Evolution

Background Bacterial fitness is shaped by interactions between genome variation and environmental context, yet how these interactions determine its predictability and heritability remains unclear. In the clinically important pathogens of Klebsiella pneumoniae, a leading cause of hospital-acquired infections, this question is particularly pressing. Despite extensive genomic characterization, we still lack a systematic understanding of how genome-wide variation translates into fitness across diverse environments in K. pneumoniae. Methods We filled this gap by profiling a systematic collection of 1,462 clinical K. pneumoniae isolates across 214 diverse environmental and pharmacological stress conditions using high-throughput chemical genomics. Fitness was quantified from colony growth and integrated with whole-genome sequencing data. Genome-wide association analyses identified genetic determinants of fitness, and machine learning models incorporating genomic features were used to predict fitness.Results Fitness exhibited a strongly environment-dependent genetic architecture, with modest but significant concordance between genetic background and phenotypic variation. Under antibiotic and stress-combination conditions, fitness was driven by discrete, high-effect determinants, including known resistance genes, resulting in stronger signals and improved predictability. In contrast, non-antibiotic environments showed more polygenic and distributed architectures with weaker associations. Genome-wide analyses identified both established and previously uncharacterized genes linked with fitness across conditions. Resistance and virulence determinants exhibited clear context-dependent trade-offs, conferring fitness advantages under selection but imposing costs in non-selective environments. Consistent with this, plasmid carriage showed environment- and genotype-dependent fitness effects, with benefits under antibiotic pressure and measurable costs otherwise. Genomic variant-based models for fitness prediction achieved moderate performance (Mean Spearman correlation ({rho}) = 0.36 (95% CI: 0.18-0.67) for predicted versus observed values in unseen data) across conditions, with improved accuracy under strong antibiotic selective pressures, and produced well-calibrated prediction intervals with high coverage. Despite strong population structure effect on predictions, models captured predictive gene and SNP biomarkers for fitness. Conclusion These findings highlight that bacterial fitness is an emergent property of genome-environment interactions rather than a fixed attribute of genotype. This work establishes a unified high-dimensional genotype-phenotype framework linking genomic variation to fitness across diverse conditions in a major pathogen, with broader implications for other pathogenic bacterial species.

Amitriptyline is commonly prescribed for chronic pain, yet treatment response and tolerability vary substantially. Genetic variation in CYP2C19 and CYP2D6 influences amitriptyline metabolism, but evidence linking pharmacogene status to clinical outcomes in chronic pain is limited. Amitriptyline is typically prescribed for chronic pain at lower doses than for depression, which may reduce pharmacogenomic effects on clinical outcomes. We analysed 1,146 participants with chronic pain from the Australian Genetics of Depression Study who reported amitriptyline use, treatment outcomes, and genotype data. Metaboliser phenotypes were assigned using PharmCAT. Associations with self-reported effectiveness and discontinuation due to side effects were examined using regression models adjusted for age and sex. Only CYP2C19 intermediate metabolisers showed nominally lower odds of discontinuation and reduced likelihood of reporting moderate effectiveness. Overall, pharmacogenetic phenotypes were not significantly associated with patient-reported amitriptyline outcomes in chronic pain, potentially reflecting the lower doses typically prescribed for pain management.

Background: Pretest genetic counseling (GC) is recommended in conjunction with genetic testing (GT) for cardiovascular (CV) indications, yet access to CVGC is limited leading to delayed GT. Posttest GC could increase GC and GT access but requires efficient pretest education that supports both informed GT decision-making and robust GT uptake. Methods: We developed four indication-tailored online CV genetics education videos and deployed them in a 3-arm randomized trial comparing pretest vs. posttest outpatient CVGC (RESEQUENCE-GC, NCT05422573). Participants were 1:1:1 randomized to pretest video education plus an optional (efficiency arm) or required (flipped arm) phone call with a genetic counselor and planned posttest CVGC or to standard pretest CVGC (SOC arm). Questionnaires administered at baseline and post-education included the CV Multidimensional Model of Informed Choice [MMIC] to quantify GT knowledge and informed GT choice. Results: 389/767 (50.7%) adults aged 18-80 (mean 51.2{+/-}14.9 years) scheduling a first CVGC appointment consented to RESEQUENCE-GC and completed the baseline questionnaire. Efficiency arm participants (video education + optional phone call) were most likely to complete pretest education (134, 97.4% efficiency; 107, 85.6% flipped; 111, 87.4% SOC, p=0.0012) and elect GT (131, 95.6% efficiency; 105, 84.0% flipped; 107, 84.2% SOC, p=0.0036). Few (4, 2.9%) efficiency arm participants requested an optional pretest phone call. Most flipped arm participants (90, 84.1%) had no post-video questions, consistent with the 97 second [IQR: 65s-145s] median call duration. CV genetics knowledge was high post-education (median 8 [IQR 7,8]/8 MMIC items correct). Only video-based pretest education was associated with a significant increase in knowledge (p<0.0001). Nearly all participants made an informed GT choice with no difference between intervention (95.6%) and SOC (90.4%) arms (p=0.074). Conclusions: Tailored, online video pretest education can enhance CV GT uptake, support informed GT decision-making, and be integrated into efficient pretest workflows, suggesting utility in scalable posttest CVGC.

Background Preterm birth (PTB) rates among Hispanic/Latina individuals in the United States have risen over the past decade. Data suggests this rise may be driven in part by psychosocial stress. Leukocyte telomere length (LTL), a marker of cumulative cellular aging that shortens under chronic stress, may capture stress-related biological vulnerability, but has not been examined as a potential population-level contributor to PTB in Hispanic/Latina pregnancies. Objective To examine the association between mid-pregnancy maternal LTL and PTB in a population-based Hispanic/Latina cohort. Methods In a case-control study nested within a California singleton birth cohort (n = 436 Hispanic/Latina individuals; 215 PTB, 221 term births), LTL was measured by quantitative PCR from biobank specimens collected from 15 to 20 weeks of gestation. Covariates from linked birth certificate and hospital discharge records were included. Logistic regression estimated ORs and 95% CIs of PTB by LTL examined continuously and by percentile category (<=10th, 11th-89th, >=90th) with and without adjustment for covariates. Results Mean and median LTL did not differ between PTB and term births. LTL at or below the 10th percentile was associated with elevated odds of PTB relative to full-term birth (12.6% versus 4.3%; ORc = 3.2, 95% CI 1.3-7.9), persisting after partial (ORadj1 = 3.2, 95% CI 1.3-8.3) and full covariate adjustment (ORadj2 = 3.4, 95% CI 1.3-9.3). Subgroup analyses showed consistent directional patterns across PTB subgroups and for early term birth (ORadj2 = 5.1, 95% CI 1.5-17.0). Conclusions Mid-pregnancy maternal LTL <=10th percentile was associated with more than three times the odds of PTB, with risk concentrated at the extreme low tail of the distribution. Consistent with a cumulative allostatic load model, markedly short LTL at mid-gestation may reflect elevated stress-related biological risk for preterm delivery. These findings support upstream investment in stress reduction and prospective LTL research in high-burden populations.

Abnormalities in the gut microbiome, intestinal permeability, and the gut-immune-brain axis are increasingly linked to neuropsychiatric disorders, neurodegenerative disorders, inflammatory bowel disease (IBD), and other immunologic/autoimmune conditions. We investigated these phenomena in 128 youth with Pediatric Acute-Onset Neuropsychiatric Syndrome (PANS) and individuals with autism spectrum disorder (ASD) and other neurodevelopmental disorders (NDD) characterized by profound, unexplained deteriorations/regressions in developmental, neuropsychiatric, and behavioral functioning. Previous studies we have carried out showed that immune dysregulation and DNA damage response (DDR) gene mutations are implicated in a subset of these patients. The current study examines the role of genetic variants affecting intestinal homeostasis. We report a series of patients exhibiting both neuropsychiatric deterioration and gastrointestinal symptoms. Genetic analysis identified ultrarare (minor allele frequency < 0.001) pathogenic or likely pathogenic variants in eight genes primarily expressed in the intestines and associated with IBD, dysbiosis, or intestinal permeability. Across thirteen patients, mutations were identified in DUOX2 (n=4), SLC10A2 (n=2), UNC45A, TTC7A, LGALS4, SI, CCR9, MEP1B, and BACH2. While these findings suggest a potential role for genetic variants governing intestinal homeostasis in these cases of neuropsychiatric decline, their presence in only a small subgroup necessitates larger, prospective cohorts to determine whether these variants are statistically significant and play a definitive role in the pathogenesis of these disorders.

This work focuses on the global and partial identification problem for fractional differential equations. We provide a general numerical procedure based on global and local optimization algorithms with two refinements for biological systems that ensure solution positivity and homogeneous parameter units. The method is applied to a new fractional model of Dengue outbreak called the Fractional Homogeneous Nishiura (FHN) model, calibrated using data of newly infected people in Cape Verde. We show that our identification method yields a better fit between data and model solutions than previous approaches and that our FHN model captures the dynamics of Dengue more closely than existing systems.

Introduction Spinal muscular atrophy (SMA) is a monogenic neuromuscular disease caused by mutations in the survival motor neuron 1 (SMN1) gene. Onasemnogene abeparvovec is a U.S. FDA-approved single-dose gene therapy for SMA. Both its intravenous formulation (Zolgensma, approximately USD 2.13 million per patient) and intrathecal formulation (Itvisma, around USD 2.59 million per patient) are prohibitively expensive, substantially limiting accessibility in low- and middle-income countries (LMICs). We conducted a clinical study of vesemnogene lantuparvovec, an alternative to onasemnogene abeparvovec developed for use in LMIC settings. Methods Sixteen patients with SMA, including 8 with type 1 SMA and 8 with type 2 SMA, received a single intrathecal administration of vesemnogene lantuparvovec. Eleven patients were treated with a low dose (1.5 * 10^14 vg) and five with a high dose (3.0 * 10^14 vg). The primary endpoints were safety and efficacy, assessed by changes from baseline in developmental gross motor milestones according to the World Health Organization criteria. Overall survival was primarily evaluated in type 1 SMA patients. This trial was registered with ClinicalTrials.gov NCT06288230. Results As of the March 2026 cutoff date, 15 of 16 treated patients had completed at least 12 months of follow-up after treatment, while the remaining one type 1 SMA patient died of disease progression at month 6 post-treatment. At 12 months post-treatment, among the surviving 7 patient with type 1 SMA, the median age was 21.6 months (range, 16.1 to 32.3 months). Among the 16 treated patients, the median age at diagnosis was 4.4 months (range, 0.0 to 18.0 months), and the median age at dosing was 10.7 months (range, 2.8 to 22.5 months). All patients experienced at least one AE. Thirty-one AESIs were reported in 13 patients, including hepatotoxicity, thrombocypenia-related events and cardiac events. No patient required prolonged prednisolone prophylaxis. SAEs, including pneumonia, lower respiratory tract infection, upper respiratory tract infection, and haemorrhagic diarrhoea, occurred in 5 of 8 (63%) patients with type 1 SMA and 2 of 8 (25%) patients with type 2 SMA. Two patients with type 1 SMA required invasive ventilation, and one of whom subsequently died. At 12 months post-treatment, 11 of 16 treated patients (69%) gained at least one new WHO motor milestone versus baseline, including 3 type 1 and 8 type 2 SMA patients; one type 2 patient gained six WHO motor milestones and achieved independent walking. Conclusions In patients younger than 24 months of age with type 1 or type 2 SMA, a single intrathecal dose of vesemnogene lantuparvovec was safe and generally well tolerated and was associated with improvements in developmental gross motor milestones compared with outcomes observed among referred but untreated patients. Additional studies are required to further evaluate the long-term safety and efficacy of this gene therapy.

OBJECTIVES To quantify socioeconomic inequalities in antibiotic prescribing for common infections in primary care, and assess whether these inequalities arise from differences in consultation frequency, prescribing behaviour, or variation in vaccination uptake, smoking, and body mass index. DESIGN Population based cohort study. SETTING Primary care data from Clinical Practice Research Datalink, England. PARTICIPANTS 17,195,399 children and adults estimated to have been registered with a general practice in 2019. MAIN OUTCOME MEASURES Antibiotic prescribing rates (prescriptions per person-year), consultation rates (consultations per person-year), and probability of receiving an antibiotic prescription following consultation. RESULTS Higher deprivation was associated with higher antibiotic prescribing rates for most respiratory tract indications. In children, prescribing rates were 44.8% (95% confidence interval [CI] 41.9% to 47.7%) higher for upper respiratory tract infections and 47.6% (95% CI 44.2% to 51.3%) higher for lower respiratory tract infections in the most versus least deprived twentile. In adults, prescribing rates for lower respiratory tract infections were 22.7% (95% CI 21.4% to 24.1%) higher in the most deprived twentile. Prescribing rates for other indications showed weak, U-shaped, or negative associations with deprivation. Prescribing inequalities were primarily driven by inequalities in consultation rates rather than probability of receiving antibiotics once consulted. Lower influenza vaccination uptake partly accounted for higher consultation rates for respiratory infections among more deprived children, while smoking prevalence contributed to inequalities among adults. CONCLUSIONS Socioeconomic inequalities in antibiotic prescribing vary by indication type and are largely explained by consultation frequency. Reducing inequalities may require interventions that decrease the need to consult, e.g. improving influenza vaccination coverage in children and reducing smoking among adults, rather than focussing solely on prescribing behaviour.

Yang et al. recently published a systematic comparison of genetic effects on disease susceptibility and disease-specific mortality across nine common diseases and seven biobanks, concluding that susceptibility and survival architectures overlap only modestly. This is an important resource, but we argue that the current mortality genome-wide association studies (GWAS) require explicit power calibration before limited overlap can be interpreted biologically. Using two-sample Mendelian randomization (MR) with positive-control exposures, we show that even a well-powered positive control, body mass index (BMI), instrumented by 855 genome-wide-significant variants, produces a clearly detectable effect for heart failure (HF) mortality, with only weaker evidence for chronic kidney disease (CKD) mortality. However, when BMI instruments were stratified into quartiles by exposure-association strength, the heart failure association remained nominally significant only in the two strongest quartiles and was not significant in the two weakest quartiles. Further, using household income as a weakly instrumented socio-economic contrast has insufficient power to detect moderate effects on any disease mortality outcome. These analyses indicate that current disease mortality GWAS may be insufficiently powered to detect shared effects. In contrast, the same BMI instrument set produced large and directionally coherent effects when applied to case-control GWAS of the matched six diseases, with the HF and prostate cancer associations preserved under a within-family BMI sensitivity analysis, and nominal support for CKD. The HF mortality association was also preserved in a within-family BMI sensitivity analysis. Similarly, genetically proxied household income was associated with HF risk in the case-control GWAS despite null associations with disease-specific mortality, consistent with limited power in the mortality GWAS. These findings indicate that the limited BMI-mortality evidence across several outcomes is unlikely to reflect a weak BMI instrument or dynastic artefacts alone and instead supports limited effective power in current disease-mortality GWAS.

ABSTRACT OBJECTIVE: We performed a systematic review and meta-analysis (SRMA) of post-surgical outcomes, comparing chlorhexidine gluconate (CHG) versus povidone iodine (PI) for vaginal antisepsis of major gynecologic procedures. DATA SOURCES: Ovid Medline, Embase, Scopus, Embase, Cochrane, and Clinicaltrials.gov were searched between 1986 and December 2023, for studies comparing CHG with PI for vaginal antisepsis of major gynecologic operations. STUDY ELIGIBILITY CRITERIA: We included Randomized Controlled Trials (RCTs) and non-RCTs comparing CHG to PI for vaginal antisepsis of major gynecologic operations. The primary outcome was surgical site infections (SSIs) and the secondary outcome was urinary tract infections (UTIs) and vaginal irritation. METHODS: Summary estimates were calculated by fixed effects models when I2 [&le;] 25% and by random effects models when I2 > 25%. Statistical analysis was performed using RevMan 5.4.1. The protocol for this systematic review was registered on PROSPERO (ID CRD42022378101). RESULTS: Nine studies met the inclusion criteria, four of which were randomized controlled trials (RCTs). 9538 patients were included, 4300 (45%) of whom were allocated to CHG and 5238 (55%) to PI. No statistically significant difference in SSI incidence was found for vaginal antisepsis with CHG versus PI in pooled analyses (n= 9538 patients; RR 1.20; 95% CI 0.92-1.57; I2 =0%). In contrast, a significantly higher risk of UTIs was observed for vaginal antisepsis with CHG than with PI (n=6061 patients; RR 1.48 95% CI 1.03-2.14; I2 = 0%). CONCLUSION: In our SRMA, there were no significant differences in SSI risk when either CHG or PI was utilized for antiseptic vaginal preparation. Interestingly, vaginal antisepsis with PI was associated with a lower incidence of post-operative UTIs following major gynecologic surgery. Our findings support current guidelines that form of vaginal antisepsis can be used for SSI prevention. They also suggest that PI may result in fewer postoperative UTIs but further randomized studies are needed to support these findings. Key words: surgical site infection, surgical wound infection, urinary tract infection, urogynecologic surgery, Chlorhexidine, Povidone Iodine, surgical antiseptic,

Background Artificial intelligence-enhanced electrocardiography (AI-ECG) enables scalable, low-cost cardiac dysfunction screening, but existing models are annotation-intensive and predominantly adult-derived, leaving paediatric generalizability uncertain. Paediatric cohorts exhibit highly variable cardiac morphology and function compared to adults, which may be useful for learning generalizable AI-ECG models. Methods We pretrained ECG-Fyler on a predominantly paediatric, all-age cohort at Boston Children's Hospital (1992-2023), annotated with a cardiology-specific coding system (Fyler codes), and evaluated it on assessments from echocardiography (echo) and cardiac magnetic resonance (CMR) studies. We validated on an external adult cohort from Columbia University Irving Medical Center. Performance was benchmarked against several AI-ECG foundation models by AUROC across age groups, lesion types, and limited-data scenarios. Findings The pretraining cohort comprised 782,138 ECGs from 255,271 patients (median age: 10.9 years, IQR: [2.8-16.8]). Internal evaluation included 178,495 ECG-echo pairs (median age: 10.9 [3.7-17.0]) and 8,584 ECG-CMR pairs (median age: 20.7 [15.6-29.6]). External validation included 82,543 ECG-echo pairs from adults (median age: 64.0 [52.0-74.0]). ECG-Fyler improved AUROC across biventricular dysfunction and dilation tasks, with the largest gains in low-data settings. In internal validation, ECG-Fyler detected low left ventricular ejection fraction (LVEF [&le;] 40%) from only 100 fine-tuning samples (AUROC: 0.80, 95% CI: [0.78-0.80]), outperforming other models (AUROC < 0.65) and improving with additional fine-tuning (AUROC: 0.94 [0.93-0.94]). Similar improvements were observed for CMR-derived LVEF, RVEF, and ventricular dilation. In external validation on adults, ECG-Fyler exhibited an AUROC of 0.83 (CI: [0.82-0.85]) for LVEF [&le;] 40%. After fine-tuning on less than 10% of external data, LVEF [&le;] 45% performance (AUROC: 0.87 [0.86-0.88]) outperformed a fully trained, site-specific prior model (AUROC: 0.85 [0.84-0.87]). Interpretation Pretraining on richly annotated, paediatric-dominant ECGs yields models that transfer efficiently across institutions and ages, supporting AI-ECG screening and triage when labels or imaging access are limited. Funding National Institutes of Health (R01LM012973); Kostin Innovation Fund, Boston Children's Hospital

The Epic Sepsis Model version 2 (ESMv2) is a prediction model embedded into the electronic medical record used to warn clinicians which hospitalized patients are at risk for sepsis. We conducted a retrospective cohort study of 31,951 hospitalizations of 25,760 patients to compare analyses conducted at the commonly used patient-level (where a maximum prediction prior to the onset of sepsis is used to measure performance) vs novel prediction-level (where each prediction is used to measure performance). Sepsis, defined by the Sepsis 3 criteria occurred during 1,049 hospitalizations (3.3%). Patient-level analyses suggested excellent discrimination AUC 0.86; [IQR 0.85, 0.87], whereas prediction-level analyses demonstrated lower performance AUC 0.62; [IQR 0.57, 0.65]. Low estimates of the positive predictive value (14.5% at the patient level vs 4% at the prediction level) imply a high number of false alerts. Common evaluation approaches may overstate the performance of dynamic prediction models and mislead clinical decision-making.

Background: Despite the success of combination antiretroviral therapy (cART), vascular comorbidities, including cerebrovascular disease, are more prominent in people living with HIV (PLWH) compared to people without HIV (PWOH). However, quantitative assessments of cerebrovascular morphometry and their associations with cognitive outcomes in the context of HIV are still limited. In this study, we explore this missing link. Methods: Magnetic Resonance Angiography (MRA) data, blood markers, and neurocognitive assessments were collected from 73 PWOH subjects (male: 57, female: 16; age: 53 {+/-} 16) and 99 PLWH subjects (male: 66, female: 30, age: 53 {+/-} 11). Vessel morphometric features were quantified using intraCranial Artery Feature Extraction (iCafe) to investigate associations between vessel morphometry, markers of monocytes, endothelial cell activation, and cognitive performance. Results: HIV status predicted a lower total number of branches ({beta} = -0.224, p = 0.001, d = -0.517) and shorter total distal length ({beta} = -0.173, p = 0.021, d = -0.370) with a moderate effect size. Total branch number was found to be negatively associated with plasma levels of monocyte markers (sCD14: r = -0.167, p = 0.033; sCD163: r = -0.157, p = 0.045) and positively correlated with white matter cerebral blood flow (r = 0.550; p [&le;] 0.05). HIV status was the strongest predictor of overall cognitive performance in ANCOVA model ({beta} = -0.219, p = 0.006, d = -0.453). Conclusions: Our results suggest that cognitive impairment in PLWH is associated with vessel morphology metrics. Monocyte immune activation may contribute to changes in vessel morphology.

Patients with primary immunodeficiency (PID) often face prolonged diagnostic delays and may increasingly turn to large language models (LLMs) to interpret their symptoms during this period. We evaluated whether an LLM could recognize PID from symptom descriptions derived from interviews with 21 PID patients. In a prior study, we showed that GPT-4o identified PID in 96% of cases when prompted with physician-written patient histories (Rider et al., JACI, 2024). Here, when prompted with symptom descriptions in patients' own words, GPT-5 identified PID in only 7 cases (33%), although it more broadly suggested immune system issues in 18 cases (81%). The gap between these findings indicates that LLMs are sensitive to the language and framing of symptom descriptions, performing substantially worse when patients describe their own symptoms in everyday language than when clinicians summarize patient histories in structured medical terms. This study underscores the need to carefully evaluate how LLMs are used in patient-facing applications.

Background Atrial fibrillation (AF) is a leading cause of stroke, cardiovascular morbidity, and mortality. Atrial myopathy, characterized by progressive metabolic, electrical, and structural changes, creates the arrhythmogenic substrate that drives AF. Defining the key drivers of atrial myopathic processes is essential for targeted therapies that can mitigate AF progression. Here we explore how reduced ERBB4 expression contributes to the development of left atrial myopathy. Methods We analyzed the Cleveland Clinic Biobank to compare left atrial ERBB4 levels in patients grouped by AF diagnosis. To investigate the impact of reduced ERBB4 levels on atrial tissue substrate, we created mouse models of cardiac-specific Erbb4 deficiency using Mlc2a (myosin light chain 2a)-Cre. Comprehensive physiological assessments were performed. Transcriptomic analyses of the left atrium were performed in an Erbb4 haploinsufficient mouse model and compared with human atrial datasets. Molecular validation of key dysregulated pathways was performed. Results We found that left atrial ERBB4 levels are reduced in patients with AF. Adult cardiomyocyte-specific Erbb4 heterozygous (Erbb4fl/+;Mlc2a-Cre) mice exhibited prolonged P-wave duration in the absence of ventricular dysfunction. Left atrial transcriptomic analysis in Erbb4 haploinsufficient mice showed upregulation of pathways related to fibrosis, apoptosis, and coagulation, and downregulation of pathways related to fatty acid metabolism and mitochondrial function, mirroring changes observed in pressure overload mouse models. A cross-species transcriptomic comparison revealed significant overlap between ERBB4-correlated gene expression and functional pathways in adult human atria and mice with Erbb4 haploinsufficiency. Validating the transcriptomic data, protein and functional assays demonstrated increased fibrosis, apoptosis, and oxidative stress in the mutant left atrial tissue. Conclusion Left atrial ERBB4 levels are reduced in AF patients. A mouse model of Erbb4 deficiency and human atrial transcriptomic analyses highlight a role for ERBB4 in supporting normal atrial metabolism while protecting against inflammation, apoptosis, and fibrosis.

Background: Frontotemporal dementia (FTD) lacks widely accessible disease-specific biomarkers. Optical coherence tomography (OCT) and OCT angiography (OCTA) may provide non-invasive measures of retinal changes associated with neurodegeneration. We conducted a systematic review and meta-analysis evaluating retinal biomarkers in FTD compared with Alzheimer disease (AD) and controls. Methods: A systematic search of PubMed and Embase was conducted through April 25, 2026 according to PRISMA guidelines. Studies evaluating OCT/OCTA biomarkers in FTD with comparator groups were included. Inverse weighted random-effects models, publication bias assessments, and meta-regressions were performed. Results: Ten studies involving 139 individuals with FTD, 87 with AD, 29 with mild cognitive impairment, 14 with TDP-43 proteinopathy, 5 with tauopathy, and 255 controls were included in the systematic review; five studies were eligible for meta-analysis. Compared with AD, individuals with FTD demonstrated significantly thinner retinal nerve fiber layer (RNFL) thickness (SMD = -0.61, 95% CI -0.98, -0.24). Compared with controls, individuals with FTD exhibited significantly thinner ganglion cell layer-inner plexiform layer (GCL-IPL) thickness (SMD = -0.55, 95% CI -1.02, -0.08), whereas pooled analyses across multiple retinal biomarkers were non-significant (SMD = -0.19, 95% CI -0.52, 0.14). RNFL thickness correlated negatively with female % in FTD and positively with age in both AD and controls. Conclusions: Individuals with FTD exhibit lower RNFL thickness than AD and lower GCL-IPL thickness than controls, suggesting retinal alterations may reflect neurodegeneration. However, larger longitudinal studies with standardized OCT/OCTA protocols are needed to determine the diagnostic and prognostic utility of retinal biomarkers in FTD

Lead is a toxic metal ubiquitous in our environment. While dramatic reductions in lead sources have paralleled equivalent decreases in lead-poisoning rates, chronic lead exposure remains a critical public health concern. Childhood lead exposure (at its lowest levels) is liked to changes in cognitive development but less is known about lead's effects on children's brain structure, especially as a result of in utero exposure. We measured prenatal and early-postnatal lead exposure in shed deciduous teeth of 448 9- and 10-year-old children (from 20 United States cities) and linked those lead levels to childhood brain structure, cognition/behavior, and neighborhood- and family-level socioeconomic characteristics. Here we show negative associations between tooth-lead levels and the thickness of the brain's cortex, particularly in regions linked to language processing. With increasing tooth-lead levels, children of lower-income (versus higher-income) families showed steeper declines in receptive vocabulary. Caregiver-reported behavioral problems exhibited similar associations. With in utero exposure linked to adverse neurodevelopmental outcomes (well before lead exposure and its risks are evaluated by healthcare professionals), prenatal screening of maternal lead levels/exposure, coupled with recommended strategies to reduce its placental transmission, may help reduce lead's effects on future generations.

Background: Conventional psychiatric screening instruments summarize symptoms within individual scales and prioritize cases with high single-instrument additive score severity. This design treats items as independent within instruments and ignores cross-instrument covariance structure, making it insensitive to respondents whose responses are distributed across multiple domains in unusual combinations that remain below threshold on every individual scale. Methods: We analyzed two cohorts spanning older and younger adults. Item prompts from depression, stress, anxiety, and sleep instruments were embedded into a shared semantic space using a pretrained sentence encoder. Principal component analysis of the item-prompt embeddings alone---with no use of respondent data at this stage---was used to construct a low-dimensional subspace retaining 80\% of variance in the item embedding matrix. Normalized participant responses were then projected into this subspace, with Jaccard-based stability analysis used as a check on dimensional robustness. Multivariate deviation from the cohort norm was quantified with Mahalanobis distance using Ledoit-Wolf covariance regularization. Candidate outliers were defined by the empirical 95th percentile of the cohort-specific distance distribution. To isolate response configurations not already captured by conventional single-instrument extreme-value logic, we excluded all outlier respondents who had endorsed any individual item at the maximum value of its Likert scale on any instrument. For the remaining outliers, anomalous components were backtracked to their original item loadings for interpretation. Results: In the older-adult Health and Retirement Study (HRS) cohort, principal component analysis of 27 item-prompt embeddings showed that a 10-dimensional subspace provided a stable representation of cross-instrument semantic structure. In the younger-adult Xinxiang cohort the corresponding stable solution was 16-dimensional. In each cohort, seven respondents remained as multivariate outliers despite falling below every single-instrument extreme-value threshold. These cases were not characterized by uniformly severe symptom scores but by unusual cross-domain response configurations that became visible only in the shared semantic covariance subspace. The response structure of the retained configurations differed across cohorts: older-adult cases more often involved weak endorsement of mood-labeled items alongside nonzero body- and sleep-related responses, whereas younger-adult cases more often involved incomplete response configurations spanning mood, sleep, stress, and self-harm-related items. Conclusions: A semantically aligned, auditable covariance subspace provides a practical tool for flagging unusual multivariate response configurations that single-instrument additive screening may not flag. The method is interpretable at the level of original item contributions. It should be understood as a hypothesis-generating screen for unusual response configurations requiring further clinical assessment, not as a diagnostic instrument. Outcome validity remains to be established by prospective study.

Hybrid mechanistic-statistical models offer interpretability and adaptability for short-term seasonal epidemic forecasting, but it remains unclear whether their accuracy depends more on increased biological complexity or on the assimilation of richer data. Using eight retrospective influenza seasons in North Carolina, we evaluate whether training on historical data and assimilating auxiliary emergency department (ED) visit data improves four-week-ahead hospital admission forecasts more than adding biological complexity (multi-subtype structure and cross-season immunity). Hierarchical Bayesian training on historical data improves accuracy by 22.4 % (95 % CI: 16.4-28.1 %), and inclusion of ED visit data yields a further 5.3 % (95 % CI: 3.0-7.6 %) improvement, whereas added biological complexity produces diminishing or null gains. We further observe a substitution effect in which ED visit data partially compensates for omitted biological structure. We deployed a simplified model variant in the 2025-2026 CDC FluSight Challenge and ranked among the top ensemble performers, supporting the robustness of Bayesian hierarchical training in real time. Together, these findings indicate that short-term forecast accuracy is driven more by historical learning and assimilating auxiliary signals than by biological fidelity, with implications for how forecasting systems should balance mechanistic complexity.

Background: Non-communicable diseases (NCDs) represent a critical public health challenge in Kenya, responsible for over 50% of inpatient admissions and 40% of deaths. While digital health tools and artificial intelligence offer promising ways to improve prevention, diagnosis, and management, little is known about how these tools are perceived and used in practice. There is limited research exploring the views and lived experiences of young people in Kenya, who are a strategic priority for NCD prevention because behavioral risk factors are established in this window, and for Community Health Providers (CHPs) who provide health services within the community. This study aims to address this gap by examining the perspectives of the burden of non-communicable diseases and the potential role of digital health technologies, including artificial intelligence, for preventing and managing these conditions in these specific populations. Methods: A qualitative research design using focus group discussions (FGDs) was employed in Nairobi (urban) and Busia (rural) counties between March and July 2024. Eight FGDs were conducted with 60 participants purposively sampled from three stakeholder groups: community health promoters (CHPs), healthcare workers (HCWs), and youth aged 18-35 years. A semi-structured guide, co-developed with a Community Advisory Board, explored beliefs about NCDs, health-seeking behaviors, lifestyle practices, and attitudes toward digital health and AI. Audio recordings were transcribed verbatim, translated where necessary, and analyzed thematically using grounded theory principles on NVivo software (v12). Results: Six consolidated themes emerged: (1) understanding of NCDs and perceived risk; (2) barriers to NCD prevention and care; (3) the role of CHPs; (4) adoption of AI tools for NCD management; (5) trust, ethics and access concerns; and (6) community-driven recommendations for AI integration. Significant barriers including stigma, economic constraints, and barriers to care were documented alongside enthusiasm for AI tools among youth and CHPs in both urban and rural areas. Conclusion: This study shows that AI tools are being used for NCD prevention and management through spontaneous community adoption. However, it emphasizes the need for culturally relevant, equitable, and community-driven solutions. Effective scaling requires the identification and bridging of digital literacy gaps, the establishment of affordable infrastructure, the protection of data privacy, and the integration of artificial intelligence tools into existing community health frameworks. This process should involve the collaboration of trusted intermediaries, such as CHPs and community leaders, to ensure successful outcomes. Future initiatives should prioritize participatory design, policy frameworks for ethical governance, and targeted capacity building to enhance acceptance and sustainability of digital health innovations in low- and middle-income country settings.