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3116 Background: Pralsetinib is an oral tyrosine kinase inhibitor that selectively and potently targets oncogenic RET fusion and mutation proteins. RET fusions or mutations are present in various tumor types. We report the final results from the phase 2 portion of ARROW, a phase 1/2, open-label, multi-cohort, dose-expansion study evaluating the efficacy and safety of pralsetinib (NCT03037385) in patients with RET fusion-positive solid tumors other than non-small-cell lung cancer (NSCLC) and thyroid cancer. Methods: Eligible pts were ≥18 years of age with a pathologically documented, definitively diagnosed advanced solid tumor with an oncogenic RET fusion or mutation, had previously received standard of care appropriate for their tumor type, and were not eligible for any other study groups. Overall response rate (ORR) and safety were primary endpoints of the study. Key secondary endpoints included duration of response (DOR), progression-free survival (PFS), and overall survival (OS). The final database lock was May 20, 2024. Results: Twenty-nine patients were enrolled with 11 different solid tumor histologies. Twenty-six (90%) received prior systemic therapy. Median age was 58 years (range 25-75); 59% were female. Twenty-eight patients were included in the efficacy analysis. ORR (by RECIST) was 46.4% (13/28); 10.7% (3/28) achieved complete response (pancreatic cancer, n=2; cancer of unknown primary, n=1) and 35.7% (10/28) achieved partial response. Median PFS was 7 months (95% CI: 3.9, 12.8). Median DOR was 11.1 months (95% CI: 5.5, 25.1). Median OS was 10.3 months (95% CI: 6.8, 25.2). Twenty-five (86%) patients experienced treatment-related adverse events (TRAEs); 19/29 (66%) reported TRAEs ≥grade 3. The most common TRAEs included increased aspartate aminotransferase (11/29; 38%), increased alanine aminotransferase (10/29; 35%), and anemia (9/29; 31%). Four (13.8%) patients experienced hypertension, and 1 (3.4%) patient had ≥grade 3 hypertension. No new safety risks were identified; AEs remained manageable with supportive care and/or dose modifications. Conclusions: In the phase 2 portion of this trial, responses were observed in many tumor types (Table). Pralsetinib demonstrated robust and durable anti-tumor activity with an ORR of 46.4%. These data validate RET fusions as a tissue-agnostic target with sensitivity to RET inhibition and activity beyond NSCLC and thyroid cancer, further supporting the promising potential of pralsetinib to address the unmet medical need in these patients. Clinical trial information: NCT03037385 . Overall response rate by tumor type. Cancer Type (patient n) ORRn (%) Pancreatic (5) 5 (100) Cancer of unknown primary (1) 1 (100) Neuroendocrine (3) 2 (67) Sarcoma (3) 2 (67) Head and neck (2) 1 (50) Small cell lung (2) 1 (50) Hepatobiliary (4) 1 (25) Colorectal (5), gastric (1), ovarian (1), thymic (1) 0

Abstract Nearly half of premature deaths can be prevented with healthy lifestyle behaviours. However, most older adults do not adhere to established guidelines to promote a healthy lifestyle. We propose a tailored framework of lifestyle medicine that acknowledges the complex contexts and care needs of older adults. The proposed framework of lifestyle medicine for older adults integrates their lived experiences (e.g. social determinants of health, built environment), the 5 M framework of geriatric medicine (i.e. Mind, Mobility, Medications, Multicomplexity, Matters Most) and the six pillars of lifestyle medicine (i.e. nutrition/diet, physical activity/exercise, restorative sleep, stress management, avoidance of risky substances, positive social connections) through a novel transdisciplinary approach. Guided by the updated framework, key gaps and opportunities are presented to advance lifestyle medicine practice, research and policy with the goal of promoting sustained behaviour change and improving the healthspan of older adults.

Objective Aneurysm wall enhancement (AWE) is a potential biomarker of inflammation within the aneurysm wall that has been correlated with a higher risk of rupture. Aspirin (ASA) may decrease AWE due to its anti-inflammatory properties. We aimed to assess the effect of ASA on AWE in an animal model and a cohort of patients with unruptured intracranial aneurysms (UIAs). Methods Three rabbits with elastase-induced aneurysms were exposed to ASA for 8 weeks and three rabbits were used as controls. 3 T high-resolution magnetic resonance imaging (HR-MRI) was performed at 7 days and 8 weeks to evaluate changes in AWE through histological and immunohistological analyses. Additionally, we evaluated AWE in patients who underwent imaging with a 3 T HR-MRI protocol. ASA exposure was defined as daily intake of 81 mg for at least six months prior to HR-MRI. AWE was quantified using three-dimensional AWE maps and histograms. Results Among rabbits exposed to ASA, the mean AWE was lower at 8 weeks compared to the controls (2.11 vs 2.15, p = 0.13). Immunostaining of the aneurysm wall in rabbits that received ASA revealed a reduced expression of CD68 + or cyclooxygenase-2 + cells, compared to the controls. A total of 99 patients with 120 UIAs were included in the HR-MRI analysis of UIAs. UIAs exposed to ASA (22/120) had significantly lower median AWE than those that were not exposed (0.60 vs 0.72, p = 0.032). Conclusion ASA therapy is associated with an objective reduction in AWE, suggesting a potential role in lowering the risk of aneurysm rupture.

Background: Cardiogenic shock (CS) is a life-threatening complication of ST-elevation myocardial infarction (STEMI) and remains the leading cause of in-hospital mortality, with rates ranging from 5 to 10% despite advances in reperfusion strategies. Early identification and timely intervention are critical for improving outcomes. This study investigates the utility of machine learning (ML) models for predicting the risk of CS during the early phases of care—prehospital, emergency department (ED), and cardiology-on-call—with a focus on accurate triage and prioritization for urgent angiography. Results: In the prehospital phase, the Extra Trees classifier demonstrated the highest overall performance. It achieved an accuracy (ACC) of 0.9062, precision of 0.9078, recall of 0.9062, F1-score of 0.9061, and Matthews correlation coefficient (MCC) of 0.8140, indicating both high predictive power and strong generalization. In the ED phase, the support vector machine model outperformed others with an ACC of 78.12%. During the cardiology-on-call phase, Random Forest showed the best performance with an ACC of 81.25% and consistent values across other metrics. Quadratic discriminant analysis showed consistent and generalizable performance across all early care stages. Key predictive features included the Killip class, ECG rhythm, creatinine, potassium, and markers of renal dysfunction—parameters readily available in routine emergency settings. The greatest clinical utility was observed in prehospital and ED phases, where ML models could support the early identification of critically ill patients and could prioritize coronary catheterization, especially important for centers with limited capacity for angiography. Conclusions: Machine learning-based predictive models offer a valuable tool for early risk stratification in STEMI patients at risk for cardiogenic shock. These findings support the implementation of ML-driven tools in early STEMI care pathways, potentially improving survival through faster and more accurate decision-making, especially in time-sensitive clinical environments.

Importance Nontraumatic subarachnoid hemorrhage (SAH) represents the third most common stroke type with unique etiologies, risk factors, diagnostics, and treatments. Nevertheless, epidemiological studies often cluster SAH with other stroke types leaving its distinct burden estimates obscure. Objective To estimate the worldwide burden of SAH. Design, Setting, and Participants Based on the repeated cross-sectional Global Burden of Disease (GBD) 2021 study, the global burden of SAH in 1990 to 2021 was estimated. Moreover, the SAH burden was compared with other diseases, and its associations with 14 individual risk factors were investigated with available data in the GBD 2021 study. The GBD study included the burden estimates of nontraumatic SAH among all ages in 204 countries and territories between 1990 and 2021. Exposures SAH and 14 modifiable risk factors. Main Outcomes and Measures Absolute numbers and age-standardized rates with 95% uncertainty intervals (UIs) of SAH incidence, prevalence, mortality, and disability-adjusted life-years (DALYs) as well as risk factor–specific population attributable fractions (PAFs). Results In 2021, the global age-standardized SAH incidence was 8.3 (95% UI, 7.3-9.5), prevalence was 92.2 (95% UI, 84.1-100.6), mortality was 4.2 (95% UI, 3.7-4.8), and DALY rate was 125.2 (95% UI, 110.5-142.6) per 100 000 people. The highest burden estimates were found in Latin America, the Caribbean, Oceania, and high-income Asia Pacific. Although the absolute number of SAH cases increased, especially in regions with a low sociodemographic index, all age-standardized burden rates decreased between 1990 and 2021: the incidence by 28.8% (95% UI, 25.7%-31.6%), prevalence by 16.1% (95% UI, 14.8%-17.7%), mortality by 56.1% (95% UI, 40.7%-64.3%), and DALY rate by 54.6% (95% UI, 42.8%-61.9%). Of 300 diseases, SAH ranked as the 36th most common cause of death and 59th most common cause of DALY in the world. Of all worldwide SAH-related DALYs, 71.6% (95% UI, 63.8%-78.6%) were associated with the 14 modeled risk factors of which high systolic blood pressure (population attributable fraction [PAF] = 51.6%; 95% UI, 38.0%-62.6%) and smoking (PAF = 14.4%; 95% UI, 12.4%-16.5%) had the highest attribution. Conclusions and Relevance Although the global age-standardized burden rates of SAH more than halved over the last 3 decades, SAH remained one of the most common cardiovascular and neurological causes of death and disabilities in the world, with increasing absolute case numbers. These findings suggest evidence for the potential health benefits of proactive public health planning and resource allocation toward the prevention of SAH.

BACKGROUND Treatment of tubo-ovarian high-grade serous carcinoma (HGSC) includes cytoreductive surgery, platinum-based chemotherapy, and often poly(ADP-ribose) polymerase (PARP) inhibitors. While homologous recombination (HR) deficiency is a well-established predictor of therapy sensitivity, over 50% of HR-proficient HGSCs also exhibit sensitivity. Currently, there are no biomarkers to identify which HR-proficient HGSCs will be sensitive to standard-of-care therapy. Replication stress may serve as a key determinant of response. METHODS We evaluated phospho–RPA2-T21 (p-RPA2) foci via immunofluorescence as a biomarker of replication stress in formalin-fixed, paraffin-embedded HGSC samples collected at diagnosis from patients treated with platinum chemotherapy (discovery cohort, n = 31; validation cohort, n = 244) or PARP inhibitors ( n = 63). Recurrent HGSCs ( n = 38) were also analyzed. p-RPA2 score was calculated using automated imaging analysis. RESULTS Samples were defined as p-RPA2-high if more than 16% of cells had ≥2 p-RPA2 foci on automated analysis. In the discovery cohort, HR-proficient, p-RPA2-high HGSCs demonstrated significantly higher rates of a chemotherapy response score of 3 to platinum chemotherapy than HR-proficient, p-RPA2-low HGSCs. In the validation cohort, patients with HR-proficient, p-RPA2-high HGSCs had significantly longer survival after platinum treatment than those with HR-proficient, p-RPA2-low HGSCs. Additionally, the p-RPA2 assay effectively predicted survival outcomes in patients treated with PARP inhibitors and in recurrent HGSC samples. CONCLUSION Our study underscores the importance of considering replication stress markers, such as p-RPA2, alongside HR status in therapeutic planning. This approach has the potential to increase the number of patients receiving effective therapy while reducing unnecessary toxicity. FUNDING The Reproductive Scientist Development Program, GOG Foundation, Pilot Translational and Clinical Studies function of the Washington University Institute of Clinical and Translational Sciences, the Foundation for Barnes-Jewish Hospital, Washington University School of Medicine Dean’s Scholar Program, The Cancer Biology Pathway Training Grant (5T32CA113275-17), The Lucy, Anarcha, and Betsey (L.A.B.) Award from the Department of Obstetrics and Gynecology at Washington University School of Medicine, and Veterans Affairs Office of Research and Development (I01BX006020).

Background: While early risk stratification in STEMI is essential, the threat of cardiogenic shock (CS) persists after revascularization due to reperfusion injury and evolving instability. However, risk prediction in later phases—after revascularization—is less explored, despite its importance in guiding intensive care decisions. This study evaluates machine learning (ML) models for dynamic risk assessment in interventional cardiology and cardiac intensive care unit (CICU) phases, where timely detection of deterioration can guide treatment escalation. Methods: We retrospectively analyzed clinical and procedural data from 158 patients diagnosed with STEMI complicated by cardiogenic shock, treated between 2019 and 2022 at the Cardiology Department of the University Emergency Hospital of Bucharest, Romania. Machine learning models—Random Forest (RF), and Quadratic Discriminant Analysis (QDA)—were developed and tested specifically for the interventional cardiology and CICU phases. Model performance was evaluated using area under the receiver operating characteristic curve (ROC-AUC), accuracy (ACC), sensitivity, specificity, and F1-score. Results: In the interventional phase, RF and QDA achieved the highest accuracy, both reaching 87.50%. In the CICU, RF and QDA demonstrate the best performance, reaching ACCs of 0.843. QDA maintained consistent performance across phases. Relevant predictors included reperfusion strategy, TIMI flow before percutaneous coronary intervention (PCI), Killip class, creatinine, and Creatine Kinase Index (CKI)—all parameters routinely assessed in STEMI patients. These models effectively identified patients at risk for post-reperfusion complications and hemodynamic decline, supporting decisions regarding extended monitoring and ICU-level care. Conclusions: Predictive models implemented in advanced STEMI phases can contribute to dynamic, phase-specific risk reassessment and optimize CICU resource allocation. These findings support the integration of ML-based tools into post-PCI workflows, enabling earlier detection of clinical decline and more efficient deployment of intensive care resources. When combined with earlier-stage models, the inclusion of interventional and CICU phases forms a dynamic, end-to-end risk assessment framework. With further refinement, this system could be implemented as a mobile application to support clinical decisions throughout the STEMI care continuum.

Abstract Introduction The new preference-informed allocation (PIA) system introduced for the 2024 UK cohort Foundation Programme (UKFPO) marks a shift away from the traditional meritocratic ‘ranking’ used in previous years. Instead of appointment to Foundation Programme places, PIA is a computer-generated allocation and deanery preferencing system. This change has raised numerous concerns among both students and clinicians. Aims To investigate the opinions of medical students on the new UKFPO PIA system. Methodology An online questionnaire was distributed to medical students graduating in 2024, 2025, or 2026 across the UK. Results In total, 2297 responses were collected and 2288 were included in the study. Overall, 51.6% (n = 1183) of respondents felt the PIA system was unfair, 76.3% (n = 1746) felt they had lost control of their application, and 46.3% (n = 1049) had noticed a negative effect on their physical or mental health. Notably, 48.2% (n = 1094) of students who responded are now considering a career outside the National Health Service (NHS). Conclusions Overall, the PIA system falls short of students’ expectations and has led to record numbers of students considering careers outside the NHS. Further changes to this system are needed and should aim to address fairness and equity while rewarding students for their hard work. According to these data, the PIA system risks further deteriorating workforce morale and attrition rates.

Abstract Celiac disease (CeD) is an immune-mediated condition driven by dietary gluten resulting in small intestinal mucosal inflammation and injury, along with myriads of symptoms. The only treatment is a lifelong gluten-free diet (GFD) and although most patients improve, the restriction can lead to nutrient deficiencies including fiber. While duodenal microbiota is altered in CeD, its fiber metabolic capacity is unknown. Here we show that both active and GFD-treated CeD patients had impaired microbial fiber metabolism in the small intestine which associates with depletion of the carbolytic taxa Prevotella spp. Colonization of germ-free mice with Prevotella spp increased small intestinal short chain fatty acids (SCFA). In gluten-sensitized mice expressing the celiac risk gene, HLA-DQ8, an inulin-supplemented diet facilitated microbial carbolytic function, SCFAs production and accelerated mucosal healing in the small intestine during GFD. The results support clinical investigations of dietary fiber supplementation and microbial carbolytic function to enhance responses to GFD in CeD.