Journal of Extracellular Vesicles

Background: Immune-oncology has revolutionized cancer treatment, but some patients fail to benefit due to primary resistance and tumour-immune evasion. Extracellular vesicles (EVs) are secreted by both tumour and immune cells and mediate communication between cancer cells and the immune system. Our study used proteomic profiling of circulating EVs collected from NSCLC patients treated with immune checkpoint inhibitors (ICI) to identify predictive biomarkers of response as well as immune evasion mechanisms related to treatment resistance. Methods: EVs were isolated from plasma collected prior to ICI treatment using peptide-affinity purification and high-throughput proteomics was performed using Proximal Extension Assay. Differentially expressed EV proteins between durable (DR) and non-durable responders (NDR) were identified and evaluated using Cox proportional hazards regression, survival analysis, sex-stratified analysis, as well as pathway and network analysis. Results: Proteomics analysis identified 116 differentially expressed EV proteins between DR and NDR. NDR was characterized by enrichment of inflammatory, angiogenic, and immune-suppressive EV proteins, such as IL1RL1, TFRC, IL6ST, galectins, TNF superfamily death receptors, chemokines, and PCSK9. Pathway analysis revealed enrichment of angiogenesis, chemotaxis, ECM remodeling, and neutrophil degranulation associated with poor progression-free survival (PFS). In contrast, DR to ICI treatment was associated with EV proteins related to T- and B-cell activation and adaptive immunity. Sex-related differences in abundance and association with PFS was observed for certain EV proteins, including IL1RL1 and TFRC. A six protein EV model (IL1RL1, TFRC, ERI1, CCN5, IGFBPL1, and TNFRSF13C) demonstrated good prognostic performance for identifying NDR (AUC = 0.907) and stratified patients into three discrete risk groups. Conclusions: High-plex EV proteomics revealed biologically coherent tumour-immune signaling programs that are associated with ICI treatment resistance. Profiling circulating EVs may improve our understanding of EV-mediated immune evasion mechanisms and identify protein signatures that reflect the tumour immune microenvironment and predict response to immune checkpoint blockade.

Joint modelling of longitudinal data using non-linear mixed effects models and time-to-event outcomes provides a suitable framework to account for informative censoring when estimating biomarker dynamics and quantifying event risk using covariates and longitudinal trajectories. Their usefulness in clinical research depends on data collection design, particularly to precisely estimate the association (link) parameter between longitudinal and survival processes. However, optimal design strategies have so far been addressed separately for longitudinal and survival endpoints and remain unexplored for joint models. We propose two Fisher Information Matrix (FIM) computation methods for joint models, relying on Monte-Carlo integration over observations combined with either Markov Chains Monte-Carlo or Adaptive Gaussian Quadrature to integrate random effects. Their accuracy is assessed against clinical trial simulations in an oncological example based on the HORIZON III study with a tumour-growth-survival model including discrete and continuous covariates. We apply these methods to quantify the impact of follow-up duration, sampling richness, sample size, and covariate distribution on parameter uncertainty and test power. In our example, longitudinal-parameter uncertainty is barely affected by follow-up duration or sampling richness, whereas survival-parameter uncertainty decreases substantially from 1-year to 2-year follow-up. The number of subjects needed (NSN) to achieve <15\% uncertainty on the link parameter is comparable for a 2-year rich design and a 3-year sparse design. Optimal covariate distributions are stable across designs and systematically improve test power, outperforming longer and richer but non-optimised designs. These FIM-based methods accurately predict uncertainty and test powers, enabling design evaluation and NSN computation for joint-model-based clinical studies.

In some countries, melatonin is sold without a physician prescription and dosage is unregulated. Transdermal products have become popular including those marketed for children. We measured consumer assumptions about these products among adult residents of the United States, analyzed lot-to-lot variability, and compared the pharmacokinetics of melatonin administered in oral, lotion, and bath product forms. Survey respondents (n=199) believed oral melatonin was more effective than transdermal products and that all melatonin products were relatively safe. Melatonin lotion products analyzed by HPLC displayed lot-to-lot variability as well as changes in formulation and product claims. To determine pharmacokinetics, three different treatments (oral tablets, lotion, and bath immersion) were administered to twelve undergraduate participants in a randomized, crossover design. Five additional participants completed bath product treatment only. Participants collected saliva samples up to 48 hours after administration, which were analyzed for melatonin by enzyme-linked immunosorbent assay. Oral (n=11) and lotion formulations (n=12) caused maximum salivary melatonin levels within 30 minutes after administration, but bath immersion did not cause increases in saliva melatonin (n=17). The half-life of oral melatonin was 1.17 [0.69 -- 1.65] hours versus 5.72 [3.75 -- 7.68] hours for lotion treatment (p = 0.011, effect size r = 0.770). Melatonin lotion may pose a risk to consumers who assume it is safe and less effective than oral tablets, when in fact it may be very potent and remain at high physiological levels into the following day. This study is registered on clinicaltrials.gov (NCT06382610) and was funded by the Sleep Research Society.

Background: Severe cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS) are major dose-limiting toxicities of chimeric antigen receptor (CAR) T-cell therapy. Existing pre-infusion biomarkers offer modest discrimination, motivating non-invasive alternatives. Methods: We prospectively enrolled 26 patients with relapsed/refractory large B-cell lymphoma receiving axicabtagene ciloleucel. Pre-infusion (day -1) exhaled breath samples were analyzed by gas chromatography-mass spectrometry for 40 volatile organic compounds (VOCs). Candidates with univariate AUC > 0.65 for severe (grade >=2) CRS or ICANS were carried forward to sensitivity-maximization-at-given-specificity with LASSO regularization (SMAGS-LASSO), which selected separate panels for each outcome. Model performance was assessed by leave-one-out cross-validation with permutation p-values and Harrell bootstrap optimism correction. Results: The 4-VOC CRS panel (heptanal, benzaldehyde, 2-butanone, ethylbenzene) achieved LOOCV AUC 82.5% (80% sensitivity at 88% specificity) and the 3-VOC ICANS panel (nonanal, allyl methyl sulfide, levomenthol) achieved AUC 86.3% (67% sensitivity at 86% specificity). By tertile, severe CRS occurred in 8/9 (89%) high-risk versus 2/9 (22%) low-risk patients (Cox HR 6.82, 95% CI 1.41-32.9, p=0.017) and severe ICANS occurred in 8/9 (89%) versus 2/9 (22%) (HR 8.28, 95% CI 1.73-39.6, p=0.008). Each 1-SD score increase corresponded to a 3.80-fold higher hazard of severe CRS (p<0.001) and 4.36-fold higher hazard of severe ICANS (p<0.001). In head-to-head comparison, the 3-VOC ICANS panel outperformed the modified Endothelial Activation and Stress Index (mEASIX) (delta-AUC +0.36, DeLong 1-sided p=0.008). The 4-VOC CRS panel had numerically higher AUC than mEASIX (delta-AUC +0.19, p=0.150). Conclusions: Pre-infusion exhaled breath VOC panels stratify CAR T-cell recipients by severity and timing of severe CRS and ICANS, providing a non-invasive complement to existing serum biomarkers. Multi-institutional validation is warranted.

Purpose: To characterize peripheral immune alterations in treated birdshot uveitis (BU) patients using high-dimensional mass cytometry and multiplex serology. Design: Cohort study. Subjects: 36 BU patients on immunomodulatory treatment (IMT) and 31 healthy controls (HCs). Methods: Detailed ophthalmologic examinations were performed, and peripheral blood and serum samples were collected for immune profiling using mass cytometry and multiplex cytokine analysis. Main Outcome Measures: Imaging-based indicators of ocular inflammation; peripheral immune cell frequencies; serum cytokine levels. Results: Compared to HCs, BU patients showed increased frequencies of Th17, CD146+ T cells, intermediate effector/central memory T cells co-expressing CXCR3 and CCR4, CD56dim NK cells and elevated IL-18 levels. Patients were clinically stratified by an expert ophthalmologist into three disease activity groups: Inactive, Active (comprising combinations of surface retina, deep retina and choroid activity) and Burned-out. Inactive patients harbored more quiescent effector T cells, e.g. Tim-3+ Tc17-Tc22 intermediates and more CD8+ TSCM, potentially representing a resting pool of autoimmune T cells. Active patients exhibited increased in vivo activation of relevant T cells, with stronger HLA-DR, CD38 or PD-1 expression, and highest levels of CD56dim NK cells. Immune profiles were also linked to treatment subgroups: csDMARDs (conventional synthetic disease-modifying antirheumatic drugs) were associated with higher CD56bright NK frequencies, and absence of therapy showed elevated PD-1/SLAMF7 Tc17+1 and PD-1CD57 CD8 TEMRA cells. IL-6R blockade (tocilizumab) resulted in loss of IL-6R T-cells accompanied by increased SLAMF7 T cells, due to epitope masking. Conclusions: Peripheral CyTOF profiling anchored to thorough clinical stratification revealed disease activity-associated immune signatures and therapy-associated imprints in BU.

Background: Mocravimod is an oral sphingosine-1-phosphate (S1P) receptor modulator. This Phase 1 multiple-ascending-dose study evaluated its safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) in healthy volunteers. Methods: In this double-blind, randomized, placebo-controlled, parallel-group trial, 60 healthy male volunteers were enrolled in five cohorts. Mocravimod was administered once daily at 0.3, 0.6, 1.2, or 3.0 mg for 14 days, or at 2.0 mg for 28 days. Safety assessments included adverse events (AEs), laboratory tests, vital signs, electrocardiography, and Holter monitoring. PK of mocravimod and its active metabolite, mocravimod-phosphate, and PD effects on absolute lymphocyte count (ALC) and leukocyte subsets were assessed. Results: Fifty-nine of 60 participants completed the study. One participant in the 3.0 mg cohort discontinued treatment because of asymptomatic transaminase elevation. No deaths or serious AEs occurred. AEs were mostly mild or moderate, transient, and showed no clear dose relationship. Mocravimod produced dose-dependent reductions in ALC from 0.6 mg onward, with maximum geometric mean reductions of 65%, 74%, 83%, and 77% at 0.6, 1.2, 2.0, and 3.0 mg, respectively. ALC values recovered to above the lower limit of normal during follow-up in all cohorts. Holter monitoring showed an initial placebo-corrected reduction in heart rate of approximately 10-15 beats/min at doses of 1.2-3.0 mg, which attenuated with continued dosing. One participant in the 3.0 mg cohort had a recurrent daytime second-degree atrioventricular block (Mobitz I/Wenckebach), reported as a mild non-dose-limiting AE. No QT prolongation was observed. Exposure to mocravimod and mocravimod-phosphate increased approximately dose-proportionally. Steady state was reached by Day 14 (Day 28 in the 2.0 mg cohort), accumulation was approximately five- to sevenfold, terminal half-lives were approximately 100-40 hours for both analytes, and parent-to-metabolite exposure ratios were close to 1. Conclusions: Once-daily mocravimod up to 3.0 mg for 14 days and 2.0 mg for 28 days was generally well tolerated and showed predictable S1P-modulator class effects on lymphocyte counts and heart rate, with PK properties supporting once-daily dosing and further clinical development.

While prostate cancer (PC) is defined as immunologically cold, limiting the efficacy of immune checkpoint inhibitors, therapeutic vaccination targeting tumor-associated antigens represents an attractive strategy to promote disease control in low volume metastatic patients. The UV1 cancer vaccine is based on immunization with tripeptide fragments from human telomerase reverse transcriptase (hTERT) and a phase II clinical trial demonstrated induction of robust T cell response in men with de novo metastatic castration-sensitive prostate cancer (mCSPC). Comparison with long-term survival data of non-metastatic CSPC patients as reference showed that despite metastatic disease at diagnosis, UV1-treated patients who mounted an early vaccine-induced immune response achieved progression-free and overall survival comparable to non-metastatic patients. We examined biological determinants of clinical benefit following UV1 vaccination including tumor transcriptome and T cell receptor (TCR) profiling from circulating and tissue resident T-cells of the 22 men enrolled. Analysis of diagnostic and post-UV1 treatment biopsies revealed that low baseline exhaustion of T cells and higher CD8+ T cell abundance are associated with early immune response to the vaccine and longer survival. Moreover, we identified specific TCR motifs relative to early responders, that can indicate potential benefit from UV1 vaccination. These findings indicate that baseline intratumoral T cell exhaustion state and repertoire shape responsiveness to hTERT vaccination and long-term outcome. Overall, our study underlines how baseline immune profiling may be used as a companion biomarker to predict mCSPC patients most likely to benefit from therapeutic vaccination.

Biological aging is heterogeneous across organ systems, yet whether CT-derived abdominal aging provides prognostic value beyond routine clinical data and whether organ decomposition adds beyond a unified estimate remains untested. We developed and evaluated organ-specific and ensemble biological age models from radiomic features across five abdominal organs in 68,675 CT scans from 32,883 subjects, evaluated on alignment with chronological age of healthy subjects (nested cross validation: MAE=3.68 years, R^2=0.90). In sequential analyses restricted to adults aged 20-60 years which is the stratum of strongest BAG-disease association, ensemble biological age gaps provided incremental prognostic value beyond demographic covariates for all-cause disease and mortality (Delta C-index=0.141, 0.051) and beyond routine blood biomarkers (Delta C-index=0.048), confirming CT-derived aging captures structural information beyond laboratory markers. Organ-specific biological age added incremental prognostic value beyond ensemble selectively for focal diseases: cardiovascular (aorta, Delta C-index=0.091) and hepato-pancreatic (pancreas, Delta C-index=0.096). These findings establish a hierarchical organization of CT-derived biological aging, positioning routine CT as a source that adds prognostic value to existing clinical biomarkers.

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

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: Black and Latino adults in the United States experience a disproportionate burden of cardiometabolic conditions due to interacting behavioral, social, and structural drivers of health. Less is known about the impact of integrating digital health tools into CHW-led interventions to improve cardiometabolic health. This trial evaluates a multilevel community-digital health promotion model delivered by CHWs to improve service utilization, health behaviors and cardiometabolic health among Black and Latino adults. Methods: This community-partnered trial uses a randomized delayed-control group with a phased recruitment design. Four cohorts (N = 664) are enrolled through three community-based organizations (CBOs). Eligible participants are 18 years who self-identify as Black or Latino, and have prediabetes/diabetes, hypertension, or overweight/obesity. Participants are allocated to either (1) a multilevel intervention consisting of CBO and CHW capacity building combined with individualized CHW-led lifestyle coaching and group activities supported by digital tools, or (2) a delayed control group receiving SMS-only cardiometabolic health education. Data collected at baseline, 6, 9, and 18 months include surveys and health metrics. Qualitative data are collected from participants and community partners to assess intervention acceptability, implementation facilitators and barriers, and sustainability. Results: The primary outcome is health service utilization at 6 and 9 months. Secondary outcomes include health behaviors, health metrics, and social determinants of health. Sustainability of health behaviors and health metrics is assessed at 18 months. Conclusions: Findings will provide evidence to inform scalable, sustainable community-digital health models for CHW-supported cardiometabolic health interventions in underserved communities.

Intro: Characteristics of the pulse wave transmitted through the carotid arteries are predictive of cognitive decline and cerebrovascular health in humans. This study aimed to identify risk factor trajectories in childhood, adolescence and early adulthood that are associated with forward compression wave intensity (FCWI) in the common carotid artery in adults aged 28 years. Methods: Systolic blood pressure (SBP), body mass index (BMI) and fasting blood glucose (FBG) measured at multiple time-points when participants were aged between 8-20 years were included in a trajectory analysis. At age 28 years, FCWI was measured in 402 (M=206, F=196) participants who underwent a Duplex ultrasound assessment of the common carotid artery. Statistical analysis assessed differences in FCWI between each trajectory group for males and females separately. Results: In males, four trajectory groups were identified for BMI, three for SBP, and two for FBG. In females, three trajectory groups were identified for BMI, SBP, and FG. In males, having higher BMI (P=0.006), SBP (P=0.021) and FBG (P=0.002) from ages 8-20 years was associated with greater FCWI at age 28 years. In females, no associations were found between FCWI at age 28-years and trajectory groups for BMI (P=0.185), SBP (P=0.289) or FBG (P=0.070). Conclusion: Having high BMI, SBP and FBG throughout childhood, adolescence and early adulthood was associated with higher FCWI in the carotid artery at age 28 years in males, but not females. This may have a direct impact on the etiology of cognitive decline and cerebrovascular disease in later life.

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.