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Objective To evaluate the feasibility and potential efficacy of percutaneous spinal stimulation (epidural stimulation, ES) combined with task-specific training to reduce spasticity and improve gait and balance in individuals with progressive multiple sclerosis (MS). Methods Two men with progressive MS (EDSS 6.5) underwent ES lead implantation targeting the lower spinal cord, followed by one month of rehabilitation involving 12 ES-assisted training sessions. Assessments included instrumented gait analysis, Modified Ashworth Scale (MAS), pendulum test, and static balance testing performed at baseline, and at end of the study with ES-Off and ES-On conditions. Results Participant 1, with spastic hemiparetic gait, demonstrated improved lower extremity joint kinematics and muscle activation during gait, reduced knee extensor spasticity, and enhanced functional movement patterns with ES-On. Participant 2, with significant paraparesis and minimal spasticity, showed limited gait changes but experienced marked improvements in static balance, particularly under eyes-closed conditions. No adverse events were reported. Conclusion This pilot study demonstrates the feasibility and tolerability of ES paired with task-specific training in progressive MS. Participant-specific responses were observed, including improvements in gait kinematics, neuromuscular activation, spasticity, or balance; however, spatiotemporal gait parameters did not improve. These preliminary findings highlight response heterogeneity and the need for individualized ES parameter tuning. Trial registry name and URL: https://clinicaltrials.gov/study/NCT06019611

Retrieval-augmented generation (RAG) is rapidly emerging as a transformative paradigm for large language models (LLMs), especially in high-stakes domains like oncology that demand precision, factual grounding, and up-to-date knowledge. By pairing LLMs with external knowledge repositories, RAG systems explicitly ground model outputs in relevant retrieved documents, helping to reduce hallucinations and ensure responses reflect current evidence. In oncology, where clinical knowledge evolves continually with new research and drug approvals, RAG offers a way to integrate the latest data (e.g., trial results, guidelines, genomic databases) into decision-making. This review synthesizes the technical foundations of RAG, including its architecture and key components, and examines current applications in oncology such as clinical decision support, patient education, radiology reporting, pathology analysis, and genomics-driven precision medicine. We highlight recent studies that demonstrate RAG’s potential—for instance, improving treatment recommendations by incorporating genetic profiles and literature, and enhancing diagnostic accuracy by integrating guidelines. We also discuss emerging developments like multimodal RAG (combining text with imaging or other data), ensemble model approaches, and new explainability tools that trace model outputs to sources. Finally, we critically analyze the limitations and challenges of deploying RAG in healthcare, including computational costs, retrieval errors, noise or conflicts in retrieved information, and ethical and regulatory considerations. While RAG-based systems show promise in augmenting oncologists’ expertise with timely knowledge, careful implementation, high-quality curation of knowledge bases, and human oversight will be crucial for safe and effective adoption in clinical practice.

Abstract Background/Aims The aim of this prospective cohort study was to evaluate the extent that kinesiophobia at baseline contributed to pain intensity at follow-up after starting first line treatments in patients with a range of musculoskeletal conditions, including rheumatoid arthritis (RA), fibromyalgia (FM) and knee osteoarthritis (OA). Methods 469 patients (RA n = 156, FM n = 149, OA n = 164) recruited from the Nuffield Orthopaedic Centre, Oxford University Hospitals, participated in this study. Pain was assessed with the pain detect questionnaire, with the item average pain severity over the last 4 weeks used for the primary analysis. The Tampa Scale for Kinesiophobia (TSK) was administered to assess fear of movement. The association between TSK and pain at baseline was investigated by analysing their correlation using Pearson’s Product-Moment Correlation. RA, FM and OA groups were treated with standard care, which consisted of DMARDs, pain rehabilitation therapy, and total knee arthroplasty, respectively. The correlation between baseline TSK and change from baseline in 4-week average pain severity was calculated at 3-, 6- and 12-month follow-up using Pearson’s Product-Moment Correlation. A simple linear regression model was used for each cohort to evaluate the extent TSK contributed to change in pain from baseline. Results At baseline, mean TSK was 38.8±7.0 and mean 4-week average pain severity was 6.6±2.0 (all conditions pooled), with a Pearson’s Product-Moment Correlation of 0.28 [95% CI: 0.18-0.37], representing a small positive correlation (p < 0.01) between baseline TSK and baseline pain. The results were similar for all 3 conditions independently (RA correlation 0.30 [95% CI: 0.14-0.44], FM correlation 0.31 [95% CI: 0.15-0.46], OA correlation 0.25 [95% CI: 0.06-0.41], all ps < 0.01). No correlation was found between TSK and change from baseline in pain severity for the RA or FM cohorts at any follow-up. A weak negative correlation between TSK and change in pain severity was found in the knee OA group at 3 months (correlation: -0.18 [95% CI: -0.39-0.04]) and 12 months (correlation: -0.16 [95% CI: -0.39-0.08]); however, these results were not statistically significant (p = 0.12 and 0.19 respectively). The linear regression analysis showed that TSK was not associated with change in pain from baseline for any condition at any timepoint. Mean pain decreased in the RA and OA groups irrespective of TSK, with no improvement in pain seen in the FM group (RA mean pain at baseline: 6.5±2.1, at 12 months: 4.3±2.6; FM mean pain at baseline: 6.6±1.6, at 12 months: 6.6±1.8; OA mean pain at baseline: 6.5±2.2, at 12 months: 1.9±2.6). Conclusion Whilst kinesiophobia and pain severity were correlated at baseline, kinesiophobia did not negatively influence change in pain with treatment in any of the cohorts studied. Disclosure R.C. Murphy: None. A. Irani: None. E. Kelleher: None. A.J. Wall: None.

ABSTRACT Myelodysplastic syndrome (MDS) and chronic myelomonocytic leukemia (CMML) are clonal myeloid neoplasms shaped by genetic lesions and immune dysregulation, both of which contribute to disease progression and poor outcomes. Existing prognostic systems, such as IPSS‐R and IPSS‐M, do not incorporate immune alterations. We assessed the biological and clinical relevance of an immune dysfunction signature (IDS) across multi‐center MDS and CMML cohorts. IDS scores, derived from bulk transcriptomic data, were significantly associated with inferior leukemia‐free and overall survival. In multivariable analyses, IDS retained independent prognostic value alongside IPSS‐R, IPSS‐M, and bi‐allelic TP53 inactivation. Incorporation of IDS into existing models improved prognostic discrimination and time‐dependent predictive accuracy. Longitudinal analyses revealed that rising IDS scores paralleled disease progression and acute transformation, whereas declining scores were observed in remission. Biologically, IDS‐high cases demonstrated reduced cytotoxic T‐cell activity, expansion of regulatory T cells, enrichment of primitive progenitor signals, and increased expression of checkpoint pathway genes. These findings were validated across multiple independent MDS cohorts and consistently reproduced in CMML, where IDS also stratified risk and tracked disease evolution. Finally, integration with drug response signatures from the BeatAMLv2 cohort suggested potential therapeutic vulnerability of IDS‐high cases to multikinase and NF‐κB pathway inhibitors. These results establish IDS as a robust and dynamic biomarker in MDS and CMML, with applications in refined risk stratification, longitudinal disease monitoring, and guiding personalized therapeutic strategies targeting immune dysfunction. Trial Registration: #201709072RINC, #202109078RINB, #202207050RINB

ABSTRACT Aims To evaluate the real‐world associations between semaglutide 2.4 mg and cardiometabolic comorbidities, biomarkers and cardiovascular risk among adults with overweight or obesity. Methods This retrospective cohort study used US claims data and laboratory measurements from the Komodo Research Database (2016–2024). Adults with obesity or with overweight and ≥ 1 obesity‐related comorbidity were included. Patients on semaglutide 2.4 mg for ≥ 12 months were compared with non‐users (including users of other obesity medications) using propensity score weighting with residual imbalances adjusted in regression model. Outcomes included time to onset of cardiometabolic comorbidities, change in cardiometabolic biomarkers and changes in 10‐year ASCVD risk score at 12 months. Results 25 783 semaglutide 2.4 mg users and 8 948 913 non‐users were identified. Semaglutide 2.4 mg was associated with significantly lower risks for incident prediabetes or type 2 diabetes, type 2 diabetes only, hypertension, ASCVD, heart failure and CVD (HRs 0.45–0.75; all p < 0.05), as well as greater improvements in BMI (−4.1 kg/m 2 ), SBP (−6.2 mmHg), HbA1c (−0.4%), LDL cholesterol (−7.7 mg/dL), triglycerides (−28.6 mg/dL) and HDL cholesterol (+1.9 mg/dL; all p < 0.001). At 12 months, semaglutide 2.4 mg users ( N = 161) demonstrated a greater reduction in ASCVD risk score (−0.7% vs. +0.3%; between‐group difference −1.0%, p < 0.001), with a decrease in the proportion at intermediate‐high ASCVD risk (14.9%–9.3% vs. 17.7%–18.7%; OR 0.3 [95% CI: 0.1, 0.6], p < 0.001). Conclusions Semaglutide 2.4 mg was associated with lower risk of multiple cardiometabolic comorbidities, favourable changes in biomarkers, and lower estimated ASCVD risk in this population.

Abstract Background : Traumatic encephalopathy syndrome (TES) is a clinical research construct used to identify individuals at risk for chronic traumatic encephalopathy (CTE) following exposure to repetitive head impacts (RHI). Adjudication of TES relies on clinical features such as progressive cognitive impairment and neurobehavioral dysregulation. Blood-based biomarkers and structural neuroimaging abnormalities have been associated with TES but are not part of the criteria. This study evaluated whether TES identification was associated with the combined contribution of cognitive performance, blood biomarkers, and structural neuroimaging measures across two well-characterized cohorts. Methods : Participants included 158 professional fighters from the Professional Athletes Brain Health Study and 149 former American football players from The DIAGNOSE CTE Research Project. Three indices were constructed representing complementary domains: a cognitive index reflecting cohort-specific cognitive features, a blood biomarker index including plasma neurofilament light chain, glial fibrillary acidic protein, total tau, tau phosphorylated at amino acid 231, and APOE -ε4 carrier status, and an imaging index comprising volumetric MRI measures of subcortical structures, ventricles, and corpus callosum subregions. Grouped weighted quantile sum regression models were estimated within each cohort to evaluate associations between these indices and TES while adjusting for age, race, competition status, and RHI exposure. Results : Multidomain models demonstrated improved model performance compared with single-domain models in both cohorts (PABHS: AUC=0.91, PPV=0.80; DIAGNOSE CTE: AUC=0.84, PPV=0.85). Biomarker and imaging indices contributed additional information across cohorts, although imaging contributions were more prominent in fighters whereas blood biomarker associations were stronger in football players. Conclusion : TES in RHI-exposed athletes was associated with a convergent clinicobiological profile observed across two independent cohorts with distinct exposure patterns. These findings support multidomain analytic frameworks for evaluating correlated biological signals in RHI-exposed populations and may inform future studies of TES and CTE.

ABSTRACT Distal urethral strictures involving the meatus and fossa navicularis present unique challenges for reconstructive urologists due to the short segment’s proximity to the glans and high stakes for cosmetic and functional outcomes. Traditional approaches such as ventral meatotomy, skin-flap urethroplasty, and 2-stage techniques are known to compromise cosmesis, increase the risk of urethrocutaneous fistula, and have high recurrence rates, particularly in patients with lichen sclerosis (LS). The evolution of one-sided dorsal onlay urethroplasty with buccal mucosa graft has increased the success rates of single-stage urethroplasties by emphasizing preservation of tissue vascularity, leading to robust graft support. Traditionally, literature described the one-sided dorsal onlay urethroplasty through a perineal incision. This review seeks to describe the refinement of this technique, adapting the one-sided dorsal onlay concept to distal strictures by accessing the urethra through a subcoronal, ventral, or penoscrotal incision. Ventral penile incisions may limit unnecessary dissection, preserve glans architecture, and allow for supine positioning, thereby mitigating risks associated with high dorsal lithotomy. All while retaining the advantages of a well-vascularized dorsal graft bed. Limited reports describe high rates of anatomic and functional success and low complication rates across etiologies, including LS, iatrogenic, and hypospadias repair. Failures tend to be focal and amenable to minor interventions.

ABSTRACT Bayesian statistics is gaining momentum in biomedical research, offering a flexible and intuitive framework for integrating prior knowledge, managing uncertainty, and informing decision-making. In nephrology, a field marked by complex pathophysiology, small patient populations, and evolving therapeutic landscapes, Bayesian approaches may provide critical advantages over traditional statistical methods. Bayesian methodologies may help reshape nephrology by improving trial efficiency, refining diagnostic and prognostic precision, and strengthening causal inference. We conducted a comprehensive narrative review of peer-reviewed literature on Bayesian applications in nephrology, highlighting studies in adaptive clinical trial designs, dynamic risk prediction models, network meta-analyses, pharmacokinetic modeling, and Bayesian Mendelian randomization approaches, including Bayesian model averaging frameworks. Bayesian adaptive designs enabled more efficient and ethically sound trials, such as the WIndow-of-opportunity clinical trial platform for evaluation of novel treatment strategies in REnal cell cancer (WIRE) platform study in renal cell carcinoma. In diagnostics and prognostics, Bayesian models allowed individualized inference from dynamic data, as demonstrated in estimated glomerular filtration rate trajectory analyses and mortality prediction. Bayesian network meta-analyses improved comparative effectiveness research by incorporating probabilistic treatment rankings, while Bayesian frameworks strengthened causal inference through methods like Mendelian randomization (MR) -Bayesian Model Averaging and Bayesian Kernel Machine Regression. Bayesian approaches have emerging applications in nephrology, promoting a probabilistic framework that enhances the interpretation of complex clinical and research data and supports more individualized patient care. Their integration into both research and clinical practice requires investment in education and training, interdisciplinary collaboration, and the development of accessible analytical tools. Embracing Bayesian methods is likely to play an important role in advancing precision medicine and improving patient outcomes in nephrology.