Introduction The rise of drug-resistant tuberculosis poses a significant challenge in patient management. Epidemiological cut-off values define drug resistance in Mycobacterium tuberculosis . In our previous study, we reported deviations from the WHO-recommended epidemiological cut-off values and identified subtherapeutic concentrations of rifampicin in clinical Mycobacterium tuberculosis isolates. Building on these findings, the present study systematically evaluated the epidemiological cut-off values and pharmacodynamic profiles of newer and repurposed second-line anti-TB drugs - Bedaquiline, Delamanid, Moxifloxacin, Linezolid, Clofazimine, Levofloxacin, and Pretomanid against the first-line drug-sensitive and isolates that are resistant to Rifampicin and Isoniazid from tuberculosis patients in and around Chennai. Methods The Broth microdilution-based Microscopic Observation Drug Susceptibility assay was employed to determine the minimum inhibitory concentration of the drugs against well-characterized wild-type and drug-resistant clinical Mycobacterium tuberculosis clinical isolates. The resulting MIC profiles were subsequently analyzed to delineate pharmacodynamic relationships underlying therapeutic efficacy and resistance development. Results and discussion Deviations from the World Health Organization–recommended epidemiological cut-off values were observed, with lower thresholds for delamanid and levofloxacin and higher concentrations for clofazimine and bedaquiline. These shifts indicate region-specific susceptibility patterns in Mycobacterium tuberculosis that have direct implications for the effective treatment of multidrug-resistant tuberculosis. Inaccurate cut-off values may lead to misclassification of resistance, inappropriate regimen selection, and exposure to suboptimal drug concentrations, thereby compromising treatment efficacy and amplifying the risk of acquired resistance. Concordantly, pharmacodynamic analyses revealed sub-therapeutic exposure ranges for several newer and repurposed anti-TB drugs, underscoring the potential for treatment failure even in strains classified as susceptible. Collectively, these findings highlight the urgent need for regionally calibrated epidemiological cut-off values to optimize drug dosing, improve MDR-TB treatment outcomes, and strengthen resistance surveillance frameworks.

Escherichia coli is a ubiquitous bacterium that acts both as a commensal and a pathogen, serving as a major reservoir of antimicrobial resistance genes (ARGs). The global spread of ARGs, particularly those conferring resistance to last-resort antibiotics such as carbapenems and colistin, poses a serious threat to human and animal health. In this study, we analyzed 9,696 publicly available E. coli genomes from human, animal, and unknown-origin metadata, identifying 42,813 acquired ARG occurrences across ten antibiotic classes. Multidrug resistance (ARGs from ≥ 3 classes) was observed in 44.7% of genomes, with aminoglycoside-modifying enzymes, multidrug efflux pumps, and β-lactamases being the most abundant. Clinically important genes, including blaTEM-1B, blaCTX-M-15, blaNDM-5, and mcr-1.1, were widely prevalent across hosts. Using binary presence/absence profiles, co-occurrence analyses revealed structured resistance gene modules, with strong associations among aminoglycoside, sulfonamide, and trimethoprim genes consistent with integron-associated assemblages. In contrast, major β-lactamase genes and efflux determinants showed weaker and more heterogeneous co-occurrence patterns, suggesting frequent independent acquisition. Geographic and host analyses highlighted significant heterogeneity and extensive ARG sharing between humans and animals. This study provides a global-scale overview of the E. coli resistome across human and animal hosts, offering a reference framework for genomic surveillance and informing strategies to curb the spread of multidrug-resistant lineages.

Longitudinal wastewater metagenomics reveals distinct environmental and anthropogenic associations with resistance, virulence, and viral communities.

Background Cervical intraepithelial neoplasia (CIN) recurrence after loop electrosurgical excision procedure (LEEP) remains a clinically consequential barrier to cervical cancer prevention, and risk stratification tools tailored to real-world practice are limited in China. This study developed and internally validated a clinical prediction nomogram for histologically confirmed CIN2+ recurrence after LEEP. Methods A retrospective single-center cohort was assembled of women treated with LEEP for CIN2+ between January 2018 and October 2024. Candidate predictors included demographic and reproductive factors, smoking, HPV vaccination, prior cervical treatment, transformation zone type, LEEP pathology (including adenocarcinoma in situ [AIS] and margin status), pre-/post-treatment high-risk HPV measures, and neutrophil-to-lymphocyte ratio (NLR). Time-to-recurrence was analyzed using Cox regression with hierarchical domain modeling. A nomogram was constructed from the final multivariable model and evaluated for discrimination and calibration. Results Among 2,230 women (median follow-up 31.8 months, IQR 19.6–43.5), 334 developed CIN2+ recurrence (15.0%), with a median time to recurrence of 15.6 months (IQR 8.2–24.3). Persistent HPV infection occurred in 50.6% of women with recurrence versus 23.1% without recurrence ( p < 0.001). Persistent HPV infection (same genotype pre-/post-LEEP) was the strongest independent predictor (adjusted hazard ratio [aHR] 2.51, 95% CI 1.99–3.16). Additional independent predictors included unvaccinated status (aHR 1.54, 95% CI 1.08–2.20), multiple positive margins (aHR 1.52, 95% CI 1.08–2.14), AIS versus CIN2 (aHR 1.48, 95% CI 1.03–2.12), prior cervical treatment (aHR 1.38, 95% CI 1.04–1.84), single positive margin (aHR 1.38, 95% CI 1.02–1.87), and higher NLR (per one-unit increase: aHR 1.21, 95% CI 1.02–1.44). Model discrimination increased across hierarchical models from 0.516 (model 1) and 0.562 (model 3) to 0.619 in the final model. Risk stratification separated low-, intermediate-, and high-risk groups with observed 24-month recurrence rates of 6.2%, 14.8%, and 31.5%, respectively ( p for trend <0.001). Conclusion In a contemporary Chinese single-center cohort, genotype-defined persistent HPV infection and margin burden were dominant determinants of CIN2+ recurrence after LEEP, with vaccination status and NLR providing additional stratification. The resulting nomogram offers a pragmatic framework for risk-adapted surveillance, pending external multicenter validation.

Trade and containment policies during COVID-19: Disaggregated evidence for adaptive public health governance.

The increasing global burden of antimicrobial resistance (AMR) has been identified as a critical public health crisis, necessitating the development of robust, real-time surveillance frameworks to evaluate AMR dynamics. Sewage surveillance is emerging as a promising tool that utilizes sewage fingerprinting to provide comprehensive and unbiased information on antibiotic resistance genes (ARGs) within human populations. Here, we conducted a large-scale, year-long field surveillance of resistome in the community sewage using both short- and long-read metagenomic sequencing. We examined samples collected from 95 geographically distributed sites across Hong Kong, covering a population of 4.8 million residents, during summer and winter seasons. Our findings revealed distinct seasonal patterns through high-resolution resistome profiling. We found that the resistome structures shifted from the community sewage collected at sewer manholes to the influent of wastewater treatment plants (WWTPs), driven by taxonomic variation. Notably, community sewage exhibited a significantly higher similarity to the resistome of human feces than WWTP influent, which provides insights for selecting suitable sampling sites for epidemiological ARG surveillance. The application of long-read sequencing markedly enhanced our understanding of the phylogenetic diversity of ARG hosts and uncovered a broad spectrum of potentially mobile ARGs with varied genetic backgrounds. Furthermore, we observed multiple local ARG transmission patterns and subsequently evaluated their potential threats to public health based on the gene trees to inform future epidemiological control strategies. Overall, this work expands our current understanding of community sewage for population-level AMR monitoring and establishes a baseline for advancing sewage surveillance efforts to better combat AMR.

Abstract Influenza remains a significant and recurrent public health burden in temperate regions. Meteorological factors such as temperature, humidity, and rainfall are recognised as associated with influenza transmission patterns, exhibiting complex, nonlinear, temporally lagged, and spatially heterogeneous effects. This study employed a Spatial Bayesian Distributed Lag Non-Linear Model (SB-DLNM) to investigate the associations between meteorological factors and influenza incidence across 15 Local Health Districts, New South Wales, Australiathe short-term meteorological variables on influenza incidence across multiple Local Health Districts within New South Wales, Australia. The method incorporates (i) cross-basis functions to model delayed and non-linear meteorological impacts; (ii) a comparative analysis of case-crossover and time-series designs to distinguish monthly-lag associations from broader temporal trends; and (iii) spatial partial pooling to enhance the stability of estimates, particularly in data-sparse regions. Temperature demonstrated the strongest associations with influenza risk (Relative Risk (RR) range: 1.16–3.90), with elevated risks observed predominantly at cold temperature extremes. While exposure-response curves suggest minimum risk at moderate temperatures ( $$18-22^{\circ }\hbox {C}$$ ), the available data primarily capture cold-related effects; warm-temperature associations remain uncertain due to limited extreme heat observations. Humidity showed marked spatial heterogeneity with variable effects across districts (RR range: 1.32–5.69), while rainfall demonstrated minimal associations (RR typically 1.03–1.42). Exceedance probabilities for RR>1 were moderate across all variables, ranging from 17.5% to 58%, with no extreme hot spots observed. Partial pooling effectively stabilised estimates in sparse datasets, improving the robustness of spatial risk assessment. These findings underscore the importance of cold temperatures in influenza transmission patterns, providing a robust framework for public health surveillance. Our use of monthly aggregated data captures population-level seasonal associations rather than acute exposure-infection dynamics, which represents an important interpretive constraint.Among the meteorological variables, temperature emerged as the strongest predictor of influenza risk, with peak incidence observed within moderate temperature ranges ( $$20-22^{\circ }\hbox {C}$$ ) Graphical Abstract A schematic overview of the workflow from merging meteorological and influenza data, evaluating four modelling approaches (with Model 3 highlighted as the best), to generating spatial risk maps and relative risk estimates for influenza in NSW.

A Standardized Statistical Framework for Population Surveillance Using the National Health Interview Survey.

ABSTRACT This study examines extreme downside risk spillovers between the global carbon market and major energy markets and evaluates their predictive value. Using daily data from 2013 to 2024, we estimate tail‐risk dependence with the MVMQ‐CAViaR model and quantify the dynamic transmission of extreme shocks via pseudo‐quantile impulse responses. Our results document strong and asymmetric spillovers between carbon and major energy markets. A bidirectional forecasting framework using Quantile Regression Forests, Quantile Gradient Boosting, and Quantile Regression Neural Networks results in substantial out‐of‐sample gains, confirmed by Diebold–Mariano tests. These findings suggest that regulators and market operators should integrate carbon–energy tail‐risk linkages into early‐warning systems and cross‐market surveillance frameworks, so materially enhance the detection of extreme risk events. The results also highlight the value of adopting machine learning‐based quantile models in policy settings where timely assessment of systemic risk is essential.

Electricity Distribution Companies (DISCOMs) manage extensive and widely distributed power networks, where traditional manual inspections are often slow, labor-intensive, and no longer sufficient for today’s expectations of reliability and efficiency. This work introduces a holistic approach for implementing drone (UAV) technologies combined with Artificial Intelligence (AI) to advance operations, safety, and business performance in power distribution. Employing drones equipped with various sensors such as RGB, thermal, zoom, and LiDAR, it examines essential applications like equipment health checks, monitoring of vegetation and right-of-way, outage assessments, theft prevention, and consumer mapping. An integrated technical solution is outlined, covering the entire process from drone-based data collection and on-the-spot processing to cloud storage, AI-powered fault detection and prioritization, and automated work order creation that connects with DISCOMs’ existing enterprise platforms (GIS, OMS, ERP, MDM). The paper also outlines practical strategies for gradual deployment in Indian utilities, focusing on tangible metrics including reduced outage durations (SAIDI/SAIFI), decreased AT&C losses, and improved crew safety. The findings show that combining drones with AI not only improves inspection efficiency but also transforms them into key tools for predictive maintenance, revenue assurance, and informed decision-making within power distribution.

First probabilistic radiological risk appraisal of Bay of Bengal beach sands: Explicit spatial hot-spot analysis.

Pancreatic adenocarcinoma is characterized by late-stage presentation and high mortality, largely due to the absence of biomarkers that signal disease during its prolonged preclinical phase. The aim of this study was to identify and temporally characterize circulating proteomic biomarkers that undergo systematic change years before clinical diagnosis, and to determine whether these trajectories define discrete pre-diagnostic risk windows that could enable earlier, biologically informed interception. Using longitudinal proteomic data from the UK Biobank, we employed hinge-regression change-point modeling to identify temporal inflection points for circulating proteins. We partitioned the pre-diagnostic period into "Far" (5-10 years) and "Near" (0-5 years) windows to evaluate discriminatory performance. We identified a sequential "relay" of protein trajectories. CTHRC1 serves as a primary early-warning signal with an inflection point 8.93 years prior to diagnosis. This is followed by a secondary rise in RELT at 2 years. The integrated proteomic model achieved an Adjusted R2 of 0.434 and an area under curve (AUC) of 0.814. At an optimal probability threshold of 0.626, the panel distinguished between pre-diagnostic windows with 87.5% precision and 80.0% specificity. Pancreatic cancer is characterized by a predictable, decade-long proteomic countdown. This staged relay model provides a biologically grounded framework for risk-stratified surveillance, extending the window for clinical action far beyond current standards.

ABSTRACT Ensuring people's safety in public places is a significant challenge for administrations today. The importance of automated crowd‐monitoring systems has recently expanded beyond their role in addressing security concerns in densely populated areas. These systems have become increasingly vital for safeguarding human lives by helping to mitigate the spread of lethal infectious viruses, such as H3N2, SARS‐CoV‐2, Influenza, and COVID‐19. Artificial intelligence (AI) has added a new dimension to this effort by addressing novel and real‐world human safety challenges through automated crowd‐monitoring frameworks. The proposed AI framework for crowd surveillance (AIFCS) employs a deep C2DN network to count people and issue warning signals for images exceeding a specified crowd threshold. Four datasets, including three publicly available ones (Mall, Beijing‐BRT, and SmartCity) and one self‐constructed dataset (Indiana), were used to evaluate the alarm‐based congestion monitoring efficiency. The people‐counting results for highly crowded frame detection accuracy on the Mall, Beijing‐BRT, SmartCity, and Indiana datasets were 98.21%, 86.23%, 75.0%, and 87.01%, respectively. The proposed AIFCS framework ensures real‐time predictions across diverse sequences to prevent overcrowding in public places.

Predictive monitoring, identification, and control of Microcystis blooms in a drinking water source basin: An integrative artificial intelligence and bioinformatics approach.

Climate change-driven dispersal of pathogenic bacteria in large-scale lakes and reservoirs.

Four decades of Canine Parvovirus research: A global bibliometric and science mapping study.

Worldwide trends in hyperuricaemia from 2000 to 2023: a systematic review and modelling analysis.

Companion animal health surveillance systems: An environmental scan.

Doctors' wellbeing is an essential, yet often overlooked component of resilient health systems. Burnout, often described as the 'canary in the coalmine', offers a measurable, validated indicator of workforce strain that has direct implications for patient safety, quality of care and crisis response. This article argues for the integration of doctors' burnout into sentinel surveillance frameworks as part of South Africa's pandemic preparedness strategy. By embedding burnout monitoring within existing occupational health and surveillance systems, policymakers can generate actionable data, strengthen workforce resilience, and safeguard system performance during future epidemics.

The rapid growth of intelligent environmental security systems has intensified the need for accurate and real-time suspicious human activity recognition. Computer vision techniques, particularly deep learning–based object detection models, have emerged as key enablers in addressing these challenges. Among them, You Only Look Once (YOLO) has gained significant attention due to its high detection speed, end-to-end architecture, and suitability for real-time surveillance applications. This review paper presents a comprehensive analysis of the application of YOLO-based models in suspicious human activity recognition for intelligent environmental security systems. It examines the evolution of YOLO architectures, their adaptations for activity and behavior analysis, and their integration with surveillance frameworks. The review further discusses commonly used datasets, performance evaluation metrics, and comparative results reported in existing studies. In addition, key challenges such as occlusion, varying illumination, complex backgrounds, privacy concerns, and computational constraints are highlighted. Finally, the paper outlines future research directions, including hybrid models, multi-modal data fusion, edge-based deployment, and explainable AI, to enhance the robustness and reliability of YOLO-driven security systems. This review aims to provide researchers and practitioners with a structured understanding of current advancements and open issues in YOLO-based suspicious human activity recognition.