Citations

Abstract Aims Prognosis in tricuspid regurgitation (TR) is shaped by complex clinical and haemodynamic interactions, complicating risk stratification. We aimed to use explainable artificial intelligence (EAI) to model these relationships and identify key predictors of outcomes. Methods and results We analysed long-term all-cause mortality in a large prospective registry of patients with native TR across the full severity spectrum. Patients underwent detailed clinical evaluation and quantitative Doppler-echocardiography. Supervised EAI models were applied to identify and rank predictors of mortality under medical therapy, capturing non-linear associations and interactions among baseline characteristics. Among 9761 patients [mean age 73 ± 15 years; 56.9% women; effective regurgitant orifice area (EROA) 40 (30–56) mm2; regurgitant volume 39 (29–53) mL/beat; 42% severe TR], 2676 deaths occurred during 3.8 ± 4.6 years of follow-up. EAI identified and ranked 20 prognostic determinants of survival. Haemodynamic measures, led by elevated pulmonary-artery systolic and right-atrial pressures, were strongest, followed by age and comorbidity. TR-related variables ranked next, led by right ventricular dysfunction and EROA, which outperformed integrative TR grade and TRIScore. Left-ventricular parameters were less influential. Non-linear effects, including those involving ejection fraction, and interactions such as higher EROA-related risk with elevated pulmonary pressures or lower TRIScore (both P < 0.03) were observed. Proportional hazards models confirmed EROA as the strongest TR severity marker (P < 0.0001), with excess mortality beginning at 40 mm2 and increasing further at 60 mm2. Conclusion EAI ranked mortality predictors in TR, highlighting patient factors, haemodynamics, TR-specific measures, and left-ventricular function. Among TR-specific measures, EROA and TRIScore were most prognostic. Recognizing their impact can refine risk stratification, guide intervention timing, and inform long-term management.

ABSTRACT MYH11 ‐related hereditary type 2 visceral myopathy is a rare smooth muscle disorder typically presenting in infancy with severe gastrointestinal and genitourinary dysfunction. We describe a 73‐year‐old female with lifelong symptoms of gastrointestinal and urinary dysfunction, including chronic constipation, neurogenic bladder, recurrent urinary tract infections, gastroparesis, and pelvic organ prolapse. A multigenerational family history revealed similar visceral smooth muscle abnormalities among 14 affected individuals over five generations, suggesting autosomal dominant inheritance. Whole‐genome sequencing identified a heterozygous, likely pathogenic variant in MYH11 c.5819del; p.(Pro1940HisfsTer91), consistent with the diagnosis of autosomal dominant type 2 visceral myopathy. This variant causes production of an abnormally elongated myosin‐heavy chain, disrupting muscle contractile function. This individual's survival into her eighth decade reflects long‐term clinical stability, possibly attributable to the variant's milder effect. We describe the clinical course of the disease, along with a comprehensive review of previously described individuals with the same variant. This case broadens the phenotypic spectrum of MYH11 ‐associated visceral myopathy, demonstrating that late‐onset, milder forms can occur and remain compatible with long‐term survival with optimal supportive management. Comprehensive genetic evaluation identifies primary disorders, enabling precise diagnosis, tailored management, and informed genetic counseling for complex phenotypes.

ABSTRACT HLA‐B*15:591 is characterised by a single nucleotide substitution in codon 219 in exon 4.

Abstract Introduction Bipolar disorder (BD) has been associated with increased medical burden and accelerated biological aging. Long non-coding RNAs (lncRNAs) regulate molecular pathways related to cellular senescence, inflammation, and telomere maintenance, which are implicated in both BD and aging. This study examined whether aging-related lncRNA expression reflects familial vulnerability or illness-specific effects, and whether childhood trauma and lifestyle factors modulate these signatures within a gene–environment framework. Methods In this cross-sectional study, expression levels of aging-related lncRNAs, including Antisense Non-coding RNA in the INK4 Locus (ANRIL), HOX Transcript Antisense Intergenic RNA (HOTAIR), Nuclear Enriched Abundant Transcript 1 (NEAT1), Taurine Upregulated Gene 1 (TUG1), Metastasis-Associated Lung Adenocarcinoma Transcript 1 (MALAT1), Growth Arrest-Specific 5 (GAS5), and Telomerase RNA Component (TERC), were measured in peripheral blood mononuclear cells (PBMCs) from individuals with bipolar disorder (BD) (n=68), siblings without BD diagnosis (SIB) (n=54), and healthy controls (HC) (n=70) using quantitative reverse transcription polymerase chain reaction (RT-qPCR). Childhood trauma and lifestyle were assessed using the Childhood Trauma Questionnaire (CTQ) and the Healthy Lifestyle Profile II (HPLP-II). Principal component analysis generated a composite aging-related lncRNA factor. Results At the individual transcript level, NEAT1 and TERC were elevated, whereas GAS5 was reduced, in both BD and SIB relative to HC. The aging-related lncRNA composite score was higher in BD and SIB than in HC (F = 7.315, p = 0.001). Familial liability to BD (presence vs. absence of familial liability ) showed a significant main effect on the composite score (F(1,182)=8.18, p=0.005) and interacted with childhood trauma (F(1,182)=10.14, p=0.002). In multivariable models, total CTQ and physical neglect were independently associated with lower composite scores, while familial liability remained a positive predictor (all p<0.001). Conclusions Aging-related lncRNA alterations mark familial vulnerability to BD and are shaped by childhood trauma within a gene–environment interaction framework.

Abstract Dermatomyositis is driven by overactivation of type I and II interferons and other proinflammatory cytokines that signal via the JAK-STAT pathway. We conducted a 12-week, open-label study of brepocitinib, an oral TYK2/JAK1 inhibitor, in five adults with severe cutaneous dermatomyositis. Treatment was associated with rapid, clinically meaningful improvement in cutaneous disease activity. Single-cell and spatial transcriptomic profiling of lesional skin showed marked suppression of interferon-responsive pathways and inflammatory cell states by week 4. Together with findings from a Phase 3 randomized trial in DM patients with skin and muscle involvement (VALOR, NCT05437263 ), these data support TYK2/JAK1 inhibition as a promising therapeutic strategy for DM.

Abstract Background Large language models (LLMs) offer promise for systematic review data extraction, but performance in complex multidisciplinary domains and utility for clinical statement generation remain insufficiently described. Objectives To evaluate Google NotebookLM for AI-assisted data extraction and RAND/UCLA consensus statement generation in a systematic review of IBD, obesity, and cardiometabolic comorbidities. Methods Studies were organized into domain-specific notebooks; structured prompts generated standardized evidence tables. Two independent reviewers validated outputs against full-text articles using a four-category error classification. Cell-level accuracy and critical accuracy (cells free of major factual errors) were the primary metrics; workflow time was compared against a published conventional extraction benchmark. Concordance between AI-generated and expert-finalized statements was assessed. Results Across 57 articles, 1,710 data cells were extracted; 151 (8.83%) were flagged, yielding 91.17% cell-level accuracy. Major factual errors occurred in only 4 cells (0.23%), for a critical accuracy of 99.77%. Most errors were minor omissions (59.6%) or incomplete extractions (30.5%); domain error rates ranged from 7.08% to 11.33%. The pipeline required 17.7 versus a projected 165.1 person-hours (89.3% reduction). PICO-structured prompting generated 70 candidate statements; 58 of 112 finalized panel statements (51.8%) were AI-derived, and 85.7% were retained in the finalized set. Conclusion Google NotebookLM demonstrates feasibility as a primary extraction and synthesis tool in a multidisciplinary systematic review, with extractive incompleteness as the principal limitation and substantial time savings over conventional approaches. Its novel application to RAND/UCLA consensus statement generation extends AI-assisted evidence synthesis to clinical consensus generation workflow.