Applied and Environmental Microbiology

Blood banks represent highly controlled healthcare environments where microbiological surveillance has traditionally focused on blood products rather than environmental microbial reservoirs. Despite their critical role in transfusion safety, the ecology of surface-associated microorganisms and the persistence traits that enable their long-term survival remain poorly understood. Here, we combined scanning electron microscopy, culture-based microbiology, phenotypic characterization, MALDI-TOF mass spectrometry, and whole-genome sequencing to investigate whether surfaces within a public blood bank facility constitute reservoirs of environmentally derived bacteria with enhanced persistence potential. Samples collected from a public blood bank in Tucuman, Argentina yielded 37 culturable bacterial isolates, predominantly Gram-positive environmental taxa together with a limited number of opportunistic Gram-negative species. More than 30% of the isolates exhibited multidrug resistance, while several strains displayed strong biofilm formation, amyloid-like fiber production, motility, and hemolytic activity, indicating multiple phenotypic strategies associated with long-term surface persistence. Whole-genome sequencing of six representative isolates confirmed species identity, identified genes related to antimicrobial resistance, adhesion, biofilm formation, stress adaptation, and cytotoxicity, and revealed frequent genotype-phenotype discordance, highlighting the importance of integrating genomic and phenotypic analyses. Notably, one isolate exhibited less than 92% average nucleotide identity with publicly available genomes, suggesting the presence of a previously undescribed environmental species. Thus, blood bank surfaces function as selective ecological niches favoring bacteria with persistence-associated traits rather than simply reflecting contamination from blood products. These microorganisms may constitute latent biosafety hazards if environmental barriers fail, particularly in facilities handling biological materials intended for vulnerable patients. Our results support the incorporation of integrated bioimaging, phenotypic characterization, and genome-resolved environmental surveillance into infection prevention strategies and transfusion biosafety programs within a One Health framework.

Antimicrobial resistance (AMR) is an ever-increasing threat to population health. Industrial, environmental and societal factors are increasingly recognized as important contributors to AMR within communities. Here, we investigated the spatial distribution of AMR genes (ARGs) across Alberta, Canada and their association with socio-economic, immigration-related, and agro-industrial characteristics using municipal wastewater-based surveillance. We analyzed monthly wastewater metagenomes collected between March 2022 and March 2023 across eleven municipalities, representing 39% of Alberta's population. Integration with census data enabled multivariate analysis, revealing that municipal resistome profiles were strongly structured along income and immigration-related population gradients. ARGs spanning 14 resistance classes exhibited distinct distributional patterns across income and immigration gradients, including contrasting associations among beta-lactam, aminoglycoside, and macrolide-lincosamide-streptogramin ARGs, consistent with heterogeneous selection pressures across sub-populations. These findings demonstrate the capacity of longitudinal wastewater surveillance to identify persistent population-level resistome patterns and highlight the importance of incorporating sociodemographic context into AMR surveillance and mitigation strategies.

Background Coccidioidomycosis is an emerging fungal disease across the arid Americas and a frequent cause of community-acquired pneumonia. Understanding where Coccidioides populations originate, how they move across space, and whether they are expanding is important for interpreting changing patterns of Valley fever and anticipating future infection risk. Methods We prospectively collected and whole-genome sequenced 186 Coccidioides-positive clinical isolates submitted to a national diagnostic laboratory, and included 126 previously sequenced genomes. We applied genomic clustering, time-calibrated phylogenetic reconstruction, ancestral area reconstruction, mating-type assignment, and demographic inference to identify major populations, infer dispersal patterns, assess evidence for recombination and clonality, and reconstruct historical population dynamics. Findings We analyzed 312 genomes (139 C. immitis; 173 C. posadasii) and identified three major genetic populations within each species. C. immitis included two California-centered populations and one Pacific Northwest population, whereas C. posadasii included two Arizona-centered populations and one Texas-centered population. The most recent common ancestor was estimated at approximately 127,000 years for C. immitis and 234,000 years for C. posadasii. Most populations were not fully monophyletic, consistent with retained ancestral variation and/or ongoing gene flow. Inferred dispersal was largely asymmetric, with most movement originating from California in C. immitis and from Arizona and Texas in C. posadasii. Most populations contained both mating types, but one C. immitis population and a Brazilian subgroup of C. posadasii were clonal. All populations showed recent demographic expansion. Interpretation The evolutionary history of Coccidioides is characterized by strong geographic structure, ongoing gene flow, and recent demographic expansion. These processes are likely to influence future patterns of Valley fever endemicity and supports the use of genomic surveillance to detect shifts in disease risk as environmental conditions change.

Larval source management (LSM) is a complementary malaria control intervention, yet evidence to guide context-specific implementation remains limited. Nigeria's recent national commitment to LSM scale-up makes the need for operational evidence particularly urgent. Informal settlements embedded within wards of differing dominant settlement archetypes may present distinct Anopheles larval habitat profiles with implications for how LSM strategies should be tailored. We evaluated Anopheles larval habitats within informal settlement areas across wards with contrasting settlement archetypes in Ibadan metropolis, Nigeria, to inform targeted larval source management. Potential breeding habitats were surveyed in dry and wet seasons within informal settlement areas across three wards -- Olopomewa, Challenge, and Agugu -- representing formal, informal, and slum settlement-dominant archetypes respectively. Habitats were characterized and assessed for Anopheles larval presence. Pareto analysis identified habitats accounting for 80% of larval abundance. Breeding habitat density per km{superscript 2} was estimated using a simulated pathway technique. Associations between mosquito dispersal scale and household malaria infections identified through Rapid Diagnostic Testing were evaluated using kernel-based distance-decay weighting. Environmental drivers of habitat suitability were modeled in MaxEnt. Of 420 potential breeding habitats identified, 31 (7.4%) contained Anopheles larvae, predominantly during the wet season (26, 83.9%). Puddles, dug wells, drainages/gutters/ditches and canals accounted for 80% of site-level larval abundance when standardized by sampling effort. Larval and breeding habitat density were highest in Agugu, the slum-dominant ward, across both seasons. Modeled mosquito dispersal scale showed best fit at 30-32m in Challenge (OR 1.41, 95% CI: 1.05-1.89) during the wet season and 16-18m in Agugu (OR 1.29, 95% CI: 1.04-1.60) during the dry season. Habitat suitability in Agugu was higher farther from large water bodies and in areas with higher population density and positive Normalized Difference Water Index values. In Challenge, suitability was higher in areas with lower nighttime light levels, positive Normalized Difference Water Index values, and negative Normalized Difference Moisture Index values. Further studies incorporating multiple wards across diverse urban settings are needed to determine whether differences in larval ecology between settlement archetypes provide a reliable basis for planning larval source management.

Background: Oral care is a critical nursing intervention for mechanically ventilated patients in intensive care units because it reduces oral microbial colonization and contributes to the prevention of ventilator-associated pneumonia, a major cause of morbidity, prolonged hospitalization, and increased healthcare costs among critically ill patients. Despite the availability of evidence-based guidelines, gaps in nurses knowledge and inconsistent implementation of oral care practices remain challenges, particularly in low- and middle-income settings. Objective: This study aimed to assess the level of knowledge among intensive care unit nurses regarding oral care for mechanically ventilated patients at Tenwek Hospital, Kenya, determine factors influencing knowledge, and examine the relationship between nurses knowledge and selected demographic and professional characteristics. Methods: An analytical cross-sectional study design was employed. The study was conducted among intensive care unit nurses at Tenwek Hospital. A sample of 38 nurses was selected from a target population of 60 intensive care unit nurses using simple random sampling. Data were collected using a structured self-administered questionnaire assessing socio-demographic characteristics, knowledge of evidence-based oral care practices, awareness of guidelines, and factors influencing knowledge. Data were analyzed using descriptive statistics, chi-square tests, Pearson correlation, and logistic regression, with statistical significance set at probability value less than 0.05. Results: Thirty-five nurses participated, yielding a response rate of 92.1 percent. The findings demonstrated generally high knowledge levels regarding oral care practices for mechanically ventilated patients. Most nurses correctly identified recommended oral care frequency (88.6 percent), oral assessment before care (91.4 percent), suctioning before and after oral care (94.3 percent), and the role of oral care in preventing ventilator-associated pneumonia (97.1 percent). However, knowledge gaps were identified in areas such as toothbrushing practices and documentation of oral care procedures. Training, continuing professional education, availability of guidelines, workload, resources, supervision, and teamwork were identified as important factors influencing knowledge. The findings revealed that professional qualification (probability value equal to 0.02), intensive care unit experience (probability value equal to 0.030) and formal oral care training (probability value equal to 0.021) were significantly associated with nurses knowledge of oral care practices. Conclusion: Intensive care unit nurses at Tenwek Hospital demonstrated adequate knowledge of evidence-based oral care practices for mechanically ventilated patients. However, targeted educational interventions are required to address identified gaps and strengthen standardized oral care delivery. Continuous professional development, consistent use of guidelines, supportive supervision, and adequate resource provision are recommended to enhance patient safety and reduce preventable intensive care unit complications such as ventilator-associated pneumonia. Keywords: Intensive care unit nurses; Oral care; Mechanically ventilated patients; Ventilator-associated pneumonia; Nursing knowledge; Evidence-based practice

Background Oral care is a critical nursing intervention for mechanically ventilated patients in intensive care units (ICUs) and plays an important role in preventing ventilator-associated complications. However, variability in nurses' competency in oral care remains a concern, particularly in resource-limited settings. Objective To assess ICU nurses' competency in providing oral care to mechanically ventilated patients and determine factors associated with competency at Tenwek Hospital, Kenya. Methods An analytical cross-sectional study was conducted among ICU nurses at Tenwek Hospital. A total of 38 nurses were invited, and 35 participated, yielding a response rate of 92.1%. Data were collected using a structured questionnaire and an observational competency checklist. Descriptive statistics and inferential analysis, including chi-square tests and binary logistic regression, were performed using SPSS version 30. Statistical significance was set at p<0.05. Results ICU nurses demonstrated generally high competency in key oral care practices, including use of personal protective equipment (100%), suctioning before and after oral care (91.4%), and oral assessment (80.0%). However, gaps were identified in documentation of oral care (62.9%) and adherence to standardized protocols (65.7%). Formal oral care training was significantly associated with competency (OR=5.63, 95% CI: 1.78-17.81, p=0.002), as were professional qualification (p=0.030) and ICU experience (p=0.021). In multivariable analysis, oral care training (OR=3.01, p=0.006), ICU experience (OR=2.85, p=0.015), and availability of guidelines (OR=2.17, p=0.047) were independent predictors of competency. Conclusion ICU nurses at Tenwek Hospital demonstrate satisfactory competency in oral care for mechanically ventilated patients, although gaps remain in documentation and protocol adherence. Strengthening training, guideline availability, and institutional support systems is essential to improve consistency and quality of oral care practice. Keywords Intensive care unit; oral care; nursing competency; mechanically ventilated patients; ventilator-associated pneumonia; Kenya.

Background: Zoonotic infectious disease risk arises at human-animal-environment interfaces where pathogen spillover can occur. Rural communities living in biodiverse settings may experience frequent contact with wildlife and shared environments through livelihoods, food practices, and economic activities. Reducing spillover risk and strengthening pandemic prevention requires both structural and individual-level change. Community-based interventions that promote awareness, risk perception, self-efficacy, pro-environmental behaviour, and safe coexistence with wildlife may support prevention by shifting behavioural determinants of zoonotic disease risk. The Saan Suk intervention was co-developed with rural communities in Thailand using a Human-Centred Design approach and is grounded in the Health Belief Model and One Health principles. The intervention is intended to be feasible, acceptable, and deliverable through Thailands established Village Health Volunteer (VHV) system. Methods: This protocol describes a parallel-arm, cluster-randomised controlled superiority trial that will be conducted during July - October 2026, in Chanthaburi Province, Thailand. 24 villages will be equally randomised to the Saan Suk intervention or the current practice (control). In intervention villages, trained VHVs will deliver, once a week over four weeks, a multimodal One Health educational intervention designed to improve knowledge of zoonotic spillover, promote protective behaviours, reduce risky wildlife-related contacts, and support respectful coexistence with wildlife. Trained outcome assessment teams will conduct structured interviews with 42 adult participants per village, yielding a total sample size of 1,008 participants. The sample size was calculated for the primary outcome, accounting for clustering, with 90% power to detect a medium effect size (6 points on the 0-100 knowledge scale) at a significance level of 0.05, accounting for a design effect with an ICC of 0.028. The primary outcome is knowledge of zoonotic spillover, transmission pathways, risk factors, protective and risky behaviours, and safe coexistence with wildlife. Secondary outcomes include attitudes, self-efficacy, preventive and risky behaviours, and reported contacts with major local reservoir hosts. A structured questionnaire was developed, expert-reviewed, and piloted for the outcome assessment. Outcomes will be analysed using mixed-effects regression models with random effects for village and adjustment for relevant pre-specified confounders. Primary analyses will follow the intention-to-treat principle. Discussion: This trial will evaluate whether a co-designed, VHV-delivered One Health educational programme can improve knowledge of zoonotic disease prevention and behavioural determinants in rural communities living in close contact with wildlife and shared ecosystems. If effective and feasible, Saan Suk could inform integration into routine VHV training and community-based zoonotic disease and pandemic prevention strategies. Trial Registration: The Saan Suk trial is registered with the German Clinical Trials Register (DRKS). Registration ID: DRKS00038582; date of registration: 11 May 2026.

Objectives: Research on mental health in agriculture has increased in recent years; however, it remains largely focused on farmers themselves and is predominantly male-oriented. The mental health of farm wives and partners, many of whom play integral roles in farm operations, business management, and family life, remains difficult to characterise. This study therefore aims to explore the prevalence and causes of mental health challenges among farm wives and partners, and to investigate their use of, and barriers to, mental health support services. Methods: Quantitative data was collected using over 450 structured questionnaire responses that assessed mental health prevalence, contributing stressors and support service utilisation. Results: Findings indicate that there is a high prevalence of mental ill health amongst farm wives, seemingly due to industry stressors and support role overwhelm. Interpersonal relationships played a significant role in the types of mental distress experienced and highlighted the toll that farm life can take on farm wives' social and emotional connections. Despite a range of formal and informal support services being available, and effective when used, significant barriers to accessing these services were identified, including both practical difficulties and self-stigmatisation due to cultural beliefs. Conclusions: Farm wives and partners experience substantial mental health burdens linked to their diverse and often underrecognized roles within agricultural systems. In future, targeted interventions are needed to reduce stigma, improve service accessibility, and recognize women's contributions within farm enterprises. Further research and dedicated investment are also essential to better understand and help improve the mental health of this overlooked population within agricultural industries.

Malaria remains a severe health problem in endemic regions because people lack adequate diagnostic tools, leading to delayed medical care and elevated death rates. This research introduces a dual-mode artificial intelligence system that uses two complementary models to enhance malaria pre-screening and diagnosis. The patient-centered model uses multivariate logistic regression to analyze biosignals, including heart rate, body temperature, and oxygen saturation, collected through a wearable sensor prototype and a mobile interface for symptom analysis. The system enables patients to begin self-assessment to determine their level of need before scheduling a doctor's appointment. The clinician-centered model represents a customized convolutional neural network that uses annotated microscopy images of red blood cells to achieve 94.84% accuracy, 95.71% precision, 93.87% recall, 94.78% F1 score, and 0.84 Area Under Curve (AUC). The patient model achieved 94.6% accuracy and an AUC of 0.985 using a 70/30 train-test split. These systems work together to create a layered diagnostic system that can operate independently or together to detect malaria at an early stage, especially in areas with limited resources. The findings demonstrate that wearable biosignal data integration with image-based deep learning can produce dependable, scalable, and user-friendly systems for malaria pre-screening. Keywords - malaria diagnosis, artificial intelligence (AI), convolutional neural networks (CNN), wearable biosensors, multivariate logistic regression

Background: Lead exposure is estimated to cause approximately 3.5 million premature deaths a year, yet the key ongoing sources of lead exposure are unclear. Methods: We estimated the contribution of dietary lead intake to global blood lead levels (BLLs) for 7-year-old children and 22-year-old adults by applying the All-Ages Lead Model (AALM) to calculate blood lead levels (BLLs) based on 25 total diet studies (TDS) that quantify dietary lead intake across 46 countries. Results: For children, the population-weighted average dietary lead intake in low- and middle-income countries (LMICs) (32.0 g/day) was found to be more than three times higher than in high-income countries (HICs) (9.3 g/day), and more than 10 times higher than the FDA reference level for children (2.2 g/day). The average impact on BLLs for children is estimated to be near 29 g/L in LMICs and near 12 g/L in HICs. Averaged across the TDS data, vegetables (27%) and cereals (24%) were found to contribute the most to dietary lead. Conclusions: While there are limitations associated with biokinetic modelling and the TDS data from LMICs, these results suggest that the contribution of dietary lead intake to global lead exposure is in the region of 40 to 50%, suggesting, in turn, that dietary lead intake is likely a major global driver of lead poisoning. Lead absorbed from the environment into food crops is expected to be the key driver of dietary lead. Current regulatory levels for maximum lead concentrations in foods (0.05-0.3 mg/kg) are out-of-date and may imply a dietary lead intake of 200 g/day, far higher than the FDA reference level (2.2 g/day). Collecting representative TDS data in high lead burden countries should be a priority. Further research is also recommended on upstream lead sources and pathways of lead uptake in plants, driving global food contamination.

Diarrheal disease remains a leading cause of morbidity and mortality among children under five globally, with poor sanitation and hygiene accounting for over 88% of diarrhea and malnutrition burden. In Kenya, diarrhea ranks third in under-five mortality, particularly affecting arid and semi-arid regions. School-Led Total Sanitation (SLTS), adapted from Community-Led Total Sanitation (CLTS), uses pupils as change agents for household hygiene knowledge transfer. However, SLTS effectiveness in addressing diarrhea and malnutrition has not been evaluated in Kenya. This study assessed SLTS effects on diarrheal disease and nutritional outcomes among children aged 5-59 months in Baringo County. A pre- and post-test quasi-experimental design with nonequivalent control groups was employed in Mogotio (intervention) and Baringo South (control) sub-counties. Using multistage sampling, 440 children aged 6-59 months were enrolled. The six-month SLTS intervention included capacity building, school health club formation, triggering activities using Participatory Rural Appraisal tools, Information, Education, and Communication materials distribution, continuous sensitization, and household monitoring. Data were collected at baseline and three months post-intervention using electronic questionnaires and anthropometric measurements. Nutritional status was assessed using WHO Anthro software z-scores for length/height-for-weight (HWZ), and weight-for-age (WAZ) to determine wasting, and underweight prevalence. Chi-square analysis assessed intervention-control differences. Baseline and endline socio-demographic characteristics were comparable between groups. At endline, no significant nutritional outcome differences were observed: wasting prevalence was at 15.0% versus 16.4% ({chi}{superscript 2}=0.155, df=1, p=0.694) while underweight was 12.3% versus 13.6% ({chi}{superscript 2}=0.181, df=1, p=0.670). However, diarrheal disease prevalence significantly reduced in intervention versus control groups: 5.9% versus 13.2% ({chi}{superscript 2}=6.738, df=1, p=0.009), representing a 53% reduction. SLTS intervention showed no significant effect on nutritional outcomes but demonstrated a significant reduction in diarrheal disease among children aged 6-59 months. These findings provide strong evidence for integrating school-based sanitation and hygiene approaches into broader public health strategies addressing diarrheal diseases.

Dengue is a mosquito-borne, viral disease of increasing public health significance. Currently, most public health interventions target the vector, with efficacy dependent on timing within the season. Whilst seasonal profiles have been characterised in some endemic settings a global assessment is lacking. Here, we develop and apply a proportion-based measure of dengue seasonality to reported case time series from 1990 to 2024 across 106 countries and territories, the largest assessment of this phenomenon to date. We identify regional differences in seasonality such that every month of the year saw cases peak in at least one country or territory. Latitude was identified as influencing seasonality, with cases peaking between March and April in the southern hemisphere and July and October in the northern hemisphere. Equatorial locations displayed flat seasonality, and amplitude increased with distance from the equator. K-means clustering identified three seasonal profile types: two with pronounced seasonal outbreaks (with distinct peak timing and shape) and one with flatter, more endemic transmission. Peak month timing covaried among locations within the same seasonality cluster, with phase differences meaning that information on shifts in peak timing may be available several months in advance in some settings, of potential significance for prediction and intervention planning. Beyond aiding public health planning, identification of seasonal clusters suggests that information on dynamics in one location could be leveraged to improve forecasting power in others with similar seasonal dynamics.

Context. Survival of low-birth-weight (LBW) neonates depends heavily on modifiable nursing care processes at the bedside, making the newborn unit a decisive site for improvement. Evidence linking measurable care-process conditions to outcomes, and the provider experience that explains them, remains limited in Kenyan county referral settings. Aim. To examine the care-related determinants of severe adverse outcomes among LBW neonates and the provider perspectives that explain them, framed for newborn-unit nursing practice and quality improvement. Methods. A convergent mixed-methods design was applied at Kericho County Referral Hospital. Quantitatively, 169 LBW neonate-mother pairs were analysed; care-process indicators (skilled personnel at admission, warm-chain maintenance, shortage of essential drugs/feeds, and referral/outborn status) were related to a composite severe adverse outcome using Pearson chi-square tests and crude odds ratios (OR) with 95% confidence intervals (CI). Qualitatively, key-informant interviews with newborn-unit providers were analysed thematically and coded to care-process themes; strands were integrated for practice. Findings. A severe adverse outcome occurred in 136/169 neonates (80.5%). Skilled personnel (94.7%) and warm-chain practices (92.9%) were near-universal, whereas 58.6% of neonates faced shortages of essential drugs/feeds and 49.1% were referred (outborn). Shortage of essential drugs/feeds (OR = 2.26, 95% CI [1.04, 4.90], p = .036) and referral/outborn status (OR = 2.25, 95% CI [1.01, 5.00], p = .043) were significantly associated with higher odds of a severe outcome. Warm-chain care was statistically associated but in a counterintuitive direction (OR = 4.81, p = .006), consistent with confounding by indication, while skilled-personnel availability was not associated (p = .834). Provider narratives converged on seven care-process themes: staffing and workload, warm-chain maintenance, infection prevention, drug and equipment availability, referral coordination, monitoring and documentation, and caregiver/transport barriers around the first hour of care. Conclusion. Care-related conditions-commodity supply, referral readiness, thermal care, infection prevention, and monitoring-are clinically modifiable levers that shape whether vulnerable LBW neonates stabilize or deteriorate, even where they do not all retain independent statistical significance after adjustment. Recommendations. Newborn units should protect nurse staffing norms, secure consistent supply of essential neonatal commodities, standardize pre-referral stabilization and thermal-care protocols, and strengthen structured monitoring and documentation, supported by competency-based training and county-level policy. Keywords: low-birth-weight neonates; newborn unit; nursing care processes; warm chain; infection prevention; referral coordination; drug and equipment shortage; quality improvement; provider perspectives; mixed methods

Histidine-rich protein 2 (HRP2)-based rapid diagnostic tests (RDTs) are central to malaria case management in Africa but fail when Plasmodium falciparum parasites lack the pfhrp2 or pfhrp3 genes. Widespread deletions have been reported in Eritrea, Ethiopia, and Djibouti, yet no systematic data have been available from Somalia. Between May and October 2023, we collected 7148 dried blood spot (DBS) samples from patients with suspected malaria attending eight health facilities across seven regions in Somalia. Field HRP2/pan-lactate dehydrogenase (LDH) RDTs and microscopy were performed, and DNA was extracted from 301 RDT-positive and 173 RDT-negative DBS samples. A multiplex quantitative PCR assay targeting pfldh, pfhrp2, and pfhrp3 was used to identify deletions in pfldh-positive samples lacking pfhrp2 or pfhrp3 amplification, with mixed infections inferred from delta cycle threshold ({Delta}Ct) differences. Of 474 analysed samples, 301 (4.2%, 95% CI 3.7-4.7) were RDT or microscopy positive, and 159 (33.5%) were confirmed pfldh-positive by qPCR. Among these, six (3.8%, 95% CI 1.4-8.1) carried pfhrp2 deletions and 59 (37.1%, 95% CI 29.6-45.1) carried pfhrp3 deletions. Eleven infections (6.9%, 95% CI 3.5-12.1) produced discordant RDT outcomes, HRP-/LDH+ or RDT-negative despite pfldh positivity. Deletions were most frequent in Dolow, Luq, and Bosaso. A single isolate carried the pfk13 R622I mutation, confirming the first report of the emergence of an artemisinin partial resistance-associated in Dolow, Gedo region, Somalia. Pfhrp2/3 deletions causing false RDT results remain low in Somalia and the confidence interval overlaps with the 5% policy threshold for changing RDTs, indicating uncertainty that warrants larger-scale assessment. Pfhrp3 deletions are widespread and compromise the diagnostic redundancy of HRP2-based tests. Most deletion-carrying parasites remain detectable through the pan-LDH line, minimising immediate clinical risk but leading to systematic misclassification of P. falciparum as non-falciparum malaria. These findings support the continued use of HRP2/Pan-LDH RDTs but highlight high risk areas and emphasise the need for periodic and expanded molecular surveillance for prevalence trends to guide timely future diagnostic policy.

Background The 2022-2023 global mpox outbreak highlighted the critical need for robust genomic surveillance capabilities to track mpox virus (MPXV) evolution and transmission dynamics. Methods Building upon our established SARS-CoV-2 sequencing infrastructure, we implemented a Molecular Loop probe-based long-read sequencing approach using Pacific Biosciences Sequel II technology for comprehensive MPXV genomic surveillance across the United States (US). From August 2024 to June 2025, we generated 326 high-quality whole genome sequences from residual mpox-positive clinical specimens collected by Labcorp across all 10 US Department of Health and Human Services regions. Results Our analysis identified two samples containing clade Ib MPXV in January and June 2025 and captured shifting trends in clade IIb diversity, with 13 distinct lineages observed. We also identified multiple instances of large (~1.6-17.6kb) deletions proximal to the inverted terminal repeats in clade IIb genomes. APOBEC3 mutation analysis indicated substantial evidence of human-to-human transmission among both clades. Further, we observed significantly higher APOBEC3-associated SNPs per kilobase (P<0.001) in clade IIb genomic variable regions relative to their central conserved region. Our assay exhibited strong reproducibility across biological replicates from individual patients and accuracy was confirmed via parallel sequencing of select specimens by US Centers for Disease Control and Prevention (CDC) using metagenomic sequencing. We also demonstrated via custom simulation that our assay discriminates all known MPXV clades and lineages, including those we have not observed in the US. Conclusions Our integrated nationwide surveillance system facilitates real-time genomic tracking of outbreak evolution, with demonstrated capacity across SARS-CoV-2 and MPXV, positioning this platform for rapid deployment during future pathogen emergence.

Stochastic epidemic models are a cornerstone of infectious disease epidemiology and are often used to study intervention scenarios. However, large run-to-run variability can make intervention effects difficult to estimate precisely. We revisit the epidemic Sellke construction, which assigns each individual an infection threshold for the cumulative infection hazard such that, conditional on the thresholds, the epidemic trajectory becomes deterministic. This enables coupling of simulations with and without an intervention, yielding low-variance effect estimates even when outcomes such as final size or peak incidence vary widely between runs. We develop an exact, event-driven implementation that maintains infection and recovery events in priority queues. Cumulative infection-hazard updates require O(log N) time per event, yielding overall complexity O(Elog N) for E events in a population of size N. The implementation achieves computational performance comparable to the classical Gillespie algorithm while naturally accommodating non-Markovian infectious periods and complex infectiousness profiles. We illustrate the approach using distance-dependent spread of avian influenza between poultry farms in the Netherlands and a multilayer population with households, schools, and workplaces. In both examples, coupling enables efficient within-run comparisons of intervention scenarios across stochastic realisations.

We present FootNet, a 453-image multi-view smartphone foot dataset for binary foot segmentation, with expertannotated masks across six anatomical views (dorsal, medial, and plantar, both left and right). We benchmark four segmentation models under a controlled protocol: U-Net with a MobileNetV2 encoder achieves the best performance (IoU 0.9268, Dice 0.9608, 95 % CI [0.9209, 0.9320]); DeepLabV3 with MobileNetV3-Large scores IoU 0.8984 (Dice 0.9449); UNet++ with MobileNetV2 scores IoU 0.8913 (Dice 0.9391); and SAM ViT-B with oracle boundingbox prompt scores IoU 0.9219 on the matched 191-image subset. Bonferroni-corrected Wilcoxon signed-rank tests (k = 6 comparisons) show U-Net significantly outperforms DeepLab (p < 0.001, r = 0.638) and SAM ViT-B with oracle boundingbox (p = 0.005, r = 0.202); UNet++ does not significantly differ from DeepLab (p = 0.062). Connected-component postprocessing yields negligible benefit (mean {triangleup}IoU = +0.0003, 12 of 453 images improved). The extended dataset is available upon request

Background: Opioid use disorder (OUD) is heritable, yet most genome-wide association studies (GWAS) have focused on European populations, leaving the genetic architecture of OUD in non-European populations underexplored. Methods: We conducted GWAS of OUD across three ancestries using electronic health records and genomic data from 52,357 All of Us Research Program participants (8,912 cases; 43,445 matched opioid-exposed controls; 48.5% female). Participants were stratified into European (EUR), African (AFR), and Admixed American (AMR) ancestry groups for logistic regression GWAS, with independent replication in the Million Veteran Program. We then applied the deep-learning model AlphaGenome to predict the tissue-specific transcriptomic and splicing consequences of top risk variants across 13 reward-pathway brain regions. Results: We identified and replicated a novel DDX6 risk locus, alongside established OPRM1 and FURIN signals. AlphaGenome predicted the DDX6 regulatory allele downregulates the stress-resistance gene FOXR1 in the nucleus accumbens, while the protective OPRM1 variant (rs1799971) upregulates OPRM1 expression across reward networks. Other signals of interest included IL6R and SHISA9 (EUR); GHR (AFR); and ASTN2 (AMR). Conclusions: This study identifies DDX6 as a novel OUD risk locus, replicates associations with OPRM1 and FURIN, and highlights biologically plausible ancestry-specific signals in AFR and AMR populations. We also replicated top variants in an independent population. Finally, integrating GWAS with deep-learning annotations provides specific, localized biological hypotheses to guide future experimental validation and targeted therapeutics.

Background: Monoclonal antibodies have emerged as a prophylactic strategy to prevent symptomatic SARS-CoV-2 infection in immunocompromised individuals. However, the evolutionary and clinical implications of breakthrough infections under this regime remain unclear. Methods: A male in their 80s with a haematological/oncological diagnosis received a 2000 mg intravenous infusion of sotrovimab in March 2023 and was diagnosed with COVID-19 by RT-qPCR from a nasopharyngeal swab in August 2023. Weekly samples (n=24) were collected through February 2024 (171 days). All samples underwent whole-genome sequencing, with select mutations subjected to functional assessment. Findings: Sequencing identified the GE.1 lineage at all timepoints. An intra-host recombination event in ORF1ab (positions 8942-12458) was detected prior to 23 weeks post-detection, followed by a 14-fold increase in viral load (7.42e+06 to 1.00e+08 RNA copies/mL) and a marked shift in the viral population. E340D, a sotrovimab resistance mutation, was detected at low abundance (46%) within the first week post-infection, fluctuated over time, and was nearly fixed by week 15 (107 days) post-detection. We assessed five spike mutations - V36M, S98F, and V213G in the N-terminal domain, Y505P in the receptor-binding domain, and P681Q near the S1/S2 cleavage site - and additionally evaluated the impact of E340D. V36M conferred the highest infectivity across all cell lines, with the most significant effect in low-TMPRSS2 cells. While all mutations showed enhanced infectivity with the addition of E340D, the effect was most pronounced in mutations with lower baseline infectivity. The addition of E340D significantly decreased relative neutralizing titres for V36M, S98F, and V213G, enabling escape from neutralizing antibodies in XBB-responsive individuals, illustrating an enhanced phenotypic advantage. Patient neutralizing activity was absent pre-sotrovimab, and sotrovimab-induced neutralization was further compromised by selection of E340D. Interpretation: Sotrovimab pre-exposure prophylaxis in an immunocompromised patient did not prevent SARS-CoV-2 infection, and selected for resistant mutation E340D, with unexpected fitness consequences across non-receptor binding domain spike regions.

Estimating dengue force of infection (FOI) is essential for understanding transmission dynamics and targeting intervention programmes, yet surveillance data in endemic settings required for estimations are often incomplete, with varying formats. We developed a Bayesian hierarchical catalytic model that jointly fits age-stratified case data, aggregate case data, and seroprevalence surveys within a single framework, incorporating external covariates to improve parameter identifiability. Synthetic validation showed that covariates alone recovered accurate FOI point estimates even when most districts contributed only aggregate data, but did so with poorly calibrated uncertainty; anchoring the model with a single seroprevalence survey was necessary to bring credible interval coverage close to nominal. Applied to 128 districts across Java and Bali, Indonesia (2016-2024), the model revealed substantial spatial heterogeneity in FOI and reporting rates. Many districts in Java exceeded the WHO-suggested seroprevalence threshold for vaccine introduction, yet were classified as low-priority when using reported incidence as prioritisation criterion, particularly in areas with weak surveillance. Model-based seroprevalence estimation, integrating multiple data sources, offers a more consistent basis for identifying high-priority districts for vaccine introduction, and is less susceptible to surveillance bias than reported incidence.