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Background: Clinical trajectories in functional neurological disorder (FND) are variable, and the mechanisms underlying this heterogeneity remain poorly understood.

Objective: This longitudinal study examined resting-state functional connectivity predictors and mechanisms of symptom change in FND.

Methods: Thirty-two adults with FND (motor and/or seizure phenotypes) completed baseline questionnaires and a functional MRI (fMRI) session, followed by naturalistic treatment for 6.8±0.8 months. All participants completed follow-up questionnaires; 28 individuals completed a follow-up fMRI. At each timepoint, three graph-theory network metrics of functional connectivity were computed: weighted-degree (centrality), integration ( between-network connectivity), and segregation ( within-network connectivity). Analyses adjusted for age, sex, anti-depressants, head motion, time between sessions, and baseline score-of-interest, with cluster-wise correction. Results were contextualized against 50 age-, sex-, and head motion-matched healthy controls (HCs).

Results: Based on patient-reported Clinical Global Impression of Improvement, 59.4% improved, 31.3% were unchanged, and 9.3% worsened. Psychometric scores of core FND symptoms and non-core physical symptoms showed variable trajectories, with no group-level changes. Greater improvement in core FND symptoms was associated with higher baseline between-network integrated connectivity and reduced integration longitudinally within salience, frontoparietal, and default mode network regions. Right anterior insula integration emerged as a prognostic marker and mechanistic site of reorganization, with the most improved participants showing elevated baseline integration compared to HCs. Increased baseline within-network segregated connectivity in dorsal attention network regions correlated with non-core physical symptom improvement. Findings remained significant adjusting for FND phenotype.

Conclusions: This study identified large-scale network interactions as potential prognostic and mechanistically-relevant sites of reorganization related to symptom change in FND.

Recent studies showed that expression QTLs, even from trait-related tissues, explained a small fraction of complex trait heritability. A natural strategy to close this gap is to incorporate molecular QTLs (molQTLs) beyond gene expression, across diverse tissue/cellular contexts. Yet, integrating such QTL data presents analytical challenges. Molecular traits often share QTLs or have QTLs in high LD, complicating the attribution of GWAS signals to specific molecular traits. Our simulations showed that commonly used colocalization and TWAS methods have highly inflated false positive rates in such settings. Building on our earlier work, we developed multi-group causal TWAS (M-cTWAS), for integrating QTLs of different modalities and contexts. M-cTWAS is able to estimate the contribution of each group of molQTLs to the trait heritability, and using such information, identifies the causal molecular traits, informing the modalities and contexts through which genetic variations act on the phenotype. M-cTWAS showed improved control of false discoveries than commonly used methods. Using M-cTWAS, we found that QTLs of multiple modalities greatly increased the explained heritability compared to using eQTLs alone, and enabled the discovery of many more risk genes of a range of complex traits. In conclusion, M-cTWAS effectively integrates diverse molecular QTLs with GWAS to enable causal gene discovery.

Background: Insufficient physical activity (PA) is associated with higher risk of morbidity and premature mortality. Wearable devices offer a scalable, objective measurement of physical activity, but most studies reduce these data to a single activity metric measured over a fixed 7-day period. We compared different wearable-derived phenotyping approaches to understand their impact on activity-disease associations.

Methods: We analyzed 11 million days of Fitbit data from 29,351 participants in the All of Us Research Program, deriving four daily activity metrics (step count, peak 1-min cadence, peak 30-min cadence, and heart rate per step) across five time-windows (1-day, 1-week, 1-month, 6-months, 1-year). We performed phenome-wide analyses on >700 incident and >1,300 prevalent disease outcomes identified from linked electronic health records.

Findings: Among participants with EHR and Fitbit data (mean age 57.3 years, 69% female, 47% with >1 year of Fitbit data), all 20 phenotypes were highly correlated (median Pearson r = 0.71). Longer measurement windows yielded stronger and more stable associations, with 1-year step count associated with 373 prevalent and 37 incident outcomes (versus 231 and 17 for 1-day step count) after Bonferroni-correction, including novel associations with chronic pain syndrome, SARS-CoV-2, and autoimmune disease. Differences between prevalent and incident associations suggest that activity metrics can act as both early markers of disease or risk factors.

Interpretation: These findings highlight how large-scale, longitudinal wearable data can advance understanding of health and disease and inform scalable approaches for clinical risk stratification.

Funding: National Institutes of Health Intramural Research Program, Wellcome Trust.

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is the fastest-growing etiology of hepatocellular carcinoma (HCC). A mechanistic understanding of the metabolic heterogeneity of MASLD-driven tumors is crucial to inform strategies for future treatment options.

Methods: Paired tumor (n=8) and adjacent non-tumor tissue (n=8) were collected from patients with steatohepatitic HCC at the University of Kentucky Markey Cancer Center. Hematoxylin and eosin (H&E) staining was used for pathological determination of tumor and adjacent nontumor tissue by a board-certified pathologist. Lipidomic, metabolomic, and transcriptomic analyses were performed, and data were integrated across platforms to identify novel relationships across tumor and adjacent nontumor tissue.

Results: Histological analysis by H&E showed significant lipid vacuole accumulation and inflammatory foci in HCC tumors relative to nontumor tissue. Across omics platforms, we identified 1,679 genes, 1,696 metabolites, and 292 lipids that were significantly (padj<0.01) increased or decreased in tumors relative to nontumor tissue. We identified significant reductions in total ceramides and increases in fatty acyl chain saturation in tumor tissue. Furthermore, metabolites involved in amino acid and fatty acid metabolism were largely decreased in tumors relative to nontumor tissue. We also identified a total of 303 highly significant and novel transcript-metabolite associations (117 gene-metabolite; 186 gene-lipid) across tumor and nontumor tissue.

Conclusions: Taken together, this integrative analysis reveals novel relationships between steady-state gene transcripts and specific metabolites in steatohepatitic tumors, thereby identifying new pharmacological targets that may be exploited for therapeutic benefit.

Introduction: Previous studies have shown that Aquamin ^® , a multi-mineral extract from red marine algae, enhances barrier integrity proteins in the human colon. These findings prompted further investigation into Aquamin ^® 's effects on gastrointestinal barrier function and permeability.

Methods: Subjects with mild or in remission ulcerative colitis (UC) and healthy controls were enrolled in an open-label trial and received Aquamin ^® capsules (800 mg calcium/day) for 90 days. Intestinal permeability was evaluated before and after the 90-day intervention by urinary mannitol excretion after ingestion of a 5 g mannitol solution, with collections across several time intervals (pre-drink, 0-2 h, 2-8 h, and 8-24 h). The primary outcome was the change in mannitol excretion. Serum samples were also collected to assess liver and renal function.

Results: In this pilot study ( NCT04855799 ), which included UC patients and healthy controls (n = 8 per group), baseline urine mannitol levels in the 0-2 h sample were 54% higher in UC patients compared to healthy subjects (p = 0.006). Following 90 days of Aquamin ^® supplementation, urinary mannitol levels in UC patients decreased by 28%, 26%, and 41% at the 0-2 h, 2-8 h, and 8-24 h timepoints, respectively; the reduction at the 0-2 h interval reached statistical significance (p = 0.015). Overall, Aquamin ^® supplementation reduced total post-intervention mannitol excretion by 29% (p = 0.024). Aquamin ^® was well tolerated, with no serious adverse events reported. The serum metabolic panel revealed a modest but statistically significant reduction in alkaline phosphatase levels after 90 days of intervention.

Conclusion: These results provide preliminary evidence that Aquamin ^® supplementation beneficially modulates gut barrier function and supports epithelial integrity in UC patients. These findings support further investigation of Aquamin ^® as a safe and promising adjunct to current UC management strategies, with potential utility as a barrier therapy in UC.

Summary: Aquamin ^® supplementation for 90 days reduced intestinal permeability in ulcerative colitis patients, as measured by urinary mannitol excretion. The intervention was well tolerated, suggesting Aquamin ^® may be a safe, promising adjunct for enhancing gut barrier function in UC management.

Functional interpretation is essential for understanding how genetic variants contribute to complex traits. Here, we identified and characterized regulatory variants in CD4+ T cells collected from 362 donors. We integrated molecular QTL mapping from single-cell RNA-seq profiles and chromatin accessibility with predicted variant effects from a deep learning model trained on chromatin accessibility data. We identified molecular features and transcription factor binding mechanisms underlying variant sharing and mediated effects across the modalities and approaches. While predicted variant effects correlated with molQTLs, only a small fraction of empirically detected molQTLs were discovered by predictive models. MolQTLs, primarily those affecting chromatin, indicated potential molecular drivers for 33% of immune-related GWAS loci, with the deep learning approach providing insights into 4.7% of GWAS loci. These results highlight the value of multi-omic data and systematic integration of empirical and predictive approaches to interpret regulatory effects of genetic variants.

Background: Poor patient retention and family engagement compromise the effectiveness of coordinated specialty care (CSC) for first-episode psychosis (FEP). This mixed methods study aimed to identify program-level characteristics (CSC fidelity and engagement strategies) associated with patient retention and family engagement in Massachusetts CSC programs.

Methods: Primary outcomes were rates of patient retention and family engagement (≥1 evidence-based family intervention session), based on CSC program census (October 2022 - September 2023). Quantitative analyses explored program characteristics (EPINET Program-Level Core Assessment Battery) and fidelity ratings (Massachusetts Psychosis Fidelity Scale) as predictors using t-tests or univariate linear regressions. Thematic analysis of program interviews compared patient and family engagement strategies employed by high versus low performing programs.

Results: Across nine programs, mean patient retention was 86% (range: 58-97%) and family engagement was 40% (range: 12-100%). Higher fidelity to evidence-based services (e.g., individual therapy, family intervention, and supported education/employment) was significantly associated with both outcomes (p<.05; R ² range: .51-.72). Mixed-methods analysis showed that high performing programs used case management-related supports to meet service users' practical needs. Factors associated with higher patient retention included having comprehensive intake assessments, provider visits during hospitalization, and periodic treatment reviews. Programs that conducted benefits counseling and proactively recommended family services as standard care had higher family engagement.

Conclusions: Higher fidelity CSC programs had better patient retention and family engagement. Case management-related supports addressed treatment barriers. Strategies designed to strengthen therapeutic alliance and goal alignment may promote patient engagement, while family engagement may benefit from proactive recommendation of family intervention.

The effect of biological aging on brain structure is widespread and apparent. However, little is understood regarding which regions exhibit similarities in vulnerability, and what biological processes drive regional patterns of senescence-associated atrophy. Here, we investigated whether associations between age and brain structure exhibit distinct patterns of regional vulnerability, and if so, whether they are related to patterns of cerebral physiology which also show age-related decline. Using both data-driven and hypothesis-driven approaches, we identified recurring patterns of accelerated and delayed decline across the lifespan. Notably, the results mapped using unsupervised clustering methods mirrored the organization of major arterial flow territories, suggesting that vascular architecture may serve as a key organizing principle in brain aging. These results provide support for future research on aging and neurodegenerative disorders that aim to link patterns of structural deterioration to physiological processes that may be useful for identifying 'at risk' individuals and developing novel therapeutics.

Purpose: Ventilator-associated pneumonia (VAP) remains one of the most serious hospital-acquired infections in the intensive care unit (ICU), with high morbidity and mortality. Early identification of patients at risk for developing VAP could enable timely diagnostics and intervention. However, current clinical tools are limited in their ability to detect early physiologic signals preceding VAP onset. We aimed to build supervised machine learning models to predict short term onset of VAP.

Methods: We analyzed electronic health record data from a prospective observational cohort of ICU patients, where VAP was adjudicated using a standardized published protocol by a panel of critical care physicians. Clinical features (including vital signs, ventilator settings, laboratory values, and support devices) were extracted for each patient-ICU-day. We explored unsupervised clustering to characterize feature dynamics associated with VAP onset. We built multiple machine learning models across different prediction windows (3, 5, 7 days before VAP). We examined model performance in two external cohorts, MIMIC-IV and secondary analysis of the AMIKINHAL trial. Results were evaluated with discrimination metrics such as AUROC.

Results: The internal cohort included 507 patients with BAL-confirmed diagnoses: 261 developed VAP and 246 did not have VAP. Visualization using clustering identified distinct physiologic states enriched for VAP-labeled days. The best-performing model achieved an AUROC of 0.866 in predicting VAP up to seven days before clinical diagnosis. Temporal model probability trajectories showed rising model confidence in the days leading up to VAP. On external validation in MIMIC-IV, the best model achieved an AUROC of 0.817 for forecasting VAP within five days. There was low feature overlap with the AMIKINHAL trial data, leading to poor model performance. Feature analysis revealed that platelet count, positive end-expiratory pressure (PEEP), ventilator duration, and inflammatory markers were key drivers of model predictions.

Conclusions: Machine learning models trained on routinely collected ICU data with careful labeling can anticipate VAP onset up to a week in advance with strong predictive performance. Model performance generalized to data from an entirely different hospital system despite differences in practice and labeling patterns, but did not perform well when there was poor feature overlap. Future work should focus on real-time prospective evaluation.