Alcohol, Clinical and Experimental Research

Importance: Preventing substance use disorder (SUD) is a top public health priority, but clinical practice currently lacks validated prognostic tools to identify risk before the onset of heavy use/problems. Objective: To determine whether models can predict SUD using genetic, behavioral, and environmental factors and to evaluate performance across substance types (alcohol, cannabis, tobacco), sex, and genetic similarity groups. Design: This prognostic study developed and validated a multi-domain risk model using data from four longitudinal cohorts (The National Longitudinal Study of Adolescent to Adult Health, Avon Longitudinal Study of Parents and Children, The Collaborative Study on the Genetics of Alcoholism, and FinnTwin12) with follow-up from early adolescence into young adulthood. Setting: Population-based and family-based samples from the United States, United Kingdom, and Finland. Participants: The analysis included participants with available genomic and behavioral data. The cohorts represented a spectrum of risk, ranging from nationally representative community samples to high-risk populations. Exposure(s) (for observational studies): Predictors included polygenic scores for substance use-related phenotypes and 10 established early-life behavioral and environmental risk factors. Main Outcome(s) and Measure(s): The outcomes were a diagnosis of moderate-to-severe SUD based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive value. Results: A total of 16,362 participants were included in the study (54.7% female; mean [SD] age 26.73 [3.36]). Genetic similarity group composition was 70.4% European, 16.8% African, 9.8% admixed American, 2.2% East Asian, and 0.7% South Asian. SUD prevalence was 22.0%. The integrated model achieved an AUC of 0.78 for predicting any SUD, with a sensitivity of 69% and a positive predictive value of 45%. Performance was comparable for specific substances (AUC range 0.74-0.82) and across genetic similarity groups (AUC range 0.61-0.82). The model demonstrated higher AUC in females compared with males. Conclusions and Relevance: Findings highlight the potential utility of an SUD prediction model to inform clinicians and patients about risk for developing a SUD.

Objective To evaluate the association of early addiction consultation with withdrawal pharmacotherapy and complications in hospitalized patients with alcohol withdrawal. Methods Retrospective single-center cohort of adult hospitalizations with alcohol withdrawal (diagnosis code or CIWA-Ar [≥]10 within 24 hours) from July 2020 to January 2026. Exposure was an addiction consult service order within 24 hours of arrival, compared with no or later consultation. Primary outcomes were intensive care unit (ICU) transfer after the first 24 hours and delirium tremens or withdrawal seizure not present on admission. Process outcomes included phenobarbital and thiamine administration within 24 hours and benzodiazepine dose in the first 48 hours. Secondary outcomes included length of stay and 30-day death or return to hospital. Results Of 4,663 hospitalizations, 1,379 (29.6%) received early consultation. After adjustment, early consultation was associated with lower odds of ICU transfer (odds ratio [OR] 0.73; 95% CI 0.54-0.99) and fewer episodes of delirium tremens (risk difference -1.09 per 100; -1.45 to -0.73), with no significant difference in withdrawal seizure. Early consultation was associated with phenobarbital (OR 1.57; 1.32-1.86) and thiamine (OR 1.23; 1.07-1.41) within 24 hours, higher 48-hour lorazepam-equivalent dose (ratio of geometric means 1.44; 1.32-1.57), shorter stays (ratio 0.92; 0.88-0.97), and lower odds of 30-day death or return to hospital (OR 0.78; 0.66-0.91). Conclusions Early addiction consultation was associated with earlier, more intensive withdrawal pharmacotherapy, lower odds of ICU transfer, and fewer episodes of delirium tremens, although the complication associations were sensitive to cohort definition.

Objective: Amphetamine (AMP) and its derivates are commonly prescribed for the management of attention-deficit/hyperactivity disorder (ADHD). This study aimed to 1) quantify AMP's transfer into human milk in lactating women receiving therapeutic doses and 2) contextualize infant exposure. Methods: Milk samples and dyad health histories of thirteen lactating women treated with varying doses of either mixed AMP, dextroamphetamine, or lisdexamfetamine were released for this study from the InfantRisk Human Milk Biorepository. Milk drug concentrations were quantified by validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) and used to calculate standard relative infant dose (RIDmat) and RID standardized to the defined daily dose (RIDDDD) of 15 mg AMP base. Results: Average milk concentrations (Cavg) of AMP base ranged from 23.3 to 297.0 ng/mL and increased linearly (R^2 = 0.92) with maternal daily dose. The RIDmat ranged 3.6-10.8% and RIDDDD ranged 1.4-21.8%. The fitted regression line crossed the 10% RIDDDD threshold at 1.17x DDD, with the upper 95% prediction limit crossing at 0.84x DDD. No adverse event reported by the mother-infant dyads were attributed to AMP exposure, notably including absence of reduced milk supply. Conclusions:The levels of AMP observed in human milk followed a dose predictable manner. The average patient was predicted to remain below the 10% RID DDD safety threshold at total daily doses at or below approximately 28 mg of mixed AMP salts or 59 mg of lisdexamfetamine. Ninety-five percent of patients were predicted to remain below the threshold at or below approximately 20 mg and 40 mg, respectively.

Importance: Anhedonia, social withdrawal and executive deficits represent transdiagnostic symptoms in depression, schizophrenia and addition, yet transdiagnostic brain volumetric alterations and their contribution to these symptom domains have not been systematically characterized. Objective: To determine common and disorder-specific gray matter deficits in patients with schizophrenia (SZ), major depression disorder (MDD) and alcohol use disorder (AUD) compared to heathy controls. Data Sources: A comprehensive literature search using three databases including PubMed, Web of Science and PsycInfo (between January 2000 to March 2024) as well as searches on review articles. Study Selection: Studies had to be published in English, reporting whole-brain results, comparing patients and healthy controls. Data Extraction and Synthesis: This meta-analysis adhered to the PRISMA guidelines and was pre-registered (https://osf.io/s57uq) prior to data analysis. Data was independently extracted and verified by 3 contributors, and statistical brain maps were generated using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) random-effects model. 147 VBM studies encompassing 90 Schizophrenia, 44 MDD and 13 AUD studies were finally included. Main Outcomes and Measures: Statistical maps of common and disorder-specific gray matter deficits in patients versus healthy controls are presented. Statistical inference utilized permutation tests, with threshold at p < .0025 and cluster extent of 10 voxels. Heterogeneity was evaluated using the I2 statistic and publication bias using Eggers regression test. Results: Capitalizing on a coordinate-based meta-analysis involving n=13,041 participants, we found shared GMV alterations in the ventromedial prefrontal cortex, ventral anterior cingulate cortex, insula, superior temporal gyrus. On the network level, we found robust functional interactions of the identified shared regions with the cortical midline structures as well as social and theory of mind-related behavioral functions. Subsequent characterization analyses highlighted the involvement of dopaminergic and serotonergic neurotransmitters as well as associated genetic annotations. Conclusions and Relevance: Overall, the present meta-analysis found common as well as separate GMV abnormalities in schizophrenia, major depressive disorder and alcohol use disorder suggesting a common neurostructural substrate and treatment target for social withdrawal and anhedonia across these disorders. Keywords: alcohol-use disorder, depression, meta-analysis, schizophrenia, voxel-based morphometry

Background: Psychiatric disorders pose a significant public health burden, and their complex genetic architecture underlies multiple biological pathways, contributing to a lack of diagnostic biomarkers. Laboratory-Wide Association Studies (LabWAS) leverage electronic health record laboratory data as intermediary phenotypes to link genetic risk to physiology. Methods: Polygenic Scores (PGS) for 25 health-related, psychiatric, and substance use phenotypes were generated for individuals of African (AFR; N=10,473) and European (EUR, N=35,131) ancestry from the Penn Medicine Biobank. Laboratory data was prepared using the LabWAS pipeline, encompassing 78 laboratory tests and 6 vital signs. Linear regression models tested the association of each lab value with PGS adjusting for sex, age, and ten within-ancestry genetic principal components. Results: Four PGS were significantly associated with at least one laboratory value in AFR individuals and 22 PGS in EUR individuals. In EUR individuals, psychiatric PGS partitioned into three physiological profiles: anxiety, attention-deficit/hyperactivity disorder, major depressive disorder, and post-traumatic stress disorder shared a metabolic/inflammatory signature (higher BMI, glucose, and monocytes; lower high density lipoprotein cholesterol and oxygen saturation); anorexia nervosa, obsessive compulsive disorder, and schizophrenia were characterized by lower BMI; and bipolar disorder demonstrated a more circumscribed profile with lower electrolytes and renal makers. Substance use disorder PGS similarly clustered: tobacco use disorder and externalizing showed metabolic/inflammatory profiles; alcohol-related phenotypes showed lower red blood cell indices; and cannabis use disorder had a more circumscribed profile. Conclusions: LabWAS reveals physiologic signatures associated with genetic risk for psychiatric disorders, offering new biological insights into their pathology.

Sepsis is a global health problem. Patients in the Intensive Care Unit (ICU) are at risk of having sepsis because of their vulnerability and use of invasive medical devices. Emerging antimicrobial-resistance complicates treatment. Understanding bacterial profiles and antibiotic sensitivity patterns in the ICU plays an important role in managing sepsis, optimizing antimicrobial use, and improving patient outcomes. The aim of this study was to show bacterial profiles and antibiotic sensitivity patterns among patients who were admitted to the ICU at Mbeya Zonal Referral Hospital for 12 months. This was a prospective cross-sectional study among 187 sepsis-suspected patients. Data analysis was performed using SPSS Version 27. Categorical data were summarized by frequency and percentage. Fishers exact test, chi-square test, and modified Poisson regression were used to show the association between patients clinical variables and culture positivity at a 95% CI. Statistical significance was set at a p-value < 0.05. Out of 187 samples, 41.7% were culture positive. Gram-negative bacteria were commonly isolated (65.4%, with Klebsiella pneumoniae and Escherichia coli being commonly isolated. Commonly isolated Gram-positive bacteria were Coagulase-negative Staphylococcus species (19%). 100% of gram-positive bacteria and 96% of gram-negative bacteria were Multi-Drug Resistant (MDR), respectively. In multivariable analysis, patients who were intubated and mechanically ventilated (aPR = 2.208, 95% CI: 1.133-4.305, p = 0.020) and post-laparotomy patients (aPR = 3.241,95% CI: 1.320-7.958, p = 0.010) independently had a higher proportion of Gram-negative bacteria. The study reveals a high level of resistance to commonly used antimicrobials in our setting, necessitating strengthening of the antimicrobial stewardship program and routine surveillance of antimicrobial resistance patterns.

Schizophrenia spectrum disorders (SSD) have been linked to immune dysregulation and altered intestinal barrier function, although findings across studies remain inconsistent. The present study investigated circulating markers of intestinal permeability and immune activation in individuals with early-stage SSD and healthy controls and examined their associations with clinical outcomes. Serum concentrations of intestinal fatty acid-binding protein (iFABP), zonulin, soluble CD14 (sCD14), bactericidal/permeability-increasing protein (BPI), and lipopolysaccharide-binding protein (LBP) were measured in 36 patients with SSD at baseline and one-year follow-up and in 36 matched healthy controls. Linear mixed-effects models adjusted for age, sex, and body mass index (BMI) were used to evaluate group differences and longitudinal changes. No biomarker differences between patients and controls, and no longitudinal changes within patients, remained statistically significant after correction for multiple testing. Similarly, biomarker concentrations were not associated with symptom severity, global functioning, or neutrophil-to-lymphocyte ratio (NLR). Exploratory analyses identified a robust positive association between BMI and zonulin concentrations and additional associations among iFABP, LBP, and sCD14. Exploratory mixed-effects models identified a negative non-significant association between iFABP and sCD14 after adjustment for age, sex, and BMI. Overall, the findings do not support a generalized alteration of gut permeability or immune activation markers in SSD. Instead, they suggest that variation in gut-related biomarkers may be more strongly influenced by metabolic and individual factors than by diagnosis or current symptom severity.

Incidence of ventilator-associated pneumonia (VAP) is increased in patients admitted for brain injury. Staphylococcus aureus appears to be a particularly common pathogen in these respiratory infections. Our aim was to confirm the extent of S. aureus involvement in VAP among patients with neurological injuries. We conducted a retrospective, multicenter study including patients with and without neurological injuries who developed VAP during their ICU stay. Among 15 702 patients admitted in the participant centers during the study period. 407 were finally included in the study. S. aureus was involved in 122 (61.6%) VAP in brain-injured patients and 63 (30.1%) of non-brain-injured patients. In brain-injured patients, multivariate analysis identified age (0.97 [0.95; 0.98]), antibioprophylaxis (0.31 [0.18; 0.53]; p < 0.0001), subarachnoid hemorrhage (2.18 [1.01; 4.68]; p=0.0461), lower Glasgow coma score (0.92 [0.87; 0.97]; p= 0.004) and SAPS II (0.98 [0.97; 1]; p=0.0174) as critical factors associated with the risk of S. aureus in VAP. In the non-brain-injured population, multivariate analysis identified antibioprophylaxis (0.38 [0.22; 0.65]; p=0.0006), age (0.96 [0.95; 0.98]; p<0.0001), SAPS II (0.98 [0.97; 1]; p=0.0265), thoracic surgery (0.21 [0.09; 0.48]; p=0.0002) and peripheral neurosurgery (1.95 [1.2; 3.19]; p=0.0076) as critical factors. A total of 172 patients (42.3%) died during their ICU stay and 90-days mortality was 194 (47.7%). No difference of mortality was noticed function of VAP-associated pathogen.

BACKGROUND: The chromosome 9p21.3 locus is among the strongest genetic risk loci for cardiovascular disease (CVD), yet the regulatory mechanisms underlying this association remain poorly understood. Previous vascular disease studies focused predominantly on the role of this locus in smooth muscle cell (SMC) dysfunction, whereas its potential role in endothelial cells (ECs) remains less well characterized. Here, we sought to identify candidate regulatory variants at 9p21.3, their distal target genes, and effector transcripts that contribute to CVD-related phenotypes in ECs. METHODS: Promoter capture Hi-C was used to map chromatin interactions at 9p21.3 in primary human ascending aortic SMCs (n=6), supplementing the previously obtained ECs (n=16) dataset. To support the regulatory relevance of these interactions, expression quantitative trait locus analysis was performed in 136 vascular biopsies. Variant effects were prioritized in silico using AlphaGenome. The regulatory activity of the prioritized region was validated using CRISPR activation. RESULTS: Chromatin interaction mapping identified a complex regulatory architecture at the 9p21.3 locus involving 19 putative regulatory elements and 15 genes across ECs and SMCs. MIR31HG showed the most recurrent distal promoter connectivity with the 9p21.3 region. Consistent with the chromatin interaction map, expression of MIR31HG transcripts was associated with genetic variation at the 9p21.3 locus. In ECs, the lncRNA MIR31HG, but not miR-31, was responsive to hemodynamic flow. Depletion of MIR31HG in ECs was associated with endothelial activation. Epigenetic tracks and deep learning predictions highlighted an active enhancer element at 9p21.3, which was subsequently validated by CRISPR activation and increased MIR31HG expression, confirming its capacity to regulate MIR31HG over a large genomic distance of approximately 538 kb. CONCLUSIONS: Altogether, these data implicate lncRNA MIR31HG as a likely effector of the 9p21.3 CVD locus, linking the cis-regulatory element and heritable genetic variation in the respective region to endothelial activation through long-range enhancer regulation.

Background. Regulatory pressure to avoid confusingly similar drug names appears to have pushed manufacturers to coin more distinctive proprietary names, but no one has quantitatively analyzed this trend. Objective. To determine whether proprietary drug names became more orthographically distinctive (unusual in spelling) over time. Methods. We examined 1,321 US Food and Drug Administration (FDA) new molecular entity (NME) names (1985-2024), 2,769 FDA Orange Book trade names (1982-2026), 177,337 US pharmaceutical trademarks (1980-2026), and 1,273 generic names (1982-2025). Compared to prior names (including those already marketed) and ordinary English, we scored each name's character trigram (three-letter sequence) surprisal (statistical unexpectedness) and its edit-distance similarity to the closest existing names. Linear regression tested time trends, controlling for name length. Results. Mean NME trigram surprisal rose in every decade between the 1980s and 2020s, from 7.21 to 8.50 nats (natural-logarithm units, higher meaning less predictable), an 18% increase (b = 0.038, p < .001, R2 = 0.34). Half of 2020s names (7% in the 1980s) contained a letter sequence found in no common English word (e.g., Jesduvroq, Omvoh). Nearest-neighbor similarity fell after the early 1990s. Trademark data showed the same trend (b = 0.016, p < .001). Generic names showed a weaker trend (b = 0.011, R2 = 0.063). Conclusions. Over four decades, drug names became more orthographically distinctive, consistent with namespace crowding, deliberate efforts to avoid confusing names, and branding strategy. Because unfamiliar letter sequences can introduce other error pathways, increased distinctiveness does not necessarily imply improved safety.

Background: Artificial intelligence (AI) may improve electrocardiographic differentiation of Takotsubo syndrome from acute anterior ST-segment elevation myocardial infarction (STEMI), but its impact on human diagnostic performance, the added value of visual explanations, and differences by reader background and experience remain uncertain. Methods: Using 12-lead electrocardiogram (ECG) images from two hospitals, we developed and externally validated an ensemble of three deep learning models after self-supervised pretraining and fine-tuning. The diagnostic task used ECGs recorded within 14 days after symptom onset, regardless of reperfusion status. We conducted a four-phase reader study. Phases 1-3 compared unaided interpretation, a reference sheet, and additional AI assistance. In Phase 4, readers evaluated a new ECG set after randomization to reference-sheet support alone, with AI predictions, or with AI predictions plus gradient-weighted class activation mapping (Grad-CAM) heatmaps. Diagnostic correctness was analyzed using generalized linear mixed-effects models. Exploratory analyses examined differences by cardiologist status and experience. Results: Development and external-validation cohorts, including healthy controls, comprised 832 and 95 participants, respectively. For Takotsubo-STEMI discrimination, external-validation accuracy was 0.914, with areas under the receiver operating characteristic and precision-recall curves of 0.970 and 0.984. Seventy healthcare providers completed the reader study. The estimated probability of correct diagnosis increased from 0.632 without assistance to 0.864 with AI assistance (odds ratio [OR], 3.71; P<0.001). In randomized Phase 4, AI assistance improved accuracy vs reference-sheet support alone (OR, 4.28; 95% CI, 3.08-5.95; P<0.001), and Grad-CAM provided additional benefit (OR, 1.53; 95% CI, 1.04-2.25; P=0.033). Exploratory analyses showed greater improvement among less experienced readers; with AI assistance, non-cardiologists outperformed cardiologists. AI-assisted readers remained less accurate overall than the model alone. Conclusions: An ECG image-based ensemble differentiated Takotsubo syndrome from anterior STEMI and improved reader accuracy. Visual explanations provided additional benefit, while experience-related differences highlight the importance of effective human-AI interaction.

Background. In people with type 2 diabetes (T2D), postprandial glucose weighs on glycaemic control, and carbohydrate counting is difficult. Patients can describe their meal but rarely quantify it. Objective. To assess (1) whether a small open large language model (LLM), fine-tuned on public data, estimates the carbohydrate content of a meal described in words, and (2) what this carbohydrate adds to the prediction of the postprandial glucose rise in two public cohorts. Methods. Step 1: the Qwen3-4B LLM was fine-tuned with QLoRA on Nutrition5k dishes and compared with an automatically matched USDA food composition table and with Qwen3-4B and Qwen3-8B used without training, on test dishes (internal control) and on 100 real meals of ShanghaiT2DM patients, whose reference carbohydrate was computed from the recorded quantities. Step 2: the maximum glucose rise between 0 and 120 minutes ({Delta}Gmax) was predicted by personalised gradient boosting (M2), compared with a spline linear regression and with the Chronos-2 foundation model, using only information available at meal start and person-level cross-validation, in ShanghaiT2DM (people with T2D recruited in two Shanghai hospitals; carbohydrate estimated by the LLM) and CGMacros (free-living adults in Santa Barbara, California; carbohydrate entered in the dataset, no text). Step 3: the model was transferred between datasets. 95% confidence intervals (CI) were obtained by cluster BCa bootstrap (2,000 resamples). Results. On the 100 real meals, the carbohydrate mean absolute error (MAE) of the fine-tuned model was 12.2 [9.9, 15.2] g, almost half that of the table (22.6 g; paired difference -10.5 [-16.8, -6.0] g) and nearly a third of that of the same model without fine-tuning (34.3 g), with a bias of -10.3 g; on test dishes it was 2.2 [2.0, 2.5] g. Personalised gradient boosting (M2) reached a correlation between predicted and observed rises of r = 0.63 [0.55, 0.69] in CGMacros (1,286 meals, 45 adults) and 0.49 [0.42, 0.54] in ShanghaiT2DM (2,339 meals, 99 people with T2D), about twice the linear reference (r = 0.32 and 0.23); for a rise above 50 mg/dL, its area under the ROC curve (AUROC) was 0.80 and 0.74, versus 0.64 and 0.68 with pre-meal glucose alone. Adding carbohydrate to pre-meal glucose changed r by 0.095 [0.068, 0.128] in CGMacros and by 0.004 [-0.010, 0.017] in ShanghaiT2DM; difference between datasets: 0.091 [0.060, 0.126]. Applied from one dataset to the other, the model lost correlation ({Delta}r = -0.11 [-0.17, -0.05] towards ShanghaiT2DM and -0.12 [-0.19, -0.04] towards CGMacros). The full "text [-&gt;] LLM [-&gt;] glucose" chain was evaluated in ShanghaiT2DM only. Conclusion. An open 4-billion-parameter LLM, fine-tuned with QLoRA on public data and usable offline, almost halves the error of carbohydrate counting for real meals described in words compared with an automatically matched table, with an underestimation of large meals. Personalised gradient boosting, based on pre-meal glucose and the person's usual response, doubles the correlation of a linear reference for predicting the glucose rise in both cohorts. Carbohydrate improved this prediction in CGMacros but not in ShanghaiT2DM; the two datasets differ in many respects, and the cause of this gap is not established.

Background: Epidermal Growth Factor Receptor (EGFR) tyrosine kinase inhibitors (TKIs) are widely used for EGFR-mutated non-small cell lung cancer (NSCLC) but have distinct toxicity profiles across generations. Aims: To investigate relationships between EGFR-TKI physicochemical, pharmacokinetic and pharmacological properties and drug-specific adverse drug reactions (ADRs) using national and global pharmacovigilance data. Methods: ADRs for erlotinib, afatinib and osimertinib were extracted from the UK MHRA Yellow Card scheme and WHO VigiAccess. Disproportionality analysis used reporting odds ratios (RORs). Signals were defined as ROR >2, lower 95% confidence interval >1 and >3 cases. Drug properties were curated from ChEMBL, emc, SmPC and EMA sources. Results: VigiAccess contained 88,662 individual case safety reports (163,619 ADRs) and Yellow Card contained 752 reports (1,814 ADRs). Four System Organ Class (SOC) signals were identified in VigiAccess and three in Yellow Card, with five additional Yellow Card Preferred Term signals. Eye disorders showed the strongest consistent association with erlotinib (VigiAccess ROR 4.33; Yellow Card ROR 4.18). Osimertinib was associated with pneumonitis (ROR 7.67) and pulmonary embolism (ROR 3.94). Afatinib showed psychiatric disorder signals in both databases (ROR 3.2-3.3) and diarrhoea (ROR 2.79). Neoplasm-related signals likely reflected confounding by indication. Conclusions: EGFR-TKIs demonstrate generation-specific pharmacovigilance profiles. Integration with drug properties supports mechanistic plausibility for several signals, while the atypical afatinib psychiatric association warrants further investigation. Findings are hypothesis-generating and require confirmation in controlled observational or mechanistic studies.

Exposure to armed conflict can leave persistent effects on mental health, yet its neurophysiological correlates remain poorly characterized, particularly in populations exposed to prolonged violence outside narrowly defined post traumatic stress disorder samples. Here we examined resting state electroencephalographic activity in 122 Colombian adults, including 74 participants exposed to armed conflict and 48 controls. Eyes closed EEG was analysed for 240 seconds per participant. We quantified absolute and relative spectral power across delta, theta, alpha and beta bands and regional functional connectivity using weighted phase lag index in theta, alpha and beta. Group comparisons were adjusted for age, education, sex and assessment year, with correction across 78 EEG contrasts. Median occipital and global alpha power was lower in exposed participants, although adjusted estimates were close to the null and confidence intervals were broad. Temporal theta relative power was lower in exposed participants at nominal significance, but this association did not survive Holm correction. No spectral power or regional connectivity contrast remained significant after multiple testing correction. Exploratory associations between EEG measures and MoCA performance were modest in participants with confirmed scores. These findings provide a systematic characterization of resting EEG after exposure to armed conflict in a population shaped by prolonged violence and displacement. They also indicate that large or widespread alterations in resting spectral power and connectivity were not detectable with the current sample and analytical framework, while defining candidate signals and methodological priorities for larger studies of conflict related brain health.

People experiencing homelessness (PEH) have substantially lower quality-adjusted life expectancy and substantially higher healthcare spending compared to housed populations. California has the largest population of PEH in the United States, and an estimated ten percent of PEH have co-occurring opioid use disorder (OUD). We developed a lifetime, individual-level microsimulation of the 18,928 PEH with OUD in California to evaluate the health and economic effects of expanding access to housing through a Housing First approach, which does not require OUD treatment or abstinence. Increases in permanent housing and shelter access were based on Los Angeles County's Measure A goals, and we accounted for both housing and healthcare costs. Expanding housing access increased mean age at death by 0.34 years, reduced fatal overdoses for the cohort by 39, and produced an incremental cost-effectiveness ratio of $31,083 per quality-adjusted life-year gained, well below conventional willingness-to-pay thresholds. We provide evidence that housing is a high-value health intervention for PEH with OUD in California. Policymakers should support Housing First programs as part of integrated responses to the intertwined crises of homelessness and opioid use.

Fetal development is highly sensitive to environmental influences, including maternal smoking during pregnancy (MSDP) and air pollution. The placenta may play a central role in mediating these effects through epigenetic mechanisms, including small non-coding RNAs (sncRNAs). While most studies have focused on microRNAs (miRNAs), other sncRNA classes remain poorly characterized. Here, we characterized the placental sncRNA landscape and investigated its associations with prenatal exposure to MSDP and air pollution. The sncRNA expression profile of 390 chorionic villi samples from the Barcelona Life Study Cohort (BiSC) was measured using small RNA sequencing and the seqBuster/seqCluster pipeline. MSDP was assessed via questionnaire, and prenatal exposure to black carbon (BC), particulate matter [&le;]2.5 m (PM2.5), and nitrogen dioxide (NO2) was estimated using hybrid dispersion-land-use regression (LUR) spatiotemporal models. Associations were evaluated using linear regressions adjusted for relevant covariates. We identified 979 placental sncRNA clusters, assigned primarily to miRNAs (33.2%), piRNAs (26.4%), snoRNAs (12.2%), tRFs (7.8%), and others (20.4%). MSDP was associated with the downregulation of miR-421 and the upregulation of multiple snoRNAs from the imprinted DLK1-DIO3 locus (SNORD113-4, SNORD114-2, SNORD114-3, and SNORD114-17), as well as the piR-26394. No sncRNAs remained significantly associated with air pollution after multiple-testing correction, although several miRNAs showed suggestive associations. Overall, this study provides a comprehensive characterization of placental sncRNAs and identifies previously unreported smoking-associated sncRNAs within the imprinted DLK1-DIO3 locus, expanding our understanding of placental epigenetic responses to prenatal environmental exposures.

Estimating the correlation between subject-specific rates of change is a common objective in longitudinal studies. A natural approach is to fit ordinary least squares regressions per subject and correlate the resulting slope estimates directly. Under complete data, this slopes-first estimator is algebraically equivalent to a contrast-based plug-in estimator derived from the joint variance-covariance matrix of the two signals across time. Under independent dropout, however, the slopes-first approach implicitly applies heterogeneous weights across missing-data patterns, introducing bias whenever the cross-covariance between the two signals is not constant over time. We formalise this result and propose an unbiased alternative that estimates the joint covariance matrix before applying the contrast. We further extend our results to include covariates as well. Simulations confirm that the slopes-first estimator is biased under non-constant cross-covariance while the proposed alternative remains unbiased. Further, we show in a real-world example that the two estimators can lead to different scientific conclusions and recommend that researchers working with longitudinal data inspect the missing data pattern to make an informed decision about the estimator of choice.

Introduction: This study aimed to characterize the neurochemical profile of Alzheimer s disease (AD) continuum stages using proton magnetic resonance spectroscopy (1H-MRS) data for brain metabolites and macromolecules. We used Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) to classify diagnostic groups and assess feature importance. Methods: Our dataset consisted of 7 T short-echo time 1H-MRS spectra of the posterior cingulate cortex/precuneus of individuals diagnosed with AD (n = 27) or Mild Cognitive Impairment (MCI, n = 27) and Healthy Controls (HC, n = 31). Classification used 100-round Monte Carlo cross-validation based on metabolite concentrations and macromolecular peak intensities. Variable importance in projection (VIP) was assessed across classification models to identify the most relevant neurochemical features. The resulting metabolite rankings were compared with previous literature to evaluate their biological plausibility. Results: Classification was strongest for AD versus HC (AUC=0.88) and lower for comparisons involving MCI. Metabolites contributing most strongly to classification included myo-inositol, ascorbate, and choline-containing compounds, consistent with previous studies on AD-related neurochemical alterations. In addition, specific macromolecular components were identified as relevant features, suggesting a potential contribution of MM signals to the characterization of AD-related neurochemistry. Discussion: These findings support the use of short-echo-time 1H-MRS as a complementary tool for characterizing neurochemical changes in the AD continuum, with potential applications in disease characterization and monitoring, warranting validation in larger cohorts.

Computed tomography (CT) interpretation requires recognition of the anatomical level of each slice. Classification may facilitate preprocessing and AI-assisted fracture diagnosis. We developed deep learning models to classify craniofacial CT slices into six anatomical categories. A total of 11,769 images were collected from a multicenter database involving 10 Japanese university hospitals and the public Head-Neck-Radiomics-HN1 dataset. Patients were divided into training (70%) and evaluation (30%) sets. Five CNN architectures were trained from scratch, and hard- and soft-voting ensembles were evaluated. Individual-model accuracy ranged from 94.72% to 96.09%; the hard-voting ensemble achieved 96.46% accuracy (Cohen's k = 0.954). Although performance differed among individual models (Cochran's Q = 99.29, p < 0.001), the ensemble did not significantly outperform EfficientNetV2B1 (McNemar's p = 0.163). Errors occurred mainly between adjacent anatomical regions. Deep learning can classify craniofacial CT slices accurately and may support automated dataset organization and AI-assisted fracture diagnosis. External validation is needed.

Rationale: Conventional CT-derived pulmonary vascular metrics quantify vascular pruning but do not assess the spatial heterogeneity of vascular architecture. In COPD, peripheral microvascular pruning coexists with compensatory central vessel dilation, paradoxically preserving total vessel volume while reducing effective vascular penetration into lung parenchyma. Objectives: To develop and evaluate Vascular Proximity Coverage (VPC), a distance-based CT biomarker quantifying the spatial efficiency of pulmonary vascular distribution. Methods: This retrospective analysis included 7,881 COPDGene participants [PHASE] with complete vascular segmentations. VPC was estimated by integrating the area under the cumulative lung tissue coverage curve at distances of 0-10 mm from segmented vessel voxels. Multivariable models assessed associations with lung function, six-minute walk distance (6MWD), St. George's Respiratory Questionnaire (SGRQ) score, mMRC dyspnea score, and all-cause mortality over a median follow-up of [10.8] years, adjusting for demographics, smoking, emphysema, scanner type, and BV5. The cohort was stratified at median BV5 and VPC into concordant (high/high, low/low) and discordant (high-BV5/low-VPC, low-BV5/high-VPC) vascular phenotypes. Results: VPC declined across disease severity (5.82 {+/-} 0.50 [UNITS] in non-smokers to 5.21 {+/-} 0.64 in GOLD 4; p < 0.001). Among spirometrically normal smokers, current smokers had lower VPC than former smokers (5.31 vs. 5.68; p < 0.001). Each standard deviation decrease in VPC was independently associated with reduced 6MWD ({beta} = -16.8 m, 95% CI: -19.5 to -14.0), worse SGRQ ({beta} = 1.9, 95% CI: 1.4 to 2.4), and greater dyspnea (RR = 1.05, 95% CI: 1.03 to 1.08; all p < 0.001). The lowest VPC quartile was associated with 38% higher mortality than the reference quartile (HR = 1.38, 95% CI: 1.18 to 1.62; p < 0.001). The high-BV5/low-VPC phenotype showed 24% higher mortality than high-BV5/high-VPC (HR = 1.24, 95% CI: 1.08 to 1.42; p = 0.002) despite preserved vessel volume. Conclusions: VPC captures spatial heterogeneity of vascular architecture independent of vessel volume, is reduced in smokers without airflow obstruction, and identifies excess mortality risk among individuals whose total vessel volume appears preserved.