Journal of Personalized Medicine

Hypermobile Ehlers-Danlos Syndrome (hEDS) is a genetic connective tissue disorder characterized by hypermobile joints, chronic pain, fatigue, brain fog, orthostatic intolerance, and GI symptoms and dysmotility. Its heterogeneous presentation contributes to poor quality of life, inappropriate interventions, and prolonged diagnostic delays, often up to 10 years. This study primarily aimed to determine if physiological signals captured by a medical-grade wrist wearable could characterize autonomic patterns in hEDS and relate them to symptoms. Individuals with hEDS (n=30) and healthy controls (n=28) wore a medical grade smartwatch for 30 days, collecting continuous heart rate variability, activity, oxygen saturation, and blood pressure, alongside initial baseline symptom and quality-of-life surveys. Individuals with hEDS showed greater instability and variability in both systolic and diastolic blood pressure as well as the HRV metric LF/HF ratio, in comparison to healthy controls (p-values: 0.04, 0.02, 0.02). During sleep, metrics of parasympathetic activity (HRV measures: HF power, pNN50, RMSSD) trended lower in hEDS than healthy in comparison. As expected, survey domains assessing physiologic symptoms and quality-of-life were significantly worse in the hEDS cohort (p-values < 0.05). Notably, autonomic metrics correlated with GI symptoms in the hEDS cohort (Spearman's {rho} range: 0.38-0.60), and psychological symptoms in the healthy cohort (Spearman's {rho} range: -0.47-0.41). Principal component analysis (PCA) of physiologic and symptom features clearly separated groups, supporting distinct physiologic profiles. Combination of GI symptom index and wearable monitoring show promise as a hybrid screening approach that could substantially shorten the time to diagnosis in this population.

ObjectiveAiming at the core problems prevalent in biomedical research, including the "translational distance", the difficulty in aligning cross-scale studies, and the lack of direct validation of single-cell systems biology models in human samples, this study aims to verify whether the results of transcriptome-wide Mendelian randomization (TWMR) based on large-scale populations are consistent with the causal inference results of deep learning combined with double machine learning (DML) using single-cell transcriptome data from human samples, to clarify whether statistical biology and systems biology can converge to the same biological truth, and provide methodological support for mechanism dissection and precision medicine research of complex diseases such as rheumatoid arthritis (RA). MethodsThis study integrated multi-omics data to conduct a two-stage causal inference and cross-scale validation analysis. In the first stage, based on the summary statistics of RA genome-wide association study (GWAS) from 456,348 individuals of European ancestry in the UK Biobank (UKB), and cis-expression quantitative trait locus (cis-eQTL) data from 31,684 individuals in the eQTLGen Consortium, a two-sample Mendelian randomization approach was adopted. Transcriptome-wide causal effect analysis was performed using the inverse-variance weighted (IVW) method, MR Egger regression, and weighted median method, and gene-level causal effect values were obtained after strict quality control and multiple testing correction. In the second stage, based on single-cell RNA sequencing (scRNA-seq) data from RA patients and healthy controls (RA group: 11 samples, 211,867 cells; Healthy control group: 38 samples, 456,631 cells), after preprocessing via the Seurat pipeline, batch effect correction, and cell type annotation, a hierarchical deep neural network was constructed to complete feature compression of high-dimensional expression data, and the DML framework was used to estimate the causal effects of genes on RA disease status. Finally, Pearson correlation analysis was performed to conduct cell type-specific cross-scale validation of gene-level causal effect values obtained by the two methods, and the validated model was used to quantify the causal effects of 16 RA-related pathways from the Reactome database. ResultsThis study confirmed that the gene causal effect values obtained from large-scale population TWMR analysis were significantly correlated with those calculated by the deep learning combined with DML model based on single-cell transcriptome data. Among them, the correlation was extremely significant (p<0.001) in core naive B cells (r=0.202, p=3.2e-05, n=414) and core naive CD4 T cells (r=0.102, p=0.037, n=412). The validated DML model successfully quantified the cell type-specific causal effect values of 16 RA-related signaling pathways. ConclusionStatistical biology and systems biology can converge to the same biological truth. The cross-scale consistency between the two can significantly shorten the "translational distance" in biomedical research, and realizes the direct validation of the single-cell systems biology causal model of human samples based on large-scale population genetic data, getting rid of the excessive dependence on animal/cell experimental models in traditional research. This research paradigm not only provides a new path for mechanism dissection and therapeutic target screening of complex diseases such as RA, but also provides a feasible solution for rare disease research to break through the limitation of GWAS sample size, and lays an important theoretical and methodological foundation for constructing standardized systems biology models of human complex diseases and promoting the development of precision medicine.

BackgroundRetrieval-augmented generation (RAG) frameworks such as RAPID [1] have demonstrated that staged planning and retrieval grounding improve long-form text generation. However, most implementations remain similarity-driven and open-domain, lacking the epistemic safeguards required for biomedical synthesis, where mechanistic completeness, temporal governance, traceability, and explicit gap classification are essential. ObjectiveTo develop and evaluate a topology-aware, graph-augmented retrieval framework for structured biomedical narrative synthesis, and to position it as a domain-constrained evolution of staged RAG aligned with structural principles of digital evidence-based medicine (dEBM). MethodsWe implemented a two-layer architecture operating on a closed, version-controlled corpus of 11,861 peer-reviewed text chunks on iron deficiency. A metadata-constrained vector retriever (RAG01) was extended with a Graph-RAG (RAG02) overlay (RAG02) constructed from chunk-level entity extraction and weighted co-occurrence networks (30 nodes; 118 directed edges). Topic planning was organized through predefined mechanistic axes functioning as structured hypothesis probes. Retrieval was performed under identical deterministic constraints (top-k = 5; cosine threshold = 0.50; publication year [&ge;] 2023), and graph diagnostics--including local connectivity, induced subgraph density, modular overlap, and multi-hop stability--were used to distinguish retrieval insufficiency from corpus-level evidentiary scarcity. ResultsIn a case study of obesity-associated iron deficiency, the entity network exhibited a centralized regulatory topology with hepcidin as a high-connectivity hub. Axis-based retrieval combined with graph auditing consistently reinforced an inflammation-mediated hepcidin pathway linking obesity to iron deficiency, while alternative mechanisms lacked stable multi-hop embedding. Compared with vector-only retrieval, graph augmentation preserved semantic alignment and increased mean cosine similarity from 0.673 to 0.694 while reducing similarity dispersion (SD 0.056 to 0.035) under identical constraints. Graph activity ratio was 1.00 in the temporally filtered corpus. ConclusionsBy integrating mechanistic axis decomposition, topology-aware auditing, causal scaffolding, and expert-driven iterative refinement, the proposed framework implements selected structural constraints inspired by evidence-based medicine within a controlled digital synthesis environment. The approach advances retrieval-augmented generation beyond similarity-based summarization toward a reproducible model of topology-aware biomedical evidence interrogation with implications for AI-assisted systematic reviews.

Carsickness impairs comfort and affects a large proportion of the population. However, interventions that provide a therapeutic solution to carsickness have yet to be established. Here we introduce a wearable mindfulness meditation brain-computer interface (MM-BCI) system as a closed-loop training therapy for carsickness. The system records electroencephalographic activity, decodes meditative state in real time and delivers audiovisual neurofeedback to scaffold meditation practice. In a 10-week randomized controlled trial, 60 individuals susceptible to carsickness were assigned to practice mindfulness meditation with either real-time MM-BCI neurofeedback or sham feedback, both during real-world car riding and at home. Critically, pre-intervention, post-intervention, and one-month follow-up assessments of carsickness severity were conducted during regular car riding without any task or feedback system. Relative to the sham group, the MM-BCI group showed significantly reduced carsickness severity at post-intervention and follow-up. At baseline, carsickness-susceptible participants exhibited a reduced aperiodic exponent in occipito-parietal cortex relative to non-susceptible controls, identifying a candidate neural signature of carsickness susceptibility. MM-BCI training increased this exponent toward non-susceptible levels, and the magnitude of this neural normalization was associated with the degree of symptom improvement. This study provides the first demonstration that BCI-enhanced mindfulness meditation can induce promising treatment effect on carsickness, offering a transformative non-pharmacological approach to enhance passenger well-being in everyday transit.

ObjectiveTo develop and psychometrically evaluate an assessment of symptoms in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) MethodsAn initial symptom list was devised from the relevant literature with the patient and clinician advisory groups. An online survey with 85 symptom items in eight domains was completed by people with ME/CFS. Each item had two response structures (assessing symptom frequency and severity on five-point scales). Rasch analysis assessed each domain for unidimensionality, targeting, internal reliability, item fit and local dependency. ResultsSurvey data (n=721) indicated various item anomalies and inter-item dependencies, leading to item re-formatting or removal. The frequency and severity-based responses broadly replicated each other, and a four-point response format appeared more appropriate than a five-point response format. Following Rasch-based scale amendments, a revised version with a single four-point response format was re-administered to test the modifications. Validation data (n=354) showed the modified scale had an improved response structure and functionality across all domains, satisfying Rasch model assumptions. Additionally, domain-level super-items allowed for a summated total score along with sub-scales summarising neurological and autonomic symptoms, again satisfying Rasch model assumptions. ConclusionsThe Index of ME Symptoms (TIMES) and its associated sub-scales and domain scales are stable, valid assessments of symptoms in ME/CFS.

BackgroundAlthough ST-segment elevation myocardial infarction (STEMI) and non-STEMI (NSTEMI) are very similar regarding pathophysiology and clinical treatments, especially NSTEMI comprises a much more heterogenic group of patients and underlying diseases. We therefore aimed to assess the treatments and outcomes of both entities in a large contemporary cohort. MethodsPatients with STEMI and NSTEMI between 01/2010 to 12/2018 were identified from the largest German Health Insurance (AOK, {approx}26 million members). Patient demographics, their hospital course, adherence to guideline-directed drug therapy and overall survival were assessed. ResultsIn total 544,529 patients (mean age 74, IQR 62-82), one third of whom had a STEMI. Chronic kidney disease, peripheral arterial disease, and heart failure were more common in patients with NSTEMI. Patients with STEMI were more likely to get coronary angiograms and percutaneous coronary interventions. Although STEMI more frequently led to cardiogenic shock, the rate of serious cardiac events was lower. Mortality was higher for STEMI only within the first 30 days, whereas long-term survival rates were better. The combination of statins, angiotensin converting enzyme inhibitors /angiotensin receptor blockers, beta blockers, and oral anticoagulants or antiplatelet agents was associated with higher overall survival in patients with STEMI (hazard ratio [HR] 0.20; 95% confidence interval [95%CI] 0.18 - 0.24; p<0.001) or NSTEMI (HR 0.30; 95%CI 0.28 - 0.33; p<0.001). Nevertheless, the prescription rates decreased over time, particular in patients with NSTEMI. ConclusionClear differences between STEMI and NSTEMI were observed regarding short-and long-term survival. Guideline-recommended therapy improved long-term survival, but decreased during the follow-up period.

Background and AimFibromyalgia is an idiopathic chronic widespread pain syndrome affecting 2-4% of the general population globally. Besides widespread fibromyalgia pain, morning stiffness, associated neurologic as well as sleep problems are also reported. Disease is more prevalent in females of middle-age group with low socioeconomic status, thus deteriorating overall productivity and psychosocial health. There is no permanent cure of the disease. This study aimed to explore, validate and assess the effect of four weeks of supervised yogic intervention on pain status, quality of life, sleep, cortical excitability, flexibility and range of motion in fibromyalgia patients, as compared to standard therapy. MethodCase-control study, interventional study and assessor-blined randomized controlled trial, conducted in 120 fibromyalgia patients (60 yoga group: 60 waitlisted controls) and 60 age-matched healthy controls. Pain was assessed subjectively, using questionnaires and objectively, using quantitative sensory testing and ELISA. Sleep and quality of life were assessed using common and disease specific decsiptors. Flexibility and range of motion was assessed using sit and reach box, lateral goniometry and modified Schobers test. Transcranial magnetic stimulation on M1 was used to assess corticomotor excitability of participants. Study parameters were assessed at baseline and after four weeks of the intervention. ResultsA significantly poor sleep, flexibility and quality of life was reported in the fibromyalgia patients due to excruciating pain (VAS = 6.92{+/-}0.12); corticomotor function was also abnormal in the patients, which were restored after four weeks of yogic intervention. On subjective and objective assessment of pain, we found significant relief and improvement in pain status in the yoga group as compared to the waitlisted controls. Fibromyalgia impact, sleep, quality of life and flexibility were also found solely better in fibromyalgia patients undergoing yogic interventions. Cortical parameters, specifically RMT, MEPs and MEP recruitment curves showed a significant improvement in yoga group as compared to waitlisted controls. ConclusionFour weeks of regular and supervised yogic intervention may ameliorate pain, improve flexibility and range of motion and changes cortical plasticity in the Indian cohort of fibromyalgia patients, as compared to standard therapy. Yoga-based interventions can also improve overall quality of life and sleep impairmentsby reducing catastrophization and fibromyalgia impact.

Background: Irritable bowel syndrome (IBS) is characterized by heterogeneous symptom trajectories and high treatment discontinuation rates. Traditional analyses examine longitudinal outcomes and time-to-event endpoints separately, potentially missing informative dropout and the association between symptom dynamics and treatment persistence. Objective: To jointly model patient-reported IBS symptom trajectories and time-to-treatment discontinuation using shared random effects, characterizing the association between individual symptom dynamics and treatment persistence in a large Canadian prospective cohort. Methods: We analyzed 2,847 adults with Rome IV diagnosed IBS enrolled in the Canadian Gut Project (2018 to 2024) across 14 gastroenterology centres in Alberta, British Columbia, and Ontario. The longitudinal submodel used linear mixed-effects regression for the IBS Severity Scoring System (IBS-SSS) measured at baseline and months 3, 6, 12, 18, and 24. The survival submodel used a Weibull proportional hazards model for time-to-treatment discontinuation. The joint model linked both processes through shared random effects (random intercept and slope), estimated via maximum likelihood with adaptive Gauss-Hermite quadrature (15 nodes). We conducted sensitivity analyses using Bayesian estimation, alternative association structures (current value, time-dependent slopes), and multiple imputation for intermittent missingness. Results: Mean baseline IBS-SSS was 298.4 (SD 72.1). Over 24 months, 1,042 participants (36.6%) discontinued treatment. The longitudinal submodel revealed a mean IBS-SSS decline of -8.7 points/month (95% CI: -10.2, -7.1) with substantial between-person heterogeneity in both intercepts (STD = 4,218.3) and slopes (STD = 12.4). The association parameter linking the shared random intercept to the hazard of discontinuation was = 0.0034 (95% CI: 0.0021, 0.0047; p < 0.001), indicating that each 10-point increase in individual-specific baseline severity increased the hazard of discontinuation by 3.5%. The shared slope association parameter was 2 = -0.187 (95% CI: -0.264, -0.110; p < 0.001), demonstrating that individuals with steeper symptom improvement had lower discontinuation hazards. IBS-D subtype (HR = 1.41; 95% CI: 1.18, 1.69), concurrent anxiety (HR = 1.28; 95% CI: 1.09, 1.50), and social media health information use (HR = 0.82; 95% CI: 0.71, 0.95) were significant predictors in the survival submodel. Conclusion: Joint longitudinal-survival modelling reveals that IBS symptom trajectories and treatment discontinuation are dynamically linked through individual-level latent processes. Higher baseline severity and slower improvement trajectories significantly predict earlier discontinuation. These findings support personalized treatment monitoring approaches that use real-time symptom trajectory data to identify patients at risk of discontinuation.

Background: Continuous adhesive patch electrocardiographic (ECG) wearables are increasingly prescribed. Patient experience with these devices can influence adherence, but research in this area is limited. This study aimed to explore the perceptions and experiences of patients receiving wearable cardiac monitoring technology as part of their routine care through the lens of treatment burden. Methods: This was a qualitative study with semi-structured phone interviews conducted between February and May 2024. We recruited participants from primary care and outpatient clinics using maximum variation sampling to ensure diversity in sex, ethnicity, and education levels. Interviews were audio-recorded, transcribed, and analysed using reflexive thematic analysis. Results: Sixteen participants (mean age 51 years, 63% female) were interviewed (average duration: 33 minutes). Three themes were developed: 1) ?Experience using the device: Burden vs Ease of Use?, which captured participants? perceptions of how easily they could integrate the device in their daily lives; 2) ?Individual variability in responses to ECG self-monitoring? covered participants? emotional and cognitive response to knowing their heart rhythm was monitored; and 3) ?The care process shapes patient experiences? reflected support preferences during the set-up and monitoring period and the uncertainty regarding timely clinical and device feedback. Conclusions: Patients valued cardiac wearables for facilitating diagnosis and felt reassured knowing they were clinically monitored. However, gaps in information provided to patients seemed to cause anxiety for some participants. These concerns could be mitigated through clearer clinician communication and patient education at the time of prescription.

The emergence of Janus kinase (JAK) inhibitors, a relatively new class of medications for autoimmune and inflammatory conditions, has been accompanied by reports of adverse effects observed during clinical trials. However, uncertainty over their safety and efficacy in wider, unselected populations has led to discussion and speculation on social media such as Reddit. Social networks represent a novel, rich source of real-world pharmacovigilance data. They are also an environment where unverified information about these medications may circulate. This paper analyzes Reddit conversations related to JAK inhibitors, applying graph modeling and community detection techniques using Neo4j and the Louvain algorithm. Data from 2011 to 2024 were collected, cleaned, and used to construct a directed graph, incorporating posts, comments, users, and drug mentions as nodes and their interactions as edges. Advanced computational methods, including large language models, were utilized to analyze textual data and identify patient-reported experiences that diverge from current medical consensus. This study systematically maps online discourse and identifies key participants to understand how patient experiences and concerns about JAK inhibitors are shared within communities. The findings show that various subreddits serve as hubs of information in which key influencers are spreading both positive and negative information within the Reddit ecosystem. Highlighting the potential to integrate graph-based approaches, Neo4j, and advanced LLMs in real-time pharmacovigilance, this study presents compelling evidence of the emerging conversations surrounding JAK inhibitors and how they affect public health. Author SummaryPeople often turn to Reddit to share their experiences with medications, including Janus kinase (JAK) inhibitors, which are used to treat autoimmune conditions such as arthritis, eczema, and alopecia areata. These drugs are fairly recent, and some safety concerns have been identified, making discussions about them on the internet a mixture of personal stories, questions, and statements which may not correspond to serious or established medical literature. [60] In this research we analyzed over ten years of Reddit discussions to explore how individuals discuss JAK inhibitors and how both correct and possibly misleading information circulates within groups. We integrated graph-based techniques, which illustrate the connections among users and conversations with AI tools that identify claims at odds, with clinical guidelines. We applied the term "divergent patient experiences" exclusively to comments that contradict regulation or evidence-based sources, while personal accounts and feelings of individuals are not classified as divergent patient experiences. Our findings demonstrate that a very small number of users initiate a large proportion of conversations and discussions tend to revolve around the main health topics. This approach of using social media to monitor public health opinions shows the manner in which it avails information regarding real patient concerns, but it also shows the requirement of expert supervision when using AI to appraise health information being shared online.

Neurological health score (NHS), indicating the health of brain and nervous system, helps in identifying high risk individuals, and in recommending lifestyle modifications. In the present study, we developed NHS based on genetic, lifestyle and biochemical variables associated with eight neurological disorders - dementia, stroke, Parkinsons disease, amyotrophic lateral sclerosis, schizophrenia, bipolar disorder, multiple sclerosis and migraine. UK Biobank data from Caucasian individuals was used to develop the model, and the data from individuals of Indian ethnicity was used to validate the model. Logistic regression and XGBoost algorithms were used in selecting the significant variables for the disorders. NHS developed from the selected variables was found to be very significant after adjusting for age and sex (AUC:0.6, OR: 0.95). Higher NHS was associated with a lower risk of neurological disorders and better social well-being. Highest NHS group (top 25%) showed 1.3 times lower risk compared to the rest of the individuals. Results of our study help in developing a framework for quantifying the neurological health in clinical setting.

ObjectiveTo develop and evaluate a scalable and reproducible natural language processing (NLP) approach using large language models (LLM), to identify cannabis use status and reasons for cannabis use among patients with autoimmune rheumatic diseases (ARDs) from unstructured electronic health record (EHR) clinical notes. Methods and AnalysisWe conducted a retrospective study using EHR clinical notes from patients with ARDs (2015-2024). Notes were screened for cannabis-related mentions using fuzzy string matching against a curated keyword lexicon with a similarity threshold of 90, extracting 50-word context windows ({+/-}25 words). Two domain experts annotated 886 randomly sampled snippets across four classes: (1) not a true cannabis mention/uncertain, (2) denial of use, (3) positive past use, and (4) positive current use. Using these annotations, we compared multiple LLM prompting strategies (zero-shot to few-shot; temperature tuning) and a fine-tuned clinical model (GatorTron 345M). For "reason for use," 1,027 snippets were annotated into six categories: pain, nausea, sleep, anxiety/stress/mood, appetite, and not mentioned/unknown. Models were evaluated on a held-out validation set using accuracy, F1, recall, and precision. We then aggregated snippet-level predictions to patient level to describe temporal trends and subgroup differences. ResultsFor cannabis use status classification, the fine-tuned GatorTron model achieved the highest performance (accuracy 0.90; F1 0.91; recall 0.90; precision 0.90). For the reason of cannabis use classification, gpt-oss-20B achieved the highest performance (accuracy 0.77; F1 0.77; recall 0.77; precision 0.86). Patient-level analyses characterized trends in documented cannabis use from 2015-2024 and compared clinical characteristics between current users and patients denying use. ConclusionHigh-precision extraction of cannabis use status and reasons for use from EHR notes is feasible using a combination of fine-tuned clinical language models and LLM-based classifiers. This approach enables scalable measurement of patient-reported symptom self-management strategies in ARDs, supporting observational research and potential clinical decision support.

Schizophrenia (SCZ) and type 2 diabetes mellitus (T2DM) are common comorbid disorders that severely impair patient prognosis and quality of life. This study aimed to explore the association between the methylenetetrahydrofolate reductase (MTHFR) C677T gene polymorphism and MTHFR promoter methylation in patients with comorbid SCZ and T2DM. A total of 120 participants were enrolled from Liaocheng Fourth Peoples Hospital between January 2025 and June 2025, comprising 30 subjects in each of the four groups: SCZ group, T2DM group, SCZ-T2DM comorbid (SCZ+T2DM) group, and healthy control (CTL) group. Corresponding primers were designed for genetic analysis, and methylation-specific PCR (MSP) was performed to detect the methylation level of the MTHFR promoter. Genotype distribution of the MTHFR C677T polymorphism was consistent with Hardy-Weinberg equilibrium (HWE) (p>0.05). The C677T polymorphism was significantly associated with an elevated risk of SCZ and T2DM comorbidity (p<0.05). Notably, the methylation rate of the MTHFR promoter in the SCZ+T2DM group (95.00%) was not significantly higher than that in the CTL group (90.00%) (p>0.05). In conclusion, the MTHFR gene may serve as a susceptibility gene for SCZ-T2DM comorbidity, whereas MTHFR promoter methylation is not associated with the pathogenesis of this comorbid condition. These results indicate that genetic variation in MTHFR, rather than promoter methylation, contributes critically to the comorbidity of SCZ and T2DM in the Han Chinese population. Our findings may provide novel molecular insights into their shared pathophysiology and inform future clinical strategies for patients with this complex phenotype.

In this study, we investigate gender bias in a Retrieval-Augmented Generation (RAG) based AI assistant developed for Finnish wellbeing services counties. We tested the system using 36 clinically relevant queries, each rendered in three gendered variants (male, female, gender-neutral), and evaluated responses using both an LLM-as-a-judge approach and a human expert panel consisting of a physician and a sociologist specializing in ethics. We observed substantial and clinically significant differences across gendered variants, including differential treatment urgency, inappropriate symptom associations, and misidentification of clinical context. Female variants disproportionately framed responses around childcare and reproductive health regardless of clinical relevance, reflecting societal stereotypes rather than medical reasoning. Bias manifested both at the LLM generation stage and the RAG retrieval stage, in several cases causing the model to hallucinate responses entirely. Some bias patterns were persistent across repeated runs, while others appeared inconsistently, highlighting the challenge of distinguishing systematic bias from stochastic variation.

IntroductionAdverse drug reactions (ADRs) remain a major public health issue, and genetic factors contribute importantly to interindividual variability in drug response. Pharmacogenetic testing helps reduce ADR risk by optimizing drug selection and dosage, particularly in monogenic disorders. Material and MethodsWhole-exome sequencing of 6,739 samples from the Russian population was performed using the MGIEasy Universal DNA Library Prep Set on the DNBSEQ-G400 platform (MGI). Variants in 48 genes were examined, focusing on inherited arrhythmias (Long QT syndrome, Short QT syndrome, Timothy syndrome, Andersen-Tawil syndrome, Brugada syndrome, Atrial fibrillation, Catecholaminergic polymorphic ventricular tachycardia), enzyme deficiencies (Glucose-6-Phosphate Dehydrogenase Deficiency [G6PDD], Porphyrias), Dravet Syndrome (DS) and Malignant Hyperthermia (MH). All identified variants had been reported at least once as pathogenic (P) or likely pathogenic (LP) in ClinVar, along with those occasionally classified as variants of uncertain significance (VUS). Each variant was manually re-evaluated according to ACMG criteria. ResultsA total of 75 unique variants in 18 genes were observed in 119 individuals (1.77%), including 21 carriers and 13 women with a G6PD mutation. Of these, 46 variants were classified as P, 21 as LP, and 8 as VUS. Missense variants accounted for the largest proportion (73.33%). The most affected genes were KCNQ1 (24/119), which exhibited the highest number of unique variants (18), G6PD (20/119), SCN1A (15/119), and RYR1 (14/119). Regarding associated conditions, mutations linked to arrhythmias were found in 51 individuals, MH in 27, G6PDD in 20, DS in 15, and Porphyrias in 6. ConclusionsIncorporating genetic information on both common and rare clinically actionable variants into therapeutic decision-making has the potential to improve medication safety, reduce preventable ADRs, and enhance the effectiveness of personalized pharmacotherapy.

Colorectal cancer (CRC) is a leading cause of cancer-related death, with incidence rising substantially among individuals under 50 years of age. Polygenic risk scores (PRS) hold promise for identifying high-risk individuals; when combined with lifestyle factors, they substantially improve prediction accuracy compared with models based on lifestyle factors alone. However, few clinical tools currently exist that facilitate this integrated, PRS-enhanced risk assessment. To bridge this gap, we developed MyGeneRisk Colon, a publicly accessible web portal that delivers individualized CRC risk prediction by incorporating genetic, demographic, family history, and lifestyle factors. This paper details the development of the underlying risk prediction model, the portal's architecture and data security, our reporting framework, and engagement with a community advisory panel. Designed as a user-friendly platform, MyGeneRisk Colon aims to effectively communicate personalized CRC risk profiles and educate users and healthcare providers about prevention strategies.

Automating Hierarchical Condition Category (HCC) assignment directly from unstructured electronic health record (EHR) notes remains an important but understudied problem in clinical informatics. We present HCC-Coder, an end to end NLP system that maps narrative documentation to 115 Centers for Medicare & Medicaid Services(CMS) HCC codes in a multi-label setting. On the test dataset, HCC-Coder achieves a macro-F1 of 0.779 and a micro-F1 of 0.756, with a macro-sensitivity of 0.819 and macro-specificity of 0.998. By contrast, Generative Pre-trained Transformer (GPT)-4o achieves highest score of a macro-F1 of 0.735 and a micro-F1 of 0.708 under five-shot prompting. The fine-tuned model demonstrates consistent absolute improvements of 4%-5% in F1-scores over GPT-4o. To address severe label imbalance, we incorporate inverse-frequency weighting and per-label threshold calibration. These findings suggest that domain-adapted transformers provide more balanced and reliable performance than prompt-based large language models for hierarchical clinical coding and risk adjustment.

Achieving timely diagnosis for rare diseases remains challenging due to, among others, phenotypic heterogeneity and incomplete clinical data. While the Solve-RD project developed a phenotype-based gene prioritisation method, this approach did not account for the clinical consistency among related diseases in Orphanets hierarchical classifications. We present a phenotype-based computational pipeline that ranks candidate ORPHAcodes based on patient phenotypes. The pipeline computes patient-disease similarity using asymmetric semantic aggregation of Human Phenotype Ontology terms, filtering subsumed terms and incorporating Orphanet frequency annotations. Evaluated on 139 expert curated Solve-RD cases representing 78 distinct ORPHAcodes, our methodology outperformed the established Solve-RD baseline method, achieving a harmonic mean rank of 4.64 for confirmed diagnoses (versus 7.97) and retrieving the correct suspected rare disease within the top 10 positions for 39% of patients (versus 29%). We then explore a disease similarity network using Random Walk with Restart to generate ranked candidate lists. Two complementary experiments demonstrate that RWR-ranked candidates exhibited improved clinical consistency, reflected by their proximity within the Orphanet nomenclature of rare diseases. This approach provides more interpretable and actionable differential diagnosis hypotheses to guide clinical decision-making Author summaryMany patients with rare diseases face prolonged diagnostic delays due to the extreme heterogeneity of rare disorders associated with the variability of their clinical manifestations, which complicates interpretation and requires structured phenotypic representations and expert knowledge. We developed a computational pipeline that compares patients phenotypes with those documented for rare diseases in the Orphanet database. Rather than relying solely on direct matching of clinical signs and symptoms, our approach leverages relationships between diseases by propagating information through a network connecting patients and diseases. Testing on 139 cases from the European Solve-RD project, our method improved identification of correct diagnoses and generated more clinically coherent candidate lists by accounting the Orphanet nomenclature. This work provides a methodology dedicated to assisting clinicians in developing diagnostic hypotheses for rare diseases.

BackgroundOur study aimed to investigate the relationship between phenotypic age acceleration (PAA), bowel dysfunction (constipation, diarrhea), and depression severity, and examine whether phenotypic age acceleration can play a mediating role in bowel dysfunction and depression severity. MethodsThe data analysis of our study was conducted from the National Health and Nutrition Examination Survey (2005-2010). Participants with bowel dysfunction were identified on the questionnaire of bowel health. Depression was determined based on the Patient Health Questionnaire-9 (PHQ-9). The calculation of PAA is based on 9 test indicators and actual age; a higher PAA means accelerated aging. In this study, a weighted linear regression model was used to analyze the associations among defecation disorders, PAA, and depression. Restricted Cubic Spline (RCS) curves were applied to explore the potential non-linear relationships between the aforementioned variables. Additionally, a mediation effect model was employed to verify whether PAA could function as a mediating variable in the relationship between defecation disorders and depression. ResultA total of 11,808 participants were included in this study. Linear regression analysis showed that both diarrhea ({beta}=3.73, 95% Confidence Interval (CI): 1.69-8.22, P=1.60x10-3) and depression severity ({beta}=1.08, 95%CI: 1.06-1.09, P=4.61x10-16) were positively correlated with PAA. In addition, both constipation ({beta}=2.76, 95%CI: 1.89-4.04, P=2.28x10-6) and diarrhea ({beta}=4.29, 95%CI: 2.65-6.95, P=2.11x10-7) were positively correlated with depression severity. Further mediation effect analysis revealed that PAA may play a mediating role in the association between diarrhea and depression severity (the proportion of mediation effect in the total population was 7.2285%). When exploring whether PAA exerts a mediating role in the association between constipation and depression severity, it was found that PAA played a mediating role in female participants and participants aged <60 years, except for male participants and those aged [&ge;]60 years (the proportion of mediation effect was 9.8417% in females and 8.4512% in the population aged <60 years, with all relevant P-values <0.005)

Many people are affected by post-acute sequelae of COVID-19 (PASC/long COVID, LC). LC has severely affected public health. Features of LC including blood pressure dysregulation, coagulopathies, hyperinflammation, and neuropsychiatric complaints. Mechanisms responsible for LC pathogenesis are not clear. The receptor for SARS-CoV-2 is human angiotensin converting enzyme 2 (ACE2), which binds SARS-CoV-2 spike protein receptor-binding domain (RBD) to initiate infection. We hypothesized that some people produce anti-RBD antibodies that sufficiently resemble ACE2 structure to have ACE2-like catalytic activity. Those antibodies, ACE2-like abzymes, may contribute to LC pathogenesis. We previously showed that ACE2-like activity was associated with immunoglobulin in some people with acute and convalescent COVID-19. ACE2-like catalytic activity correlated with blood pressure changes following moderate exercise challenge in convalescents. We screened human monoclonal antibodies (mAbs) against SARS-CoV-2 spike protein from 4 sources. We identified 4 human mAbs with ACE2-like catalytic activity. The activity was not inhibited by MLN-4760, a compound that inhibits native human ACE2, nor by EDTA, unlike native ACE2, a Zinc metalloprotease, but was inhibited by an overlapping pool of Spike peptides. Enzyme kinetic studies showed that the mAbs had lower Vmax and Km values than ACE2. The data suggested that the antibodies cleave angiotensin II via a different mechanism than ACE2. Identification of mAbs with ACE2-like catalytic activity supports the hypothesis that antibodies induced by SARS-CoV-2 infection could help mediate the pathogenesis of COVID-19 and LC, and more generally, the hypothesis that catalytic antibodies induced by infectious agents can contribute to disease pathogenesis.