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Artificial intelligence (AI) has emerged as a transformative force in liver transplantation (LT), spanning patient selection, donor–recipient matching, intraoperative management, and post-transplant care. Historically, applications have relied on traditional machine learning (ML) and deep learning (DL) techniques trained on structured clinical datasets to improve risk stratification and outcome prediction. Since 2023, however, advances in generative AI (GenAI) and large language models (LLMs) have expanded the scope of AI in LT, enabling multimodal reasoning and the extraction of clinically meaningful information from unstructured data. This review synthesizes recent AI-driven developments across the LT patient journey, from advanced chronic liver disease management to long-term post-transplant outcomes. Representative applications include ML-based models to optimize treatment selection and waitlist prioritization for hepatocellular carcinoma, random forest and gradient-boosting approaches to improve donor utilization and predict graft survival, and DL-based imaging models for graft volumetry and steatosis assessment. Post-transplant innovations encompass neural network models for graft injury classification, recurrent architectures for immunosuppression dosing and fibrosis prediction, multi-task frameworks for complication risk stratification, and integrative omics-based approaches to detect rejection and dysfunction. Emerging GenAI applications include LLM-enabled digital phenotyping of social determinants of health and multi-agent simulations of transplant selection committees. Despite rapid progress, translation into routine practice remains limited by challenges in local validation, workflow integration, and infrastructure requirements. Future opportunities lie in incorporating underutilized data domains, such as psychosocial factors, -omics, and intraoperative physiologic streams, and leveraging GenAI to enhance clinical decision support, research scalability, and patient–clinician communication. AI is poised not only to refine prediction but to reshape the conceptual and operational landscape of liver transplantation.

Background: Tendinopathies are common in middle-aged women. While estrogen regulates tendon remodeling, the influence of systemic hormone replacement therapy (HRT) on the risk of tendon degeneration remains unclear. Hypothesis/Purpose: The purpose of this study was to investigate the association between systemic HRT use and tendon injuries/repair in perimenopausal women. It was hypothesized that systemic HRT would be associated with an increased incidence of tendon injuries and repair. Study Design: Cohort study; Level of evidence, 4. Methods: Using the TriNetX Research Network, we identified female patients aged ≥45 years diagnosed with major tendon abnormalities, including rotator cuff tears, bicipital tendinitis, lateral or medial epicondylitis, Achilles tendinopathy, patellar tendinitis, or quadriceps strain, from January 2004 to July 2025. HRT exposure was defined as systemic estrogen and/or progestin use within 1 year before a diagnosis. Patients with contraindications to HRT were excluded. After 1:1 propensity score matching, 2 groups of 63,836 patients each were generated. Primary outcomes included diagnosis rates of specific tendon conditions; secondary outcomes were lifetime rates of corresponding tendon repair procedures. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. The independent t test and chi-square test were used for analysis. Results: HRT users had significantly higher rates of several tendon abnormalities compared to matched controls, including rotator cuff tears (39.2% vs 37.9%, respectively; P < .001), bicipital tendinitis (13.3% vs 11.0%, respectively; P < .001), lateral epicondylitis (27.8% vs 22.7%, respectively; P < .001), and medial epicondylitis (6.8% vs 5.3%, respectively; P < .001). Achilles tendinopathy had similar rates between the 2 groups (14.6% vs 14.5%, respectively; P = .387). Surgical repair was more common in the HRT group, including biceps tendon repair/tenodesis (3.9% vs 3.3%, respectively; OR, 1.16 [95% CI, 1.10-1.23]; P < .001), rotator cuff repair (6.6% vs 6.2%, respectively; OR, 1.06 [95% CI, 1.01-1.11]; P = .013), and Achilles tendon repair (0.7% vs 0.6%, respectively; OR, 1.23 [95% CI, 1.06-1.41]; P = .005). Conclusion: Our study showed that systemic HRT use in women aged ≥45 years was associated with an increased risk of tendon injuries and a greater likelihood of surgical repair, although the absolute risk differences were modest. These findings underscore the need for awareness of the possible musculoskeletal effects of hormone therapy and highlight an underexplored area in women's orthopaedic health.

ABSTRACT Background Inflammation plays a critical role in cancer cell proliferation; however, the specific role of inflammasomes, multiprotein complexes that regulate inflammation‐associated signaling pathways, in prostate cancer (PCa) remains insufficiently explored. This study aims to characterize the expression of inflammasome‐related genes in PCa and evaluate their association with clinical outcomes. Methods De‐identified transcriptome data from the Decipher GRID RP, a cohort of 52,266 radical prostatectomy (RP) samples tested (2016–2024) with the Decipher prostate genomic classifier (Veracyte, San Diego, CA), were retrieved from the GRID registry (NCT02609269). Expression analysis of 34 genes involved in inflammatory pathways was conducted to associate their expression with clinical and genomic variables. Outcomes analyses were conducted on a retrospective cohort of 855 patients treated with RP (META855). Results Analysis of inflammasome gene expression in the GRID RP cohort revealed that most genes exhibit low baseline expression, whereas HSP90AB1, APP, TXN, and TXNIP demonstrate strong expression signals. Additionally, higher expression of most genes was associated with Gleason Grade Group 4–5 and very high Decipher scores. On survival analysis of the META855 cohort, higher expressions of AIM2 and HSP90AB1 were significantly associated with worse metastasis‐free survival. Conversely, both high and low expression levels of NLRP3 were associated with better metastasis‐free survival outcomes following RP compared to average expression. On multivariable Cox regression analysis, higher expressions of AIM2 (HR 1.75) and HSP90AB1 (HR 1.60) were significantly associated with shorter time to metastasis following RP. Conclusions There is molecular heterogeneity within pro‐inflammatory genes among patients with PCa. Our findings showed there is a potential association between the expression levels of certain inflammasomes, such as AIM2, HSP90AB1, and NLRP3, and oncological outcomes following RP.

ABSTRACT Background Patients with end‐stage renal disease (ESRD) undergoing dialysis have an increased risk of pacemaker‐related complications due to limited vascular access and increased susceptibility to infection. Leadless pacemakers (LPMs) may mitigate these risks compared with conventional transvenous pacemakers (TPMs), but long‐term comparative data in this population remain limited. Methods We conducted a retrospective cohort study using the TriNetX Research Network, including adults (≥ 18 years) with ESRD on dialysis who underwent LPM or TPM implantation between May 2016 and December 2020. Propensity score matching (1:1) was performed using demographics and comorbidities. Outcomes assessed over up to 5 years included all‐cause hospitalization, heart failure hospitalization, device re‐intervention, device‐related complications, infection‐related hospitalization, pacemaker‐induced cardiomyopathy, and all‐cause mortality. Results After matching, 901 patients were included in each group with well‐balanced baseline characteristics. LPM implantation was associated with a significantly lower risk of device re‐intervention compared with TPM (4.1% vs. 9.3%; hazard ratio [HR] 0.44; 95% confidence interval [CI] 0.28–0.69; p < 0.01). There were no significant differences between groups in all‐cause hospitalization (65.5% vs. 68.3%; p = 0.22), heart failure hospitalization (26.9% vs. 30.2%; p = 0.13), all‐cause mortality (37.5% vs. 35.3%; p = 0.34), device‐related complications (14.5% vs. 14.3%; p = 0.94), infection‐related hospitalization (1.6% vs. 2.1%; p = 0.35), or pacemaker‐induced cardiomyopathy (9.0% vs. 8.6%; p = 0.66). Conclusion In a large, propensity‐matched cohort of patients with ESRD on dialysis, LPMs were associated with fewer device re‐interventions and comparable long‐term clinical outcomes compared with TPMs.

ABSTRACT Leptomeningeal disease (LMD) is a rapidly fatal complication of systemic cancer for which sensitive diagnostic tools and informative biomarkers remain limited. Here, we introduce CSF-Seq, a method for whole-transcriptome sequencing of cell-free RNA (cfRNA) from human cerebrospinal fluid (CSF), designed to enable molecular profiling of LMD and other central nervous system (CNS) conditions. Using a prospectively collected CSF biobank, we analyzed 125 samples spanning multiple pathologies, including breast and lung LMD, glioblastoma, traumatic brain injury, and non-cancer neurological controls. Through optimized RNA extraction, library preparation, and deep sequencing, CSF-Seq generated robust and reproducible transcriptome-wide profiles despite the low abundance and fragmentation of cfRNA in CSF. CSF transcriptomes exhibited disease-specific expression, separating LMD from non-cancer controls and from non-LMD cancers, independent of CSF collection modality. Tumor-associated epithelial transcripts, including CEACAM6 and MUC1, were consistently enriched in LMD samples, whereas immune and CNS-associated transcripts were broadly detected across disease states, consistent with contributions from both tumor and non-tumor sources. Cross-site processing of matched samples demonstrated high concordance, indicating preservation of sample-specific transcriptional signatures across independent workflows. Importantly, we identified a collection method– independent LMD gene expression signature that was significantly associated with overall survival, supporting its potential prognostic relevance. Together, these findings establish CSF-Seq as a technically robust and clinically informative platform for transcriptomic biomarker discovery in CNS metastatic disease, offering a minimally invasive approach for disease characterization, risk stratification, and longitudinal monitoring in patients with LMD.

The word humility is derived from the Latin word humus, meaning “ground” or “soil.” Romans equated humility with being grounded. Perhaps because it contradicts our natural tendencies toward self-preservation, humility remains an often misunderstood and underappreciated virtue. It is foreign to many, especially in today’s society, which is so focused on self-promotion. However, it is truly one of the most important and powerful virtues that a person can possess and practice. Humility is a just assessment of ourselves and the world around us. It is not thinking less of oneself; rather, it is actually thinking less about oneself. In looking beyond ourselves, we can obtain an honest estimate of our limitations and what is needed to remain patient-centered. In approaching our profession with humility, we more closely honor the privileges of caring for patients, teaching others, innovating, and advancing science. The aim of this article is to show how humility nurtures our development in all aspects of medicine. A humble clinician maintains a steadfast focus on their patient, a humble educator emphasizes the learner, a humble researcher respects science through their honesty and integrity, and a humble innovator does not let pride blind them, recognizing that innovation without humility is doomed to fail. In addition to being a superpower in medicine, humility blesses us with the wisdom and gratitude that come from experiencing, living for, and working for something bigger than ourselves. Gratitude has been considered the father of all virtues, and our ability to be truly grateful requires a core of humility.

This cross-sectional study investigates the association between lipoprotein(a) levels and risk of ventricular arrhythmias among patients with heart failure with reduced ejection fraction receiving implantable cardioverter-defibrillator therapy.