NAR Molecular Medicine

Background: Biallelic variants in GFM2, encoding mitochondrial elongation factor G2 (mtEFG2), a GTPase involved in the termination stage of mitochondrial translation, cause autosomal recessive combined oxidative phosphorylation deficiency. Noncoding structural variants may be missed by exome sequencing but can disrupt splicing and provide opportunities for variant-specific therapeutic rescue. We investigated the molecular mechanism underlying suspected Leigh syndrome in an infant with mitochondrial disease and evaluated whether splice-switching oligonucleotide (SSO) treatment could correct the pathogenic splicing defect. Methods: The proband underwent exome sequencing followed by short-read and long-read whole genome sequencing. RNA sequencing, reverse-transcription PCR, quantitative PCR, and cycloheximide treatment were used to characterize the effect of the identified intronic duplication on GFM2 splicing and transcript stability. Patient-derived fibroblasts were treated with SSOs targeting the aberrant splice junction. Rescue was assessed by RNA studies, western blotting, and spectrophotometric measurement of cytochrome c oxidase (COX). Results: Whole genome sequencing identified a paternally-inherited GFM2 missense variant, NM_032380.5:c.2195C>T p.(Pro732Leu), in trans to a maternally-inherited 221-nucleotide intronic duplication, NM_032380.5:c.2029-741_2029-521dup. RNA studies revealed a 87-nucleotide pseudoexon, generated by activation of a cryptic acceptor splice site within the duplicated sequence. The resulting transcript harbored a premature termination codon (PTC) and underwent nonsense-mediated decay, as confirmed by cycloheximide rescue. Together with reduced mtEFG2 protein levels on western blot, the findings supported a loss-of-function mechanism. Enzymatic analysis of affected fibroblasts showed reduced activity of the mtDNA-dependent complex IV subunit COX, with preservation of the nuclear-encoded complex II enzyme succinate dehydrogenase and the control enzyme citrate synthase, consistent with impaired mitochondrial translation. A SSO targeting the aberrant intron-pseudoexon junction nearly abolished pseudoexon inclusion, restored correctly spliced GFM2 transcript from the duplication-containing allele, increased mtEFG2 protein levels, and significantly improved COX activity. Conclusions: This study identifies a pathogenic intronic GFM2 duplication that causes mitochondrial disease through pseudoexon activation and nonsense-mediated decay. The findings demonstrate the value of integrated genome and transcriptome analysis for exome-negative mitochondrial disease and provide in-vitro proof of concept that SSOs can restore transcript processing, protein expression, and mitochondrial respiratory-chain function in patient-derived cells.

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.

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.

Background: Citrin deficiency, caused by biallelic pathogenic variants in SLC25A13, must be identified early to prevent serious complications such as hyperammonemia and liver failure. However, clinical diagnosis is often delayed due to its nonspecific presentation and limited sensitivity of amino acid-based newborn screening methods. Although genome-based evaluations are being investigated to address these issues, concerns about their cost, turnaround time, variant interpretation ability, and data handling highlight the need for a more practical yet reliable alternative. We investigated the feasibility of applying proteomic approach on dried blood spots (DBS), which are routinely used in newborn screening. Methods: We performed untargeted liquid chromatography-tandem mass spectrometry to analyze the proteome of DBS using a previously developed "non-targeted analysis of non-specifically DBS-absorbed proteins" (NANDA) workflow. SLC25A13 protein abundance was quantified in individuals with biallelic loss-of-function mutations, compound loss-of-function/missense mutations, and heterozygous carriers; this was also evaluated in healthy and diseased controls representing relevant differential diagnoses. To leverage proteomic information, we derived a multivariate proteomic signature using feature selection and evaluated its performance with leave-one-out cross-validation. Biological relevance was assessed by enrichment analysis, and complementary transcriptomics was performed using RNA sequencing. Results: A total of 7,474 proteins, including SLC25A13, were consistently detected in DBS. SLC25A13 was undetectable in individuals with biallelic loss-of-function mutations. However, individuals with compound loss-of-function/missense genotypes showed reduced but measurable SLC25A13 levels, comparable to those observed in heterozygous carriers. In contrast, a compact 15-protein signature accurately identified individuals with compound loss-of-function/missense genotypes (AUC, 0.99; sensitivity, 1.00; specificity, 0.95). The signature was enriched for Ca2+-response, and transcriptomics showed downregulation of genes related to multimodal ion channels in affected individuals compared to controls. Conclusions: DBS-based proteomic profiling may assist in the diagnosis of citrin deficiency through SLC25A13-quantification and a biologically plausible multivariate signature. More broadly, this strategy offers a promising new diagnostic layer for protein disorders, providing a proteomic readout in a clinically practical DBS format with potential utility for future diagnostic and screening applications.

Precision medicine aims to advance our ability from a "one-size-fits-all" approach to personalized and predictive healthcare across diverse populations. It promotes integration of multi-omics and phenotypic data to understand disease mechanisms and discover novel biomarkers and risk factors, which could be used to predict and prevent critical diseases in individual patients across diverse populations. The potential implications of precision medicine approach can accelerate our ability to classify patients at higher risk of developing critical diseases, improve diagnostic capabilities, develop deeper understanding of individual risk, investigate racial differences and demographic characteristics, and find relationships between genetic variants, expressions, and diseases. This study focuses on implementing an innovative and data driven framework of translational bioinformatics and Machine Learning (ML) techniques to analyze multi-omics, including RNA-seq and Whole-Genome Sequencing (WGS) data, generated using blood samples of randomly consented patients. First, we utilized bioinformatics pipelines to identify differentially expressed genes and their pathogenic and likely pathogenic variants for the downstream data analysis, annotation, and visualization. Then, applied a nexus of ML models for multi-omics biomarker discovery, disease prediction, density-based clustering, single-patient profiling, and pathogenicity classification. WGS data analysis supported the exploration of genetic variation and diversity among patients to identify known and novel biomarkers, whereas RNA-seq data analysis improved our understanding of functional and biological pathways that underlying disease states. We classified and clustered pathogenic variants and expressions across various genes and discovered numerous diseases leading risk factors. Our results include gene-disease associations and captured common pathways across the broader population, demonstrating a level of sensitivity and accuracy that has broad clinical implications. We validated our results through clinical records, and state of the science literature. This study delves into the strengths of multi-omics data integration and capabilities of ML application in genetically diverse and complex patient cohorts. Our approach has the potential to elucidate complex gene-disease interactions for genetically diverse populations, which can support earlier diagnoses for patients in many disease realms.

Background: RNA sequencing (RNA-seq) is increasingly recognized as a complementary tool to DNA-based sequencing for improving the diagnostic yield in Mendelian disorders. However, how the diagnostic performance of RNA-seq varies across molecularly and phenotypically distinct patient subgroups remains poorly defined. This study aimed to evaluate and compare the diagnostic utility of RNA-seq across three stratified groups of patients with non-diagnostic exome sequencing. Methods: We performed RNA-seq on whole blood samples from 90 patients with suspected Mendelian disease in whom clinical exome or whole-exome sequencing had failed to establish a molecular diagnosis. Patients were prospectively stratified into three groups of 30: (i) patients with a candidate variant of uncertain significance (VUS) with predicted splicing impact (Group 1), (ii) patients with a specific clinical pre-diagnosis but no identified pathogenic variant (Group 2), and (iii) patients without a specific pre-diagnosis or candidate variant (Group 3). Aberrant splicing, gene expression outliers, and allele-specific expression were analyzed using multiple bioinformatic tools and compared against a GTEx-derived control cohort. Results: RNA-seq contributed to a molecular diagnosis in 29 of 88 evaluable patients (32.9%). Diagnostic yield differed substantially across groups: 82.8% (24/29) in Group 1, 6.9% (2/29) in Group 2, and 10% (3/30) in Group 3. In Group 1, RNA-seq enabled reclassification of candidate VUS through direct demonstration of aberrant splicing events. In Group 2, RNA-seq identified a somatic mosaic ACTB variant missed by exome sequencing and reclassified a previously deprioritized APPL1 VUS. In Group 3, a deep intronic pseudoexon-activating variant in IGBP1 was identified in two siblings with severe microcephaly, providing evidence for a candidate X-linked microcephaly gene, and a pathogenic RNU4-2 variant was detected in a patient with ReNU syndrome, a non-protein-coding gene not captured by standard exome sequencing. Conclusions: RNA-seq has the highest diagnostic utility when applied to evaluate candidate splice variants identified by prior DNA testing but also provides independent diagnostic value in patients without candidate variants. The systematic comparison across stratified patient groups supports the integration of RNA-seq into clinical genomic workflows and highlights the need for standardized analytic frameworks.

Currently, the genetic architecture of Middle Eastern populations is underrepresented in global genomic databases. This gap increases the rate of Variants of Uncertain Significance (VUSs) and clinical misinterpretations of genomic data especially in Middle Eastern populations. Whole exome sequencing was conducted on 90 healthy individuals from Jordan and the data were analysed using Principal Component Analysis (PCA) and multi-computational filtering. PCA revealed a double ancestry (EUR-AFR) admixture rather than a triple admixture (EUR-AFR-AMR). More than 3,500 populations-specific variants (PSVs) were identified, of which 72% were singletons. Additionally, 19 variants were significantly enriched compared to the maximum allele frequencies in public global databases (Fisher's exact test with Benjamini-Hochberg false discovery rate correction, p-value < 0.05). Consequently, the results suggest the reclassification of variants of Uncertain Significance (VUS) which reside in the ECE2 gene to likely benign and the variants of Conflicting Classification of Pathogenicity in the genes IL1RN and THPO to benign based on the significant allele frequency (AF=0.0389, p-value < 0.05). Furthermore, a pathogenic ClinVar variant was identified in a healthy individual, warranting careful interpretation. The findings underscore the importance of identifying PSVs in order to minimize or even prevent clinical misdiagnosis and highlight the unique genetic signature in Jordan. The study serves as a foundational resource for precision medicine in the region.

In the United States, sickle cell disease (SCD) is a rare inherited hemoglobinopathy affecting about 100,000 individuals, mostly with African ancestry. SCD causes damage to multiple organ systems and SCD nephropathy (SCDN) is a common complication associated with early mortality. We previously performed a genome-wide association study (GWAS) for SCDN and identified a modest number of genome-wide significant loci. Here, we leveraged the ancestral composition of participants from two well-characterized adult SCD cohorts to boost statistical power and perform a local ancestry-aware GWAS for estimated glomerular filtration rate (eGFR), resulting in the identification of novel genome-wide significant loci within the African (AFR) and European (EUR) ancestral components of participants. Meta-analysis identified 12 significant genomic regions in the AFR tract, including PPIL6, ARHGAP24, RAB11A, and STEAP3, and 38 regions in the EUR tract, including UBLCP1, ADAMTS6, JAZF1, MYO7B, MYO1C, PDGFA, GPC5, LRP1B, KANK1, and TRPV5. The identified regions encompass genes affecting inflammation, extracellular matrix (ECM) integrity, iron metabolism, magnesium ion homeostasis, B cell apoptosis, tumor necrosis factor (TNF) production, and estrogen signaling. Many of these genes and pathways are important not only for renal function, but also for SCD biology, providing additional support for the hypothesis that SCDN pathophysiology is unique from other forms of kidney disease. This study represents the largest local ancestry-aware analysis of SCDN to date, furthers our understanding of the genetic risk factors underlying SCDN, and proposes new targets that could be useful for the early identification and treatment of kidney dysfunction in SCD patients.

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.

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.

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.