Citations

Background: Arthrofibrosis is seen in 1.7% to 38% of patients after anterior cruciate ligament reconstruction (ACLR) and has been associated with worse postoperative patient-reported outcomes. There are limited data regarding the optimal time to intervene in cases of arthrofibrosis after ACLR. Purpose: To assess if timing of arthrofibrosis treatment after ACLR affects final knee range of motion (ROM) and to help establish guidelines with regard to timing of intervention for arthrofibrosis after ACLR. Study Design: Cohort study; Level of evidence, 3. Methods: Databases were retrospectively screened at 2 high-volume practices for patients who underwent lysis of adhesions (LOA; Current Procedural Terminology [CPT] 29884 and 29875) with or without manipulation under anesthesia (CPT 27570) after ACLR (CPT 29888). Clinical ROM assessments were collected at multiple time points. Patients were grouped into cohorts based on timing of LOA. Comparative analysis was performed to assess for relationships between ROM and timing of intervention. Results: Seventy-nine patients underwent LOA after ACLR: 25 patients who had a time from ACLR to LOA <3 months, 23 patients between 3 and 6 months, and 31 patients >6 months. Earlier LOA was associated with greater total loss of motion before LOA. The final total arc of motion was 132.7°, 133.7°, and 131.8° for patients who had a time from ACLR to LOA <3 months, between 3 and 6 months, and >6 months, respectively. There were no significant differences among groups in final flexion ( P = .8), extension ( P = .746), or total arc of motion ( P = .781). Linear regression modeling did not identify time from ACLR to LOA as a predictive variable ( P = .982) in final ROM. Conclusion: This study failed to demonstrate a linear relationship between the timing of LOA and final ROM. Regardless of the time to intervention, ROM improved significantly; however, patients with more severe arthrofibrosis underwent LOA at earlier time points.

Abstract The tumor landscape of pancreatic ductal adenocarcinoma (PDAC) is refractory to conventional photon radiotherapy (RT) because of a fibrotic tumor microenvironment (TME) that promotes chronic hypoxia and reduced immune surveillance. The radiobiological factors unique to carbon ion RT (CIRT), such as high linear energy transfer and less dependence on oxygen, make it well-suited to overcome the PDAC TME. In this study, we utilized clonal syngeneic KPC pancreatic tumor cell lines and tumors to examine this postulate and to identify underlying factors that affect the response of PDAC to CIRT. Although KPC cell lines exhibited radiobiological effectiveness greater than 3, subcutaneous tumors in the mouse hind leg showed lower radiobiological effectiveness—1.3 based on quintupling time—at a linear energy transfer between 70 and 80 keV/μm. Four days after CIRT, we observed widespread transcriptomic changes in the tumor immune microenvironment, suggesting increased infiltration of antitumor immune cells and elevated expression of antitumor T-cell cytokines, MHC class I molecules, and co-stimulatory signals. Fewer immunologic changes were observed following photon irradiation. By 7 days after CIRT, tumor-supportive transcriptomic programs characterized by protumor cytokines, M2 macrophages, and cancer-associated fibroblasts emerged, promoting resistance and limiting the durability of tumor growth delay. These findings suggest that CIRT may offer a favorable platform compared with conventional photon RT for combining with immunotherapies. Furthermore, these data highlight the risk of using in vitro survival data alone in treatment planning and indicate that underlying TME factors affect the response of PDAC in vivo.

Abstract eSource – particularly EHR-to-EDC – is an emerging paradigm in clinical research that enables automated transfer of electronic health record (EHR) data into electronic data capture (EDC) systems, with the potential to reduce site burden, improve data quality and accelerate oncology clinical trial workflows. However, widespread implementation remains limited due to technical, regulatory and operational barriers. To address these challenges, the European Institute for Innovation through Health Data (i~HD) launched the eSource Scale-Up Task Force in 2024. This multi-stakeholder initiative brings together leading oncology centres and pharmaceutical sponsors to establish a consensus-driven roadmap for eSource adoption. Central to this effort are three foundational resources: readiness criteria for early adopters, a performance indicator framework for monitoring success and an operational playbook to guide implementation. This article provides a structured overview of the Task Force’s objectives, collaborative model and outputs, with specific attention to its focus on interoperability, regulatory alignment and real-world validation. While initially developed for oncology, the Task Force’s framework is applicable across therapeutic areas characterized by data-intensive workflows.

Background: This narrative review examines the role of artificial intelligence (AI) in bowel sound analysis for the diagnosis and management of inflammatory bowel disease (IBD). Inflammatory bowel disease (IBD), encompassing Crohn’s disease and ulcerative colitis, presents a significant clinical burden due to its unpredictable course, variable symptomatology, and reliance on invasive procedures for diagnosis and disease monitoring. Despite advances in imaging and biomarkers, tools such as colonoscopy and fecal calprotectin remain costly, uncomfortable, and impractical for frequent or real-time assessment. Meanwhile, bowel sounds—an overlooked physiologic signal—reflect underlying gastrointestinal motility and inflammation but have historically lacked objective quantification. With recent advances in artificial intelligence (AI) and acoustic signal processing, there is growing interest in leveraging bowel sound analysis as a novel, non-invasive biomarker for detecting IBD, monitoring disease activity, and predicting disease flares. This approach holds the promise of continuous, low-cost, and patient-friendly monitoring, which could transform IBD management. Objectives: This narrative review assesses the clinical utility, methodological rigor, and potential future integration of artificial intelligence (AI)-driven bowel sound analysis in inflammatory bowel disease (IBD), with a focus on its potential as a non-invasive biomarker for disease activity, flare prediction, and differential diagnosis. Methods: This manuscript reviews the potential of AI-powered bowel sound analysis as a non-invasive tool for diagnosing, monitoring, and managing inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis. Traditional diagnostic methods, such as colonoscopy and biomarkers, are often invasive, costly, and impractical for real-time monitoring. The manuscript explores bowel sounds, which reflect gastrointestinal motility and inflammation, as an alternative biomarker by utilizing AI techniques like convolutional neural networks (CNNs), transformers, and gradient boosting. We analyze data on acoustic signal acquisition (e.g., smart T-shirts, smartphones), signal processing methodologies (e.g., MFCCs, spectrograms, empirical mode decomposition), and validation metrics (e.g., accuracy, F1 scores, AUC). Studies were assessed for clinical relevance, methodological rigor, and translational potential. Results: Across studies enrolling 16–100 participants, AI models achieved diagnostic accuracies of 88–96%, with AUCs ≥ 0.83 and F1 scores ranging from 0.71 to 0.85 for differentiating IBD from healthy controls and IBS. Transformer-based approaches (e.g., HuBERT, Wav2Vec 2.0) consistently outperformed CNNs and tabular models, yielding F1 scores of 80–85%, while gradient boosting on wearable multi-microphone recordings demonstrated robustness to background noise. Distinct acoustic signatures were identified, including prolonged sound-to-sound intervals in Crohn’s disease (mean 1232 ms vs. 511 ms in IBS) and high-pitched tinkling in stricturing phenotypes. Despite promising performance, current models remain below established biomarkers such as fecal calprotectin (~90% sensitivity for active disease), and generalizability is limited by small, heterogeneous cohorts and the absence of prospective validation. Conclusions: AI-powered bowel sound analysis represents a promising, non-invasive tool for IBD monitoring. However, widespread clinical integration requires standardized data acquisition protocols, large multi-center datasets with clinical correlates, explainable AI frameworks, and ethical data governance. Future directions include wearable-enabled remote monitoring platforms and multi-modal decision support systems integrating bowel sounds with biomarker and symptom data. This manuscript emphasizes the need for large-scale, multi-center studies, the development of explainable AI frameworks, and the integration of these tools within clinical workflows. Future directions include remote monitoring using wearables and multi-modal systems that combine bowel sounds with biomarkers and patient symptoms, aiming to transform IBD care into a more personalized and proactive model.

ABSTRACT Background Valve‐in‐valve transcatheter aortic valve replacement (ViV‐TAVR) is increasingly used to treat structural valve degeneration. Permanent pacemaker implantation (PPI) following TAVR is associated with increased morbidity and mortality, yet predictors specific to ViV‐TAVR remain poorly defined. Objective To identify clinical and procedural predictors of PPI in patients undergoing ViV‐TAVR. Methods We conducted a retrospective observational study of consecutive ViV‐TAVR cases at the Mayo Clinic from 2012 to 2022. Clinical, ECG, procedural data and fluoroscopic measurements were analyzed, and propensity score matching was used to adjust for confounding. Predictors of PPI were identified using multivariable logistic regression. Results Among 261 patients, 24 (9.2%) underwent PPI. Balloon‐expandable valves were used in 66.7% and self‐expanding valves in 33.3%. Predictors of PPI included Preprocedural bifascicular block (OR 12.15, 95% CI: 2.52–68.89, p = 0.003), greater septal implantation depth (OR 1.29, 95% CI: 1.04–1.65, p = 0.019), and post‐procedural new left (OR 23.56, 95% CI: 4.91–48.52, p = 0.0004) and right bundle branch blocks (OR 24.4, 95% CI: 3.82–30.51, p = 0.001). Aortic regurgitation as the mechanism of prosthetic valve failure was associated with reduced PPI odds (OR 0.28, 95% CI: 0.10–0.76, p = 0.014). Pacing requirement—defined as > 90% ventricular pacing—increased from 25% at 3 months to 56.25% at 12 months. At a median follow‐up of 962.5 days (IQR 481.5–1555.3), adjusted mortality was significantly higher in the PPI group (aOR 9.4, 95% CI: 1.9–60.4, p = 0.028). Conclusion Bifascicular block, greater septal depth, and new bundle branch blocks predict PPI post‐ViV‐TAVR, while aortic regurgitation appears protective. Risk stratification and procedural planning are critical.

Abstract Aims The Society for Cardiovascular Angiography and Interventions (SCAI) Classification provides risk stratification of patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS). This sub-study of the ECLS-SHOCK trial investigates the prognostic impact of SCAI stages in AMI-CS and the influence of SCAI stages on the effect of extracorporeal life support (ECLS) therapy in AMI-CS patients. Methods Patients with AMI-CS enrolled in the multicentre, randomized ECLS-SHOCK trial were included. The outcomes, treatment effect and safety of ECLS were stratified according to SCAI stage at admission using a post-hoc classification. Results From a total of 417 patients enrolled in the ECLS-SHOCK trial between June 2019 and November 2022, 51.6% (n = 215), 13.4% (n = 56) and 35.0% (n = 146) presented in SCAI Stages C, D and E, respectively. SCAI stages were associated with the risk of 30 day all-cause mortality (C vs. D vs. E: 32.6% vs. 67.9% vs. 64.4%, P &lt; 0.001), with rates of renal replacement therapy at 30 days (C vs. D vs. E: 7.0% vs. 19.6% vs. 13.7%, P = 0.03) and with poor neurological outcomes (C vs. D vs. E: 17.2% vs. 44.4% vs. 36.5%, P &lt; 0.001). No interaction was observed between SCAI stage and the treatment effect of ELCS on 30 day all-cause mortality (ELCS vs. control SCAI C: 32.7% vs. 32.4%; SCAI D: 68.4% vs. 66.7%; SCAI E: 59.7% vs. 68.4%, P for interaction = 0.65). Conclusions In AMI-CS patients included in the ECLS-SHOCK trial, SCAI stages at admission were predictive for mortality and for the incidence of safety events. The efficacy of ECLS treatment was not affected by SCAI stage.

Abstract This paper provides a practical guide to applying the updated seizure classification in clinical settings. The updated classification, published by the International League Against Epilepsy in 2025, builds on the operational classification introduced in 2017. It aims to enhance clarity, clinical relevance, and consistency in seizure terminology across various healthcare settings. The classification system distinguishes between four main seizure classes: focal, generalized, unknown whether focal or generalized, and unclassified. The basic version allows for application even in resource‐limited or primary care environments, while the expanded version offers detailed semiological descriptors and supports advanced diagnostic and surgical decision‐making. Consciousness, defined through awareness (recall) and responsiveness, is now considered a classifier. The paper also highlights the importance of distinguishing observable from non‐observable features and integrates semiology into the expanded classification with attention to temporal sequence and somatotopic detail. Generalized seizures are categorized into seizure types having a direct impact on syndrome diagnosis and treatment decisions. Epileptic spasms are given special attention because of their unique presentation and the urgency of early intervention, particularly in infants. To support clinical implementation, this paper includes a comprehensive table of semiological descriptors, definitions of generalized seizure types, and a series of real‐world case vignettes illustrating the application of the updated seizure classification. Supplementary figures and videos further support the educational aims of the paper. This practical guide is intended for healthcare professionals managing patients with epilepsy, providing a clear, structured approach to seizure classification that is adaptable to varying levels of clinical expertise and diagnostic resources.

Abstract Aims Left ventricular assist devices (LVADs) are a critical intervention for advanced heart failure (HF), serving as destination therapy or bridge to transplantation. Obesity and diabetes impact outcomes in patients with LVADs. Glucagon-like peptide-1 receptor agonists (GLP1-RAs) demonstrate cardiovascular benefits; however, their role in patients with LVADs remains underexplored. We evaluated the association of GLP1-RA therapy with cardiovascular outcomes in patients with LVADs. Methods This retrospective cohort study used the TriNetX database, a research network database from 98 healthcare organizations. We queried for all adult LVAD recipients (≥18 years) and were stratified into GLP1-RA users and non-users. Propensity score matching (PSM) (1:1) balanced demographics, comorbidities, medication use and laboratory data. Outcomes included heart transplantation rates, heart failure hospitalizations, all-cause mortality, all-cause hospitalizations and cardiovascular events. Logistic regression models were used to estimate adjusted odds ratios (aOR). Results After PSM, we included a total of 1036 adult LVAD recipients (518 GLP1-RA users, 518 matched non-users) with a mean follow-up time of 311.6 ± 98.4 days for the GLP1-RA cohort and 304.0 ± 111.5 days for the non-GLP1-RAs cohort. Mean age was 56.7 ± 12.2 years in the GLP1-RA cohort and 58.0 ± 12.5 years in the non-GLP1-RA cohort. Females comprised 28.0% of both cohorts while White patients represented 52.1% of the GLP1-RA group and 53.1% of the non-GLP1-RA group. GLP1-RA users had higher heart transplantation rates [n = 98 (18.9%) vs. n = 44 (8.5%); aOR 2.514 (95% CI: 1.720–3.673)]. Acute HF events and all-cause hospitalizations were lower among GLP1-RA users compared with non-users [n = 288 (55.6%) vs. n = 357 (68.9%); aOR 0.565 (95% CI: 0.438–728) and n = 324 (62.5%) vs. n = 390 (75.3%); aOR 0.548 (95% CI: 0.420–0.716)]. No differences were observed when comparing the GLP1-RA cohort with the non-GLP1-RA cohort in regard to all-cause mortality [n = 32 (6.2%) vs. n = 44 (8.5%); aOR 0.709 (95% CI: 0.442–1.138)], stroke [n = 42 (8.1%) vs. n = 58 (11.2%); aOR 0.700 (95% CI: 0.461–1.062)] or cardiac arrest [n = 18 (3.5%) vs. n = 17 (3.3%); aOR 1.061 (95% CI: 0.541–2.082)]. Conclusions GLP1-RA therapy in patients with advanced HF and LVADs is potentially associated with improved heart transplantation rates while decreasing hospitalization and acute HF event rates.

Purpose: This study assesses intersurgeon variability in proptosis reduction after orbital decompression for thyroid eye disease. Methods: This multicenter retrospective study included patients with thyroid eye disease who underwent orbital decompression from 1 of 7 surgeons at 7 different institutions between January 2002 and December 2018. Data were included if a single decompression technique was performed on ≥10 patients by ≥2 surgeons. The primary outcome was postoperative change in proptosis with emphasis on comparison among surgeons utilizing similar surgical techniques. Statistical analysis was performed with χ 2 and ANOVA testing, and a multivariable logistic regression model was generated. Results: Six hundred thirty-three orbits that underwent orbital decompression were included. Five different decompression techniques were analyzed: medial wall (n = 29), fat + lateral wall (n = 113), medial wall + floor (n = 123), fat + lateral wall + medial wall (n = 140), and fat + lateral wall + medial wall + floor (n = 228), without significant difference in proptosis reduction among surgeons. Surgeons did not demonstrate significant differences in outcomes at different time points compared with each other or themselves. Outcomes when comparing endoscopic versus open medial wall decompression varied among surgery types. Multivariate modeling revealed a statistically significant association between postoperative change in proptosis with preoperative proptosis ( p < 0.001). Conclusions: Postoperative change in proptosis did not differ significantly between surgeons utilizing similar orbital decompression techniques for patients with thyroid eye disease. This study may strengthen the statistical validity of multicenter clinical trials assessing orbital decompression outcomes performed by surgeons employing uniform surgical techniques, thereby advancing our understanding of optimal surgical management strategies for thyroid eye disease.

Abstract Women make up 51% of the world's population, and the global population of postmenopausal women is growing. About 25% of these women experience debilitating menopausal symptoms. Since it is important that all health care professionals have a fundamental knowledge of managing women presenting with symptoms related to the menopause, this European Society of Endocrinology Clinical Practice Guideline was developed. It provides guidance on evaluation and optimal clinical management of women who go through the menopause in middle age, those with Premature Ovarian Insufficiency (POI), Early Menopause and those for whom hormones are not appropriate, including women with, or at high risk of, breast cancer. This guideline discusses the benefits and risks of hormone therapy administration as well as summarizing other treatments for menopausal symptoms. Further, the contentious issue of the impact of menopausal hormone therapy in the prevention of chronic disease is considered.