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A tale of SARS-CoV-2 genomic surveillance in Mali: Variants introductions and transmission dynamics
Despite the global scale of SARS-CoV-2 genomic surveillance, data from West Africa are limited. In Mali 33,197 confirmed cases and 743 deaths (case fatality ratio: 2.23%) were recorded between March 2020 and August 2025. This study aimed to provide the first comprehensive genomic reconstruction of SARS-CoV-2 variant introductions, transmission pathways, and evolutionary trends in Mali from 2020 to 2023. A total of 548 Malian sequences were used for lineages assignment and variant tracking. For the reconstruction of transmission dynamic a high quality subset of 305 Malian sequences (passing 90% coverage) was analysed alongside 142 closely related global genomes (from 20 countries) identified using the UShER. Time-resolved phylogenetic reconstruction, ancestral trait inference, and molecular clock analyses were performed with TreeTime v0.11.4 to infer variant introductions, local transmission, and substitution rates. Genomic and epidemiological data revealed five distinct epidemic waves driven by the temporal succession of global variants, from 19A/20A lineages in early 2020 to dominant XBB sublineages in 2023. Multiple independent viral introductions were traced primarily to Europe and North America, followed by sustained local and intercontinental transmission, including reciprocal exchanges with neighbouring African countries. The substitution rate was estimated to be 1.56 x 10−3 substitutions per site per year, consistent with global evolutionary trends. Entropy analysis revealed high genetic variability within the spike gene, particularly in the receptor-binding domain. These findings underscore the persistent global connectivity and local evolutionary pressure shaping the trajectory of the pandemic in Mali. These call for the need for sustained genomic surveillance capacity to inform epidemic preparedness and response.
Facile and scalable synthesis of Si@Void@C–graphite anodes from cost-effective micron-sized Si with optimized carbon coating toward high-performance lithium-ion batteries
Visualizing youth sports specialization and injury risk: A novel application of swimmer plots
Background As research on sport participation in youth and the impact of sport specialization on injury increases, the value of a standardization of data variables and an easily interpretable way of visualizing the data will be demonstrated with improved quality and comparability of future findings. Purpose Using simulated sport participation data from the perspectives of chronological age and years of participation, the utility and flexibility provided by swimmer plots is a novel method for visualization of data to assess the impact of single versus multiple sport participation on the occurrence of injury. Methods Using recall survey data, a simulated dataset based on chronological age as well as years of participation was created to allow for differing visualizations of the impact of single vs multiple sport participation on the occurrence of injury using swimmer plots. Results Characterization and presentation of sports history are deceptively complex and the reliance on tabular and simple graphics fail to provide a comprehensive picture. Collecting a core data set, allowing for multiple denominators such as chronological age and years of participation along with using swimmer plots as an effective means of visually presenting the data allow for a more robust and multi-faceted approach to the understanding of the impact of youth early sport specialization on injury risk. Demonstrated are examples of injury occurrence as it relates to chronological age of participation as well as years of participation. Figures demonstrating differences in injury rates based on number of sports participated in at various ages are also provided. Conclusion Swimmer plots allow for visualization of data from multiple perspectives which facilitates a comprehensive understanding of the complex, overlapping factors potentially impacting injury in youth sports. Visualizing more than one perspective allows for a more complete picture of the factors involved.
A DNN assisted parallel control architecture for automated anesthesia delivery addressing inter-patient variability
Abstract The control of depth of hypnosis (DoH) in anesthesia is critical for ensuring patient safety, minimizing clinical intervention, and improving postoperative outcomes. However, the nonlinear and patient-specific nature of anesthetic dynamics poses significant challenges to traditional single-loop PID controllers which often fail to provide robust performance across diverse patient profiles. To address these limitations, this work proposes a robust and adaptive control framework based on a two-degree of-freedom (2-DOF) parallel control scheme, which decouples setpoint tracking from disturbance rejection. To further enhance adaptability, an offline deep neural network (DNN)-based gain scheduling strategy is integrated, enabling controller parameters to be precomputed based on patient-specific characteristics. Genetic algorithms are used to optimize the controller parameters for 13 clinically validated patient categories by minimizing the integral of absolute error (IAE). Simulation results demonstrate substantial improvements in maintaining the Bispectral Index (BIS) within the desired range of 40–60, validating the framework’s ability to deliver personalized and precise control of anesthesia. The paper presents the motivation behind the proposed approach, details the control architecture and optimization methodology, and concludes with a discussion of performance evaluation across various patient models.
Mining triggers extensive additional deforestation in sub-Saharan Africa
Abstract Demand for minerals sourced from sub-Saharan Africa is expanding rapidly 1–5 . If poorly managed, mining expansion poses a key threat to tropical forests across the continent 6,7 . Here we present a spatiotemporal assessment of mining-driven deforestation of dense forests across Africa, using continent-wide data on post-deforestation land uses and a robust difference-in-differences framework to assess 16,627 mines between 2001 and 2020. In total, we find 187,000 hectares of direct mining-driven deforestation, that is, deforestation due to features directly associated with mining operations, such as pits, tailing ponds and spoil heaps. We estimate that mining also triggers an additional 8.0 percentage points (pp; 95% confidence interval (CI): 7.2–8.9 pp) increase in deforestation within 1 km of a mine compared with unmined areas. Increased levels of deforestation (1.1 pp, 95% CI: 0.7–1.5) persist up to 20 km from mines even after ten years. For every hectare of direct deforestation due to the mine footprint, mining triggers, on average, 34 hectares of additional offsite loss within five years through ancillary activities, including agriculture and settlements. Mines extracting cobalt and copper—key energy transition minerals—caused the highest amount of additional deforestation. Embedding offsite deforestation levels into environmental impact assessments for new mining projects will be key to ensuring zero-deforestation or no-net-loss supply chains for critical minerals and reduce future mining-driven forest losses in sub-Saharan Africa.
Genomic characterization of multidrug-resistant Escherichia coli isolated from gills of Labeo rohita: Insight into resistome, virulence and pathogenicity
Antibiotic resistance in aquaculture settings is an emerging issue that threatens animal and human health. In this work, a multidrug-resistant (MDR) Escherichia coli strain (RG3) was isolated from the gill of Labeo rohita obtained from a retail fish market in Punjab, India. Antimicrobial susceptibility testing revealed resistance to various antibiotic groups, with intermediate susceptibility to imipenem. Whole-genome sequencing produced a 3.70 Mb draft genome with 3,814 genes and 39 resistance determinants. Genome analysis identified a chromosomally encoded multidrug resistance phenotype, including β-lactam resistance and efflux-mediated mechanisms, together with virulence factors associated with iron acquisition, biofilm formation, and host colonization. No plasmids were predicted in the short-read assembly. Genome-based prediction suggested a high likelihood of human pathogenicity and assigned the isolate to sequence type ST8020. This study aimed to characterize an MDR E. coli from an aquaculture source using phenotypic and genomic approaches. The identification of a chromosomally encoded resistome together with virulence-associated traits highlights the potential of aquaculture-derived E. coli to act as reservoirs of antimicrobial resistance within a one health framework.
Access to GLP-1s for Medicare Beneficiaries — A Bridge to Nowhere?
Organizational dehumanization and career adaptability among clinical nurses: the mediating role of self-compassion and the moderating role of psychological safety
Efficacy of adjunctive antibiotics compared to non-antibiotic therapies following mechanical debridement for peri-implantitis: A systematic review and meta-analysis of randomized controlled trials
Objective To evaluate the efficacy of antibiotic therapy as an adjunct to mechanical debridement in peri-implantitis. Method PubMed, Embase, Web of Science, the Cochrane Library, and Scopus were searched (inception to 15 January, 2026). Additionally, clinical trials were searched on ClinicalTrials.gov. Meta-analyses were performed to evaluate bone level (BL), bleeding on probing (BOP), probing pocket depth (PPD), clinical attachment level (CAL), modified plaque index (mPLI), plaque score (PS). Results Twenty-two RCTs were included. Adjunctive antibiotics with mechanical debridement demonstrated significant advantages in improving PPD (WMD = −0.69; 95% CI: [−1.04, −0.33]; P = 0.0001; heterogeneity:I 2 = 91%, P < 0.00001), CAL (WMD = −0.55; 95% CI: [−1.07, −0.04]; P = 0.04; heterogeneity:I 2 = 95%, P < 0.00001), PS (WMD = −4.07; 95% CI: [−6.80, −1.33]; P = 0.004; heterogeneity:I 2 = 0%, P = 0.94), and BOP (WMD = −12.45; 95% CI: [−24.13, −0.77]; P = 0.04; heterogeneity:I 2 = 94%, P < 0.00001). In the subgroup analysis comparing mechanical debridement plus antibiotics versus mechanical debridement alone, statistically significant improvements were observed for CAL (WMD = −0.84; 95% CI: [−1.55, −0.13]; P = 0.02) and PS (WMD = −3.94; 95% CI: [−7.14, −0.74]; P = 0.02), confirming clinical necessity of antibiotics as an adjunct to debridement. In the comparison of local versus systemic antibiotic administration, comparable efficacy in reducing PPD was observed for both local (WMD = −0.68; 95% CI: [−1.14, −0.22]; P = 0.004) and systemic (WMD = −0.70; 95% CI: [−1.25, −0.15]; P = 0.01) routes, with no significant difference between subgroups (P = 0.95, I² = 0%). For PS, local antibiotics (WMD = −6.57; 95% CI: [−11.10, −2.04]; P = 0.004) demonstrated significantly greater improvement compared to systemic antibiotics (WMD = −2.63; 95% CI: [−6.07, 0.80]; P = 0.13). Conclusion As an adjunct to mechanical debridement, antibiotics may provide modest benefits for peri-implantitis, demonstrating efficacy in improving probing pocket depth, clinical attachment level, plaque score, bleeding on probing, and other parameters related to soft tissue health and plaque control. PROSPERO CRD420261328879. Available from https://www.crd.york.ac.uk/PROSPERO/view/CRD420261328879 .
When Health Costs No Longer Count — Air Pollution and EPA Rulemaking
Automated eDNA and eRNA profiling for biodiversity monitoring in marine and freshwater ecosystems
Abstract Biodiversity monitoring is essential to measure the impacts of pollution, invasive species, and the longer-term effects of climate change. Automated samplers enable temporally flexible, remote collection of environmental DNA (eDNA), improving access to time-sensitive events. The Dartmouth Ocean Technologies (DOT) Preserving eDNA Sampler has proven effective in multi-month marine deployments, but further validation is needed across a broader range of habitats and water chemistries, and to establish its suitability for collection and assessment of environmental RNA (eRNA). In this study, we collected samples near the surface (1–1.5 m depth) of a brackish pond, a freshwater lake, and two marine harbours. We identified patterns of species turnover consistent with transitions among aquatic environments, including invasive species such as smallmouth bass and chain pickerel in the freshwater lake. Automated deployment in Halifax Harbour following a significant rainfall event detected nearly ten times as many probable fecal-associated bacteria by proportion at this site relative to Lunenburg Harbour. Preserved eRNA allowed the identification of taxa below the eDNA limit of detection. Our pilot study demonstrates the feasibility of using the DOT sampler for longer-term biomonitoring in a diverse range of aquatic habitats, yielding ecological insights that would not be attainable through manual sampling alone.
Digital storytelling across the life course: Protocol for a theory-based analysis
Background Digital storytelling (DST) combines first-person narratives with images, sound, and video to convey health experiences, empower under-represented voices, and foster empathy. Insight into how digital stories portray life-course influences remains scarce. Guided by the Life Course Health Development (LCHD) framework, this study protocol describes a planned theory-based analysis of 105 publicly available digital story videos from Alberta Health Services (AHS). We aim to (1) map representations of cumulative exposures, critical periods, and transitions across stories spanning childhood to late adulthood; and (2) generate guidance for using DST to strengthen partnerships among patients, caregivers, clinicians, and policymakers. Methods We present our detailed methods for our planned study within this protocol using the Standards for Reporting for Qualitative Research checklist. Our team will review each video in AHS’ DST repository with a structured data collection form, capturing storyteller demographics, life-course stage, clinical context, LCHD constructs, and reflexive notes. Directed content analysis of our data collection forms will be performed in NVivo 14 with a hybrid coding approach: deductive codes to mirror core LCHD concepts and inductive codes to capture context-specific themes. Discussion Insights from this study will guide clinicians’ life course dialogue, inform health systems’ investment in DST platforms for learning, and assist policymakers in embedding lived experience within equity agendas. The study will yield theoretically grounded recommendations for curating, analyzing, and mobilizing DST libraries across diverse health settings. Protocol Registration: https://doi.org/10.17605/OSF.IO/C5R9T
Prevention and Treatment of Peanut Allergy
A drug repurposing screen identifies antiviral compounds against Puumala Orthohantavirus
Abstract Hantaviruses are zoonotic negative-sense RNA viruses that cause haemorrhagic fever with renal syndrome (HFRS) and hantavirus pulmonary syndrome (HPS), yet no approved antiviral therapies are available. To identify host-directed modulators of hantavirus infection we performed a drug repurposing screen using live Puumala virus (PUUV). We identified and validated 70 drugs with antiviral activity in A549 cells and primary human endothelial cells. Functional clustering confirmed the known infection-inhibitory effect of several groups of compounds, including inhibitors of heat shock proteins, mTOR pathway and nucleotide synthesis. Our screen also identified compounds yet unexplored as antivirals against Hantaviruses, such as certain antibiotics. Our dataset provides a systematic map of host pathways influencing PUUV infection and highlights candidate compounds and cellular processes that can modulate this process.
Spatial distribution of diphtheria cases during the 2022/2023 outbreak in Kano State, Northern Nigeria
After decades of control, a nationwide diphtheria outbreak occurred in Nigeria in 2022, with approximately 75% of confirmed cases reported in Kano state, Nigeria. We assessed the spatial distribution of diphtheria cases in Kano state to identify disease clusters/hotspots. We used national surveillance data on 10,085 confirmed cases of diphtheria in Kano state from April 2022 to December 2023, accessed via the Nigerian Centre for Disease Control and Prevention website. Data were converted to CVS format and analyzed for spatial distribution of diphtheria cases using QGIS-LTR Version 3.34.11 . We found clustering of diphtheria cases in the eight metropolitan Local Government Areas (LGAs) of the state, where health facilities were also clustered. Ungogo LGA had the highest clustering of diphtheria cases but the least clustering of health facilities. This study enhances understanding of the spatial dynamics of diphtheria transmission in Nigeria and provides actionable insights for designing targeted interventions and strategies against hotspots to curb transmission and strengthen preparedness for future epidemics.
Unheralded Syncope from Ventricular Arrhythmia
Machine learning prediction of compressive strength in 3d printed fiber reinforced concrete using support vector regression and artificial neural networks with shapley additive explanations
Abstract The swift progression of three-dimensional (3D) concrete printing has opened the door to the creation of innovative materials, such as fiber-reinforced concrete, making it essential to develop accurate models for predicting their mechanical properties. Accurately estimating the compressive strength (CS) of such materials is required for optimizing mix designs and ensuring structural performance. In this context, the present study is the first to systematically investigate and compare three different support vector regression (SVR) kernels, namely, linear SVR (L-SVR), polynomial SVR (Poly-SVR), and radial basis function SVR (RBF-SVR), for predicting the compressive strength of normal, high, and ultra-high strength 3D-printed fiber-reinforced concrete (3DPFRC). A model was developed and validated using a dataset of 278 samples collected from the literature. In addition to SVR models, Artificial Neural Networks (ANN) and Gradient Boosting Machines (GBM) were developed for benchmarking purposes. Results from statistical evaluation revealed that the ANN model achieved the highest predictive accuracy overall, outperforming the L-SVR, Poly-SVR, and GBM models. Among the SVR-based models, the RBF-SVR demonstrated superior performance, showing competitive accuracy with the ANN while outperforming both the other SVR variants (L-SVR and Poly-SVR) as well as the GBM model in terms of coefficient of determination (R²) value and error metrics. All models achieved R² scores ranging between 0.94 and 0.86 for the training datasets and 0.93 and 0.72 in the testing datasets. The RBF-SVR model, in particular, offered an excellent balance between accuracy, training speed, and reliability, making it a strong and efficient alternative to more complex models. Additionally, SHapley Additive exPlanations (SHAP) analysis identified the water-to-cement ratio, silica fume content, and cement volume as the most influential parameters affecting compressive strength.
Brief intervention, lasting impact: One-year outcomes of the Bergen 4-day treatment for OCD in Germany
Background Obsessive–compulsive disorder (OCD) is a prevalent and highly disabling mental disorder. Worldwide, it is associated with substantial individual suffering and long-term societal costs. Although exposure and response prevention (ERP) is the recommended first-line treatment, access to effective care remains limited for reasons such as long treatment duration. These barriers contribute to persistent global treatment gaps. The Bergen 4-Day Treatment (B4DT) is a brief, concentrated form of ERP (cERP) designed to improve treatment efficiency and access. Although initial outcomes are promising, long-term data from countries other than Norway are limited. This study evaluated the 12-month outcomes of the B4DT in a German day-patient treatment setting, exploring its sustained effectiveness and feasibility outside its original context. Methods Fifty-eight adults with OCD received B4DT in an uncontrolled study and were reassessed 12 months after treatment. The primary outcome was OCD severity, assessed with the Yale-Brown Obsessive Compulsive Scale (Y-BOCS). Secondary outcomes included depressive symptoms, global functioning, self-efficacy, experiential avoidance, self-esteem, quality of life, and mental health service utilization. Results Large reductions in OCD symptom severity (Y-BOCS) and significant improvements across most secondary outcomes were sustained at the 12-month follow-up. One year after treatment, 69% of participants met criteria for treatment response, 51% had achieved remission, and 78% showed reliable clinical improvement. No clinically relevant symptom deterioration was observed, and treatment acceptability and follow-up retention were high. Importantly, only a small proportion of patients ( n = 4; 7%) required inpatient treatment during the follow-up period. Conclusion These findings demonstrate that brief cERP can achieve durable clinical benefits for patients with OCD when implemented outside its original context. From a global mental health and health policy perspective, the combination of short treatment duration, sustained effectiveness, and low subsequent use of inpatient services suggests that cERP represents a scalable and resource-efficient strategy to expand access to evidence-based OCD treatment across diverse health care systems.