Search Results (1,182)

Background: Traumatic brain injury (TBI) is a leading cause of disability but one of the least recognized health problems in the world, affecting up to 69 million people annually. The associated lifelong disability in survivors, the loss of economic productivity, and being a risk factor for dementia consume 0.5% of global economic activity. Yet TBI is still largely invisible in national surveillance systems and not well represented in chronic disease frameworks. Consequently, governments are not equipped to provide proportional financing of acute care and long-term care of survivors, nor to build health care systems and resources for improving outcomes of TBI through policy frameworks targeting prevention, treatment, and equitable access. Objective: This commentary aims to provide a comprehensive picture of the global effort to formally recognize TBI as a notifiable and chronic condition, including the justifications for recognition, the formation of an international coalition of stakeholders, and the strategic plan for resolution at WHA79 of the World Health Assembly, one of the first concerted multinational efforts that occurred as a side event during the 78th World Health Assembly (WHA78) in May 2025. Methods: This commentary integrates information from epidemiological studies, global registries, and testimonies from people with lived experience of TBI. We analyze these data to develop policy needs and corresponding initiatives to address key needs. These include coordinated efforts to advocate change, such as technical briefings, consultations with stakeholders, and storytelling led by survivors, all of which informed and formed a part of the WHA78 side event. Our efforts have garnered wide, multi-sector support. Results: The WHA78 side event showed that ministries of health, neurosurgical, neurological, and rehabilitation societies, academic researchers, WHO representatives, and survivors all unprecedentedly support the recognition of the importance of TBI, facilitating national policies for its prevention and treatment via standardized surveillance. More than 30 non-governmental groups officially supported the campaign. A sponsoring member state made a public commitment to co-sponsor a WHA resolution, which set the stage for ongoing diplomatic progress and engagement across regions. Conclusion: To improve global brain health equity, access to long-term care, and the resilience of health systems, it is important to recognize TBI as a notifiable and chronic condition. A dedicated WHA resolution would make TBI a part of global health governance, making sure that it is counted, tracked, and dealt with as quickly and comprehensively as possible. It is both a technical necessity and a moral duty to help survivors and families and fight for justice in global health systems. Full article

This study investigated the clinical and genomic characteristics of Enterobacter cloacae complex (ECC) and Klebsiella aerogenes bloodstream infections (BSIs) in pediatric patients. A total of 115 BSI episodes (ECC: 86, K. aerogenes: 29) from 110 children hospitalized between 2011 and 2024 were retrospectively analyzed. Whole-genome sequencing was performed on available isolates to determine species, sequence types, and antimicrobial resistance (AMR) genes. Clinical characteristics, antibiotic usage, and outcomes were compared between groups. Patients with K. aerogenes BSI were younger and more likely to be preterm or diagnosed with urosepsis, while ECC infections were more frequently associated with hematologic malignancies. According to a multivariable analysis of the entire cohort (n = 115), K. aerogenes infection (OR [6.26], 95% CI [1.36–28.78]) and gentamicin resistance (OR [10.06], 95% CI [1.88–53.87]) were independently associated with 30-day mortality. Enterobacter hormaechei was the most common ECC species (68.4%) and exhibited the highest prevalence of AMR genes, particularly those conferring resistance to aminoglycosides, β-lactams, and trimethoprim–sulfamethoxazole. In contrast, K. aerogenes harbored few resistance genes. Multi-locus sequence typing analysis revealed high genetic diversity in both ECC and K. aerogenes, without evidence of dominant clonal expansion. Despite similarities in clinical presentation, ECC and K. aerogenes exhibit distinct age distributions, resistance profiles, and genetic diversity in pediatric BSIs. These findings underscore the importance of species-level identification and continued genomic surveillance to inform empirical antibiotic strategies and prevent the spread of resistant strains. Full article

The rapid growth of precision agriculture has accelerated the deployment of plant protection unmanned aerial vehicles (UAVs). However, reliable data resources for vision-based intelligent supervision of operational states, such as whether a UAV is currently spraying, remain limited. Most publicly available UAV detection datasets target urban security and surveillance scenarios, where annotations emphasize object localization rather than agricultural operation state recognition, making them insufficient for farmland spraying supervision. Therefore, agricultural-oriented data resources are needed to cover diverse backgrounds and include operation state labels, thereby supporting both academic research and practical deployment. In this study, we construct and release the first multi-background dataset dedicated to agricultural UAV spraying behavior recognition. The dataset contains 9548 high-quality annotated images spanning the following six typical backgrounds: green cropland, bare farmland, orchard, woodland, mountainous terrain, and sky. For each UAV instance, we provide both a bounding box and a binary operation state label, namely spraying and flying without spraying. We further conduct systematic benchmark evaluations of mainstream object detection algorithms on this dataset. The dataset captures agriculture-specific challenges, including a high proportion of small objects, substantial scale variation, motion blur, and complex dynamic backgrounds, and can be used to assess algorithm robustness in real-world agricultural settings. Benchmark results show that YOLOv5n achieves the best overall performance, with an accuracy of 97.86% and an mAP@50 of 98.30%. This dataset provides critical data support for automated supervision of plant protection UAV spraying operations and precision agriculture monitoring platforms. Full article

Multiple Unmanned Aerial Vehicle (UAV) collaborative coverage path planning is widely applied in fields such as regional surveillance. However, optimizing the trade-off between deployment costs and task execution efficiency remains challenging. To balance resource costs and execution efficiency with an uncertain number of UAVs, this paper analyzes the characteristics of irregular mission areas and formulates a bi-level optimization model for multi-UAV collaborative CPP. The model aims to minimize both the number of UAVs and the total path length. First, in the upper level, an improved Best Fit Decreasing algorithm based on binary search is designed. Straight-line scanning paths are generated by determining the minimum span direction of the irregular regions. Task allocation follows a longest-path-first, minimum-residual-range rule to rapidly determine the minimum number of UAVs required for complete coverage. Considering UAV’s turning radius constraints, Dubins curves are employed to plan transition paths between scanning regions, ensuring path feasibility. Second, the lower level transforms the problem into a Multiple Traveling Salesman Problem that considers path continuity, range constraints, and non-overlapping path allocation. This problem is solved using an Improved Biased Random Key Genetic Algorithm. The algorithm employs a variable-length master–slave chromosome encoding structure to adapt to the task allocation of each UAV. By integrating biased crossover operators with 2-opt interval mutation operators, the algorithm accelerates convergence and improves solution quality. Finally, comparative experiments on mission regions of varying scales demonstrate that, compared with single-level optimization and other intelligent algorithms, the proposed method reduces the required number of UAVs and shortens the total path length, while ensuring complete coverage of irregular regions. This method provides an efficient and practical solution for multi-UAV collaborative CPP in complex environments. Full article

Aflatoxins, particularly aflatoxin B₁ (AFB₁), are among the most potent naturally occurring carcinogens and remain a major food-borne hazard in parts of Asia and Africa. China has generated a uniquely cohesive body of evidence connecting aflatoxin contamination to hepatocellular carcinoma (HCC), especially in settings where chronic hepatitis B virus (HBV) infection is highly prevalent and acts synergistically with aflatoxin exposure. Over five decades, field investigations and laboratory innovations—exemplified by long-term work in Qidong—have assembled a multi-layered causal chain spanning the following: (i) contamination monitoring in staple foods; (ii) quantification of internal dose and biologically effective dose using validated biomarkers (e.g., urinary AFB₁–N⁷–guanine, AFM₁, and serum AFB₁–lysine albumin adducts); (iii) a characteristic molecular fingerprint in tumors and circulation (TP53 R249S); (iv) reversibility demonstrated through randomized intervention trials and policy-driven natural experiments. Chemoprevention and dietary interception studies (e.g., oltipraz, chlorophyllin, and broccoli sprout beverages) showed that enhancing detoxication pathways can lower biomarker burdens in exposed populations. At the population level, a sustained dietary transition from maize to rice, together with strengthened food governance, was accompanied by marked decreases in biomarker distributions and subsequent declines in HCC mortality in endemic regions. Nevertheless, regional heterogeneity, multi-mycotoxin co-exposure, and climate variability are expected to increase exposure volatility and complicate surveillance. Here, we translate and synthesize the Chinese evidence base, highlight biomarker-enabled monitoring and policy evaluation, and propose an integrated “5+1” prevention framework spanning source control, process detoxification, tiered governance, short-course interception, precision follow-up of high-risk individuals, and climate-sensitive early warning along the climate–agriculture–storage–processing–population (CAT–CSPP) chain. Full article

The rapid growth of Unmanned Aircraft Systems (UASs) and Advanced Air Mobility (AAM) presents significant integration and safety challenges for the National Airspace System (NAS), often relying on disconnected Air Traffic Management (ATM) and Unmanned Aircraft System Traffic Management (UTM) practices that contribute to airspace incidents. This study evaluates Geographic Information Systems (GISs) as a unified, data-driven framework to enhance shared airspace safety and efficiency. A comprehensive, multi-phase methodology was developed using GIS (specifically Esri ArcGIS Pro) to integrate heterogeneous aviation data, including FAA aeronautical data, Automatic Dependent Surveillance–Broadcast (ADS-B) for crewed aircraft, and UAS Flight Records, necessitating detailed spatial–temporal data preprocessing for harmonization. The effectiveness of this GIS-based approach was demonstrated through a case study analyzing a critical interaction between a University UAS (Da-Jiang Innovations (DJI) M300) and a crewed Piper PA-28-181 near Purdue University Airport (KLAF). The resulting two-dimensional (2D) and three-dimensional (3D) models successfully enabled the visualization, quantitative measurement, and analysis of aircraft trajectories, confirming a minimum separation of approximately 459 feet laterally and 339 feet vertically. The findings confirm that a GIS offers a centralized, scalable platform for collating, analyzing, modeling, and visualizing air traffic operations, directly addressing ATM/UTM integration deficiencies. This GIS framework, especially when combined with advancements in sensor technologies and Artificial Intelligence (AI) for anomaly detection, is critical for modernizing NAS oversight, improving situational awareness, and establishing a foundation for real-time risk prediction and dynamic airspace management. Full article

Streptococcus agalactiae is a major cause of bovine mastitis, which affects the quality and yield of milk. The main strategy for controlling this pathogen on dairy farms is the use of antibiotics. This study investigated the clonality, serotype distribution, antimicrobial susceptibility, and presence of resistance and virulence genes in 46 S. agalactiae isolates obtained from raw bovine milk in northeastern Brazil. Capsular types were determined using multiplex PCR and antibiotic susceptibility profiles were determined using disc diffusion or the gradient strip method. Clonal diversity was evaluated via pulsed-field gel electrophoresis. Eight isolates were sequenced using short- and long-read methods. There was high overall genetic diversity, whereas the resistance and virulence profiles were largely homogeneous within herds. Tetracycline and macrolide resistance was frequent and mediated by tetO and ermB and less frequently by tetM. Genome analysis demonstrated that resistance genes are present in mobile genetic elements that are also present in human isolates, and phylogenomic analyses identified ST-103 as the predominant and multi-host-adapted lineage, whereas ST-91 clustered with the bovine-adapted lineage. These findings expand the molecular epidemiology of S. agalactiae in dairy farms of a region in northeast Brazil and highlight the importance of surveillance strategies for guiding mastitis control and mitigating the spread of antimicrobial resistance. Full article

Aim of the study: To investigate the detection rate of respiratory viruses identified by multiplex real-time PCR (MPL real-time PCR) in respiratory specimens collected from hospitalized patients with acute lower respiratory tract infections (LRTI) over a five-year period (2020–2024), and to emphasize the importance of MPL real-time PCR testing in identifying respiratory viruses responsible for severe lower respiratory tract infections requiring hospitalization. Subjects and Methods: This cross-sectional retrospective study analyzed 15,936 respiratory specimens collected from hospitalized patients between 2020 and 2024. Seventeen respiratory viruses were detected using MPL real-time PCR. Statistical comparisons were performed using the chi-square test. Results and Discussion: The overall respiratory virus detection rate was 31.88% and was significantly higher in children than in adults (52.98% vs. 18.10%). The most frequently detected viruses were rhinovirus, influenza A, respiratory syncytial virus, and parainfluenza virus type 3, while influenza A and SARS-CoV-2 predominated in adults. During the peak of the COVID-19 pandemic in 2021, SARS-CoV-2 accounted for 78.92% of detected viruses, accompanied by marked suppression of other respiratory pathogens. Measles virus re-emerged in 2024, predominantly affecting children (17.65%). Most Respiratory virus-positive cases (82.8%) involved single-agent infections. Conclusions: Hospitalized acute LRTI cases often lack distinctive clinical signs to identify viral pathogens. MPL real-time PCR provides simultaneous multi-virus detection, enabling accurate etiological diagnosis and strengthening hospital-based viral surveillance, particularly in resource-limited settings. Full article

Early and accurate diagnosis is critical for disease surveillance, therapeutic guidance, and relapse monitoring. Sensor arrays have emerged as a multi-analyte detection tool via non-specific interactions to generate unique fingerprint patterns with high levels of selectivity and discrimination. Conjugated polymers (CPs), with their tunable π-conjugated backbones, exceptional light-harvesting capability, and efficient “molecular wire effect,” provide an ideal and versatile material platform for such arrays, enabling significant optical signal amplification and high sensitivity. This review systematically outlines the rational design and functionalization strategies of CPs for constructing high-performance sensor arrays. It delves into the structure–property relationships that govern their sensing performance, covering main-chain engineering, side-chain functionalization, and microenvironmental regulation. Representative applications are discussed, including non-small cell lung cancer, breast cancer, bacterial and viral infections, Alzheimer’s disease, and diabetic nephropathy, highlighting the remarkable diagnostic capabilities achieved through tailored CP materials. Finally, future perspectives are focused on novel material designs and device integration to advance this vibrant field. Full article

Objective: The emergence and spread of multi-drug-resistant (MDR) bacteria pose a growing threat in veterinary medicine, particularly in equine hospitals. This study investigated the colonization and infection dynamics of horses undergoing emergency laparotomy with two distinct antibiotic protocols (single-shot versus 5-day protocol) during hospitalization. Methods: Nasal swabs and fecal samples were collected from 67 horses undergoing emergency laparotomy at clinic admission as well as on postoperative days 3 and 10. These were screened for multi-drug-resistant indicator pathogens. As multi-drug-resistant indicator pathogens, methicillin-resistant Staphylococcus aureus (MRSA), extended-spectrum β-lactamase (ESBL)-producing Enterobacterales (ESBL-E), and bacteria belonging to the Acinetobacter baumannii complex were defined. Results: Preoperatively, 6.2% of horses tested positive for MRSA and 13% for ESBL-E. An increase in colonization was observed on day 3 postoperatively, with 62.1% of nasal swabs and 86.4% of fecal samples testing positive for MDR organisms. On day 10, 53.4% of nasal swabs and 62.5% of fecal samples tested positive for indicator pathogens. Surgical site infection developed in five horses, two of which tested positive for MRSA in both nasal and wound samples during hospitalization, supporting the potential role of nasal carriage as a source of infection. Furthermore, all horses tested positive for ESBL-E during at least one time-point during hospitalization, and Enterobacterales (MDR in two surgical site infections (SSI)) were involved in all surgical site infections. No significant differences were observed between the two antibiotic treatment groups regarding colonization rates with indicator pathogens during hospitalization. However, the results indicate that hospitalization itself contributes to increased colonization with resistant bacteria. A clear limitation of the study is the restricted number of sampled horses and the lack of environmental contamination data. Non-sampled hospitalized horses with and without antibiotic treatment may have acted as reservoirs for MDR bacteria. Conclusion: The findings emphasize the need for routine environmental monitoring and strict adherence to hygiene protocols in equine clinics to reduce the risk of nosocomial transmission. Ongoing surveillance and infection control strategies are essential to mitigate the spread of MDR pathogens in veterinary settings. Full article

Background and objectives: The increasing volume of total hip and knee arthroplasty created a significant postoperative surveillance burden. While plain radiographs are standard, the detection of aseptic loosening is subjective. This review evaluates the state of the art regarding AI in radiographic analysis for identifying aseptic loosening and mechanical failure in primary hip and knee prostheses. Methods: A systematic search in PubMed, Scopus, Web of Science, and Cochrane was conducted up to November 2025, following PRISMA guidelines. Peer-reviewed studies describing AI tools applied to radiographs for detecting aseptic loosening or implant failure were included. Studies focusing on infection or acute complications were excluded. Results: Ten studies published between 2020 and 2025 met the inclusion criteria. In internal testing, AI models demonstrated high diagnostic capability, with accuracies ranging from 83.9% to 97.5% and AUC values between 0.86 and 0.99. A performance drop was observed during external validation. Emerging trends include the integration of clinical variables and the use of sequential imaging. Conclusions: AI models show robust potential to match or outperform standard radiographic interpretation for detecting failure. Clinical deployment is limited by variable performance on external datasets. Future research must prioritize robust multi-institutional validation, explainability, and integration of longitudinal data. Full article

Background/Objectives: Antimicrobial resistance (AMR) surveillance is a cornerstone of national AMR strategies but requires sustained, cross-sectoral financing. While the need for such financing is well recognized, its quantification remains scarce in low- and middle-income countries. This study aimed to estimate the full costs of AMR surveillance across the human health, animal health, and food sectors (2021–2030) in selected facilities in Nepal and generate evidence to inform sustainable financing. Methods: A bottom-up micro-costing approach was used to analyze data from five sites. Costs were adjusted for inflation using projected gross domestic product deflators, and probabilistic sensitivity analyses were conducted to assess uncertainty in laboratory sample volumes under four scenarios. Results: The total cost of AMR surveillance in Nepal was $6.7 million: $3.4 million for human health (50.3% out of the aggregated costs), $2.7 million for animal health (39.8%), and $0.7 million for the food sector (9.9%). Laboratories accounted for >90% of total costs, with consumables and personnel as the main cost drivers. Average cost per sample was $150 (animal), $64 (food), and $6 (human). Conclusions: This study offers the first robust, multi-sectoral 10-year cost estimates of AMR surveillance in Nepal. The findings highlight that sustaining AMR surveillance requires predictable domestic financing, particularly to cover recurrent laboratory operations as donor support declines. These results provide cost evidence to support future budgeting and policy planning toward sustainable, nationally financed AMR surveillance in Nepal. Full article

Influenza is typically framed as an acute respiratory infection, yet accumulating evidence suggests that—like SARS-CoV-2—it may trigger persistent, multi-organ morbidity consistent with a post-acute infection syndrome (“long flu”). Leveraging the nationwide FinnGen registry infrastructure, we conducted a temporally stratified disease-wide association study (DWAS) to map antecedent risk factors and long-term sequelae following clinically diagnosed influenza and COVID-19. We assembled an exposed cohort comprising 9204 individuals with influenza (ICD-10 J09–J11) and 4258 individuals with COVID-19 (ICD-10 U072) recorded in specialist inpatient/outpatient care between 1998 and 2021, and an unexposed comparator cohort of 420,005 individuals with no recorded influenza or pneumonia (J09–J18) across their available medical history. Across harmonized clinical endpoints, we fitted age- and sex-adjusted Cox proportional hazards models and controlled for multiple testing using a stringent false discovery rate threshold (FDR-adjusted p < 0.001), further interrogating temporal persistence within 1-, 5-, and 15-year windows. The DWAS revealed that both infections are associated with broad, system-spanning disease signatures extending beyond the respiratory tract, including circulatory, neurological, metabolic, musculoskeletal, digestive, mental/behavioural, ocular, and oncologic endpoints. Predisposition analyses demonstrated that infection risk is concentrated in individuals with substantial pre-existing multimorbidity, most prominently cardiovascular disease, alongside cardiometabolic, respiratory, renal, neuropsychiatric, and inflammatory conditions. Post-infection analyses identified a durable burden of incident multi-system morbidity after influenza, with particularly robust and persistent cardiovascular and neurological signatures—encompassing thromboembolic disease and major adverse cardiovascular outcomes, as well as migraine, neurodegenerative disorders, and depression—together with metabolic and renal sequelae that, in subsets, extended across multi-year horizons. Collectively, these longitudinal findings reframe influenza as a systemic event embedded within a chronic disease continuum, motivate recognition of “long flu” as a clinically meaningful post-viral risk landscape, and support intensified prevention and risk-stratified surveillance strategies alongside analogous efforts for long COVID. Full article

Background/Objectives: Gestational exposure to substances (GES) is associated with adverse developmental outcomes. Early identification is limited by reliance on self-reported data. This study assessed the incidence and predictors of discordance in GES reporting between birth certificates and Medicaid claims among Medicaid-covered births in Nevada from 2022 to 2024. Methods: A statewide, hospital-clustered, cross-sectional analysis was conducted using linked Medicaid billing and birth record data. Discordance was defined as GES identified in one source but not the other. Incidence per 1000 live births was stratified by demographic characteristics. Multilevel logistic regression assessed patient- and hospital-level predictors, with random hospital intercepts. Results: Among 50,394 live births, the discordance rate was 95.09 per 1000 (95% Confidence Interval: 92.5–97.7). Substantial disparities were observed by race/ethnicity, socioeconomic status, and geography, with higher discordance among White non-Hispanic mothers, those residing in rural or frontier counties, and individuals with lower educational attainment or living in lower-income areas. Modest but meaningful variation was also observed across hospitals, including differences by hospital size and teaching or research status. Conclusions: Findings highlight substantial discordance in GES reporting and underscore the limitations of single-source surveillance. Findings also have clear policy relevance, indicating that improved cross-system data integration would strengthen statewide surveillance, enhance early detection, and support more equitable resource allocation and intervention strategies. Full article

Background/Objectives: Staphylococcus aureus is a multifaceted pathogen responsible for diseases in humans and in several animal species, including dairy cows. This study aimed to characterize and compare the genetic diversity, lineage distribution, and antimicrobial resistance profiles of S. aureus isolates from bovine milk with human-derived reference genomes to investigate host adaptation and inter-species transmission. Methods: Genomic analyses were performed on S. aureus isolates from quarter milk samples of dairy cows together with human-derived sequences from public databases. Whole-genome sequencing and multi-locus sequence typing (MLST) were used to determine sequence type (ST) distribution, and the presence of key antibiotic resistance genes and mobile genetic elements (MGEs) was assessed. Comparative genomics was applied to evaluate gene content, phylogenetic relationships, and lineage–host associations. Results: The dataset encompassed bovine-adapted lineages (CC97, CC133, CC151) and human-associated lineages (CC1, CC5, CC8, CC30, CC45), as well as livestock-associated ST398 in bovine samples and human-adapted ST5 and ST6 in animals. ST8 was the only ST shared between animal and human isolates and showed differing resistance profiles, with animal ST8 carrying resistance determinants absent from human ST8. Bovine-adapted strains were characterized by recurrent loss of human-associated virulence genes and acquisition of bovine-associated mobile genetic elements, and blaZ and mecA were rarely detected in bovine-adapted CC97 but were frequently present in human CC5 and CC8. Overall, animal isolates carried fewer resistance genes than human isolates. Conclusions: S. aureus from dairy cows and humans displayed substantial genetic diversity, with evidence of host-associated lineages and dynamic changes in gene and mobile element content. These findings support the need for integrated One Health surveillance to track shared and host-adapted lineages and their antibiotic resistance determinants. Full article