including epidemics and outbreaks, thereby contributing to the reduction in disease burden. Timely and accurate pathogen detection using LAMP can substantially strengthen public health responses aimed at controlling and mitigating viral transmission. This review provides an overview of the LAMP methodology, with an emphasis on its application in the detection of viral pathogens with epidemic and pandemic potential. Dengue virus and influenza virus are discussed as representative model infections to illustrate the diagnostic performance and practical advantages of LAMP-based assays. In addition, we explore current challenges and future perspectives for the implementation of LAMP in resource-limited settings, highlighting the need for continued technological refinement and contextual adaptation to maximize its impact on global health initiatives. Full article

Ambulance offload delay (AOD) occurs when incoming ambulance patients cannot be transferred promptly from paramedics to emergency department (ED) staff, usually due to ED and hospital congestion. This study empirically examines how AOD affects ambulance system efficiency in Nova Scotia, Canada. Using 12 months of call data from an integrated provincial EMS system and the electronic patient care reporting system, the analysis quantifies AOD impacts on the number of ambulances at EDs, turnaround time, total call time, response time, and ambulance availability across all regions. Findings show that AOD in the Central Region negatively affects all performance measures locally and in adjacent regions, prolonging turnaround and total call times, lengthening response times, and reducing ambulance availability where resources are shared. These results highlight the scale of AOD’s system-wide impact and provide a generalizable methodological framework that other EMS operators can adapt to assess and manage AOD in their specific operational contexts, recognizing that region-specific factors significantly influence outcomes. Full article

Background/Objectives: Chest radiography remains a fundamental diagnostic tool for evaluating thoracic disease, yet its interpretation requires considerable time and specialized expertise. Worldwide shortages of trained radiologists can lead to lengthy turnaround times and delayed treatment. This study introduces the Multi-label Chest Abnormality Detection System (MCADS), a deep-learning-driven platform designed to automatically identify and interpret 18 distinct radiographic abnormalities to address these diagnostic challenges. Methods: MCADS integrates a pre-trained DenseNet121 convolutional neural network (via TorchXRayVision) to balance broad pathology coverage with rapid inference. Images are processed asynchronously on a central server to avoid the interruption of clinical workflows. To enhance transparency and clinician confidence, the system employs Gradient-weighted Class Activation Mapping (Grad-CAM) to overlay heatmaps pinpointing image regions most influential to each predicted abnormality. The system was evaluated using eight large, publicly available datasets. Results: When evaluated on diverse datasets, MCADS achieved high area-under-the-curve performance metrics across all 18 target conditions. The platform consistently produced accurate, multi-condition analyses in under thirty seconds per image, demonstrating both reliability and speed suitable for clinical environments. Conclusions: MCADS demonstrates the potential to accelerate chest X-ray interpretation by delivering fast, reliable, and explainable multi-abnormality screening. Its deployment could reduce radiologist workload and mitigate diagnostic delays, offering a pathway to improve patient care within data-driven healthcare environments. Full article

Background/Objectives: Acute leukemia, the most common childhood cancer, poses a significant public health challenge in low- and middle-income countries (LMICs) due to its high incidence and mortality rates. Survival rates in these regions are often lower, primarily due to delayed and inaccurate diagnoses, limited access to treatment, therapy abandonment, therapy-related toxicity, and inadequate healthcare infrastructure. In Mexico, a new initiative called OncoCREAN has been developed to address this urgent need by establishing local treatment centers near pediatric patients’ home cities, ensuring timely cancer detection and comprehensive disease treatment. Methods: A retrospective observational study was conducted on pediatric patients treated at the Mexican Social Security Institute (IMSS) between 18 May 2022 and 30 June 2025. Patients presenting clinical suspicion of acute leukemia were referred to OncoCREAN centers for sample collection and subsequent shipment to the Oncoimmunology and Cytomics Laboratory (OCL), where immunophenotyping confirmed the diagnoses. Results: The implementation of the OncoCREAN model significantly reduced diagnostic turnaround times, facilitating timely therapeutic decisions, minimized uncertainty, and optimized clinical management. The decentralized framework demonstrated feasibility across diverse geographic regions, ensuring access to advanced diagnostic technology for vulnerable populations and generating valuable data on disease incidence and molecular profiles. Conclusions: The OncoCREAN model highlights the critical importance of decentralizing high-technology diagnostic resources in modern pediatric oncology. This new approach to translational research that is accessible, inclusive, and relevant to society creates a paradigm shift in the management of childhood cancer and other diseases. Full article

Workers’ exposure to silica dust is a global occupational and public health concern and is particularly prevalent in Southern Africa, mainly because of inadequate dust control measures. It is worsened by the high prevalence of HIV/AIDS, which exacerbates tuberculosis and other occupational lung diseases. The prevalence of silicosis in the region ranges from 9 to 51%; however, silica dust exposure levels and controls, especially in the informal mining sector, particularly in artisanal small-scale mines (ASMs), leave much to be desired. This is important because silicosis is incurable and can only be eliminated by preventing worker exposure. Additionally, several studies have indicated inadequate occupational health and safety policies, weak inspection systems, inadequate monitoring and control technologies, and inadequate occupational health and hygiene skills. Furthermore, there is a near-absence of silica dust analysis laboratories in southern Africa, except in South Africa. This protocol aims to systematically evaluate the effectiveness of respirable dust and respirable crystalline silica dust exposure evaluation and control methodology for the mining industry. The study will entail testing the effectiveness of current dust control measures for controlling microscale particles using various exposure dose metrics, such as mass, number, and lung surface area concentrations. This will be achieved using a portable Fourier transform infrared spectroscope (FTIR) (Nanozen Industries Inc., Burnaby, BC, Canada), the Nanozen DustCount, which measures both the mass and particle size distribution. The surface area concentration will be analysed by inputting the particle size distribution (PSD) results into the Multiple-Path Particle Dosimetry Model (MPPD) to estimate the retained and cleared doses. The MPPD will help us understand the sub-micron dust deposition and the reduction rate using the controls. To the best of our knowledge, the proposed approach has never been used elsewhere or in our settings. The proposed approach will reduce dependence on highly skilled individuals, reduce the turnaround sampling and analysis time, and provide a reference for regional harmonised occupational exposure limit (OEL) guidelines as a guiding document on how to meet occupational health, safety and environment (OHSE) requirements in ASM settings. Therefore, the outcome of this study will influence policy reforms and protect hundreds of thousands of employees currently working without any form of exposure prevention or protection. Full article

Background/Objectives: Gastroenteritis remains a major global health concern, particularly in resource-limited regions, where rapid and accurate diagnosis is crucial for effective patient management. Syndromic multiplex PCR panels, such as the FilmArray gastrointestinal (FAGI) panel, offer the potential to significantly improve diagnostic yield and turnaround time, enabling more targeted treatments and reducing unnecessary antibiotic use. However, real-world data on their performance in low-resource settings remains scarce. This study evaluates the performance, clinical impact, and cost-effectiveness of the FAGI panel compared to standard of care (SOC) diagnostic methods in gastroenteritis cases at São José Hospital for Infectious Diseases in Fortaleza, Brazil, an HIV Reference Center, in a resource-limited region of a middle-income country. Methods: A retrospective observational study was conducted among patients tested with FAGI (n = 161) and a retrospective control group tested only with SOC methods (n = 166). Results: The FAGI panel was associated with a significant reduction in the turnaround time, antimicrobial use, and total treatment costs while increasing the pathogen detection rate. Specifically, the median diagnostic time was reduced by 18%, with an increase in pathogen detection compared to SOC methods (64% positivity compared to 32%). Moreover, the FAGI group experienced a 30% reduction in antibiotic use, with a corresponding 83% reduction in antimicrobial costs. Conclusions: These results suggest that the FilmArray panel may offer substantial benefits in terms of efficiency and cost savings, highlighting its potential for broader implementation in clinical practice, especially in resource-limited settings, to improve patient outcomes in infectious disease management. Full article

Dengue fever remains a major mosquito–borne disease worldwide, with over 400 million infections annually and a high risk of severe complications such as dengue hemorrhagic fever. The disease is prevalent in tropical and subtropical regions, where population density and limited vector control accelerate transmission, making early and reliable diagnosis essential for outbreak prevention and disease management. Conventional diagnostic methods, including virus isolation, reverse transcription polymerase chain reaction (RT–PCR), enzyme–linked immunosorbent assays (ELISA), and serological testing, are accurate but often constrained by high cost, labor–intensive procedures, centralized laboratory requirements, and delayed turnaround times. This review examines current dengue diagnostic technologies by outlining their working principles, performance characteristics, and practical limitations, with emphasis on key target analytes such as viral RNA; nonstructural protein 1 (NS1), including DENV–2 NS1; and host antibodies. Diagnostic approaches across commonly used biofluids, including whole blood, serum, plasma, and urine, are discussed. Recent advances in biosensing technologies are reviewed, including optical, electrochemical, microwave, microfluidic, and CRISPR–based platforms, along with the integration of artificial intelligence for data analysis and diagnostic enhancement. Overall, this review highlights the need for accurate, scalable, and field–deployable diagnostic solutions to support early dengue detection and reduce the global disease burden. Full article

Background/Objectives: Conventional next-generation sequencing (NGS) workflows often require more than two weeks to complete, delaying treatment decisions and limiting access to precision oncology in community settings. This report aimed to demonstrate the feasibility of performing rapid, comprehensive cell-free DNA (cfDNA)-based genomic profiling by introducing a fully automated NGS workflow in a community hospital environment. Case Presentation: A postoperative patient with pancreatic ductal adenocarcinoma and liver metastasis underwent cfDNA-based liquid biopsy using plasma collected in PAXgene^® Blood ccfDNA Tubes. Gene analysis was performed using the Oncomine Precision Assay GX5 on the Ion Torrent Genexus™ System (Thermo Fisher Scientific). Three pathogenic hotspot mutations—KRAS G12R, TP53 M246I/M246K, and GNA11—and one copy number gain in PIK3CA were identified, whereas no variants were detected in a healthy volunteer control. The total turnaround time from plasma separation to report generation was approximately 27 h, requiring only 40 min of total hands-on time. Discussion: This rapid, automated workflow enabled comprehensive cfDNA analysis within a clinically practical timeframe, overcoming key limitations of conventional multi-step NGS workflows that typically require external sample shipment and specialized personnel. The results confirm the technical feasibility of conducting high-quality molecular testing in a regional hospital setting. Conclusions: This report demonstrates that fully automated cfDNA-based NGS can achieve clinically meaningful genomic profiling within 27 h in a community hospital. This advancement addresses the time and cost barriers of traditional NGS analysis and represents a significant step toward promoting precision medicine in community healthcare. Full article

African swine fever is a highly contagious and deadly disease of both domestic and wild pigs. In developing countries such as Ghana, the diagnosis and control of ASF are very challenging. In this paper, we discussed factors that account for ASF endemicity in many developing nations, with special focus on Ghana. We identified possible ASF spread via pig value chain through the transportation of live pigs across regions in Ghana. Major factors contributing to ASF spread in Ghana include lack of farmer compensation during ASF outbreaks, free range system of pig farming, porous country borders, lack of rapid on-site diagnostic test kits, unsafe sample collection and transportation to diagnostic laboratories, long diagnostic test turnaround, and improper carcass disposal. We also discuss available diagnostic options for ASF and their limitations. We propose a more holistic approach to ASF control in Ghana. These measures include applying a muti-sectoral approach, rehabilitation of inactive regional laboratories and expansion of services to six newly established regions, promoting point of care testing and developing and implementing farmer compensation plan during outbreaks. These proposed ASF control measures will provide field veterinarians with effective means to make informed decisions while awaiting laboratory confirmation of outbreaks. Full article

This study developed a rapid and reliable SYBR-Green semiplex PCR assay for simulta-neous detection of major carbapenem resistance genes in Klebsiella pneumoniae and Acinetobacter baumannii. Two primer sets were used: one to detect blaKPC, blaNDM-1, and blaOXA-48 genes in K. pneumoniae and blaOXA-23 in A. baumannii, and another to amplify conserved 16S rRNA gene regions as internal controls. The intra- and inter-assay coeffi-cient of variation ranged from 0.03% to 3.8%. Standard curves exhibited excellent linearity across six logarithmic scales (10¹–10⁶ DNA copies/µL), with detection limits of 10–10² DNA copies/mL. Melting temperatures (Tm) were: 88.85 °C (KPIC), 90.65 °C (NDM-1), 89.45 °C (KPC), 84.23 °C (OXA-48), 87.81 °C (OXA-23), and 80.67 °C (ABIC). The SYBR-Green Semiplex PCR assay offers a rapid (65 min turnaround), cost-effective, and sensitive method for early detection of carbapenem-resistant pathogens, enabling timely targeted therapy and improved infection control by potentially reducing empirical antibiotic use before culture confirmation. Full article

Background/Objectives: Monitoring community health and tracking SARS-CoV-2 evolution were critical priorities throughout the COVID-19 pandemic. However, widespread shortages of personal protective equipment, the necessity for social distancing, and the redeployment of healthcare personnel to clinical duties presented significant barriers to traditional sample collection. Methods: In this study, we evaluated the feasibility of using self-collected saliva specimens for the qualitative detection of SARS-CoV-2 infection. Following confirmation of reliable viral detection in saliva, we established a large-scale surveillance program in Arizona, USA, to enable clinical diagnosis and genomic sequencing from self-collected samples. Between April 2020 and December 2023, we tested approximately 1.4 million saliva samples using RT-PCR, identifying 94,330 SARS-CoV-2 infections. Whole genome sequencing was performed on 69,595 samples, yielding 54,040 high-quality consensus genomes. Results: This surveillance approach enabled real-time monitoring of general infection trends that matched regional case counts. We monitored multiple wave-like introductions of viral lineages over the course of the pandemic. We identified three periods of S gene target failure on a commercial assay and assessed its ability to make fast, genotyping assignment during the pandemic (PPV = 0.98, 95% CI = 0.97–0.99; NPV = 0.94, 95% CI = 0.94–0.96). The co-location of clinical testing and sequencing capabilities within the same facility resulted in low turnaround time from the sample collection to the generation of sequencing data (median = 12 days, IQR: 9.0–19.75). Conclusions: Our findings support the use of self-collected saliva as a scalable, cost-effective, and practical strategy for infectious disease surveillance in future pandemics. Full article