Citations

Background: Right atrial thrombus (RAT), though rare, carries a high risk of pulmonary or systemic embolism and a 25–44% mortality rate. In LVAD candidates, coexisting RAT significantly complicates perioperative management due to their critical condition and high risk of decompensation during surgery. Percutaneous thrombectomy offers a less invasive, lower-risk alternative to open surgery, enabling faster recovery. Case Presentation: We present a case of a 76-year-old male with ischemic cardiomyopathy and heart failure secondary to Adriamycin chemotherapy with an ejection fraction (EF) of 26%, diabetes mellitus, and stage 3 chronic kidney disease, scheduled for left ventricular assist device (LVAD) implantation. Preoperative echocardiography revealed a large, multilobulated, mobile thrombus in the right atrium. Given the high surgical risk and thrombus mobility, percutaneous thrombectomy using the AngioVac system (Angiodynamics, Latham, NY, USA) was performed. The thrombus was successfully removed, and the patient was discharged on apixaban for long-term anticoagulation. Advanced medical therapy and transplant/durable LVAD evaluation were delayed by 35 days to allow for the stabilization of any postoperative effects. Conclusions: This case underscores the efficacy of percutaneous thrombectomy as a bridge to LVAD in high-risk patients, reducing thromboembolic complications and supporting safe LVAD implantation.

Coronary anomalies, although rare, should be considered when young patients present with angina. Clinical suspicion and multi-modality imaging including coronary angiography and tomographic imaging should be pursued for symptomatic patients such as the one we are presenting with anomalous right coronary artery from the pulmonary artery. She was promptly referred for surgical intervention with re-implantation of the right coronary artery onto the aorta.

Abstract Background: Spatial Reasoning AI (SRAI) systems are reshaping clinical practice by enabling real-time interpretation of complex anatomical structures in medical imaging, surgery, and chronic disease management (Lin et al., 2023, Qiao et al., 2021; Song et al., 2024) . Unlike traditional AI approaches that rely on tabular or 2D data, SRAI models incorporate geometric priors, multimodal fusion, and 3D anatomical reasoning to support high-impact tasks such as volumetric segmentation, disease localization, and intraoperative guidance (Milletari et al., 2016; Hatamizadeh et al., 2022; Zhang et al., 2022) . Objective: This scoping review aimed to (1) characterize the emerging landscape of spatial reasoning models in healthcare, (2) evaluate their clinical performance across diagnostic and prognostic applications, and (3) identify architectural trends that underpin successful translation into practice. Methods: Following PRISMA guidelines, we systematically screened PubMed to identify studies that implemented spatially aware AI models with real-time or clinically validated outcomes (Page et al., 2021) . Data extraction captured model architecture, modality, anatomical scope, clinical application, and performance metrics. Results: We identified 124 eligible studies across the clinical domains of oncology, neurology, cardiology, orthopedic surgery, and infectious diseases (Nie et al., 2019; Hao et al., 2020; Tasken et al., 2023) . SRAI systems consistently outperformed conventional methods in segmentation, lesion classification, and risk stratification. (Lin et al., 2023; Qiao et al., 2021; Mahootiha et al., 2024) . A paradigm shift from task-specific CNNs to foundation models that reason across spatial structures has emerged, beginning with adaptations of generalist models such as MedSAM and MedCLIP for zero-shot segmentation and cross-modal retrieval, and NeRF-based reconstruction for anatomically faithful 3D modeling (Ma et al., 2024; Mildenhall et al., 2020; Radford et al., 2021). Beyond technical performance, randomized evidence shows that SRAI systems can improve workflow efficiency without compromising outcomes (Lang et al., 2023) . Conclusion: SRAI models hold promise for enhancing diagnostic accuracy, prognostic precision, and clinical efficiency across diverse applications. Their successful integration will depend on standardized evaluation metrics, robust validation, and interpretable outputs. As foundation models evolve to better encode spatial priors, SRAI is poised to become a cornerstone of personalized and procedural medicine.

PURPOSE Patients with advanced, well-differentiated extrapancreatic neuroendocrine tumors (epNETs) have limited systemic treatment options. Pazopanib, an oral multikinase inhibitor with activity against vascular endothelial growth factor receptor (VEGFR)-2 and -3, PDGFR-alpha and-beta, and c-Kit, was tested for efficacy in epNET. PATIENTS AND METHODS We conducted a multicenter, randomized, double-blind, phase II study of pazopanib (800 mg once daily) versus placebo in low- to intermediate-grade epNET with radiologic progressive disease (PD) within 12 months of study entry. Previous somatostatin analog (SSA) was required for midgut tumors, and concurrent SSA was allowed. The primary end point was progression-free survival (PFS) by blinded independent central review. Unblinding and crossover were allowed if PD was confirmed by central review. RESULTS One hundred seventy-one patients (97 pazopanib and 74 placebo) were randomly assigned between September 2013 and October 2015. The majority had a midgut primary site (75%) and previous SSA treatment (93%). About half (49%) of the patients had functional tumors. The median follow-up was 61 months (95% CI, 60 to 63). Median PFS was 11.8 versus 7.6 months in pazopanib versus placebo, respectively (hazard ratio, 0.54 [95% CI, 0.37 to 0.79]; P < .001); 49 placebo patients crossed over to pazopanib. There was no significant difference in overall survival between the treatment arms. Rates of grade 3 or greater adverse events (regardless of attribution) were higher in pazopanib versus placebo (84% v 47%; P < .001), as were grade 5 death events (8% v 0%, P = .017). CONCLUSION Pazopanib compared with placebo significantly improves PFS in patients with progressive epNET, confirming that the VEGF signaling pathway is a valid target for therapy in epNET. However, after integrating the associated risks relative to the benefits, further development of pazopanib in this clinical context is not planned.

Abstract We investigated the influence of 55 583 autophagy-related single-nucleotide polymorphisms (SNPs) on chronic lymphocytic leukemia (CLL) risk across 4 independent populations comprising 5472 CLL cases and 726 465 controls. We also examined their impact on overall survival (OS), time to first treatment (TTFT), autophagy flux, and immune responses. A meta-analysis of the 4 populations identified, to our knowledge, for the first time, significant associations between CDKN2A (rs3731204) and BCL2 (rs4940571, rs12457371, and rs1026825) SNPs and CLL risk, with CDKN2A showing the strongest association (P = 1.57 × 10−12). We also validated previously reported associations for FAS, BCL2, and BAK1 SNPs with CLL risk (P = 4.73 × 10−21 to 3.39 × 10−9). The CDKN2Ars3731204 and FASrs1926194 SNPs associated with increased CDKN2A and ACTA2 messenger RNA expression levels in the whole blood and/or lymphocytes (P = 5.1 × 10−7, P = 1.58 × 10−21, and P = 7.8 × 10−41), although no significant effect on autophagy flux was observed. However, associations were found between CDKN2A, BCL2, and FAS SNPs and various T-cell subsets, cytokine production, and circulating concentrations of interferon gamma, tumor necrosis factor–related apoptosis-inducing ligand, CD40, chemokine ligand 20, and interleukin-2 receptor subunit β proteins (P ≤ .005). No significant association was detected between autophagy variants and OS or TTFT, suggesting that these variants drive disease initiation rather than progression. In conclusion, this study identified 4 novel associations for CLL and provided insights into the biological pathways that influence CLL development.

Objective To update, synthesise and provide meta-analytical evidence of the associations between domain-specific physical activity (PA) and mental health and mental ill-health outcomes. Design Systematic review and multilevel meta-analysis. Data sources In March 2024, we systematically searched five databases. Eligibility criteria Methods employed replicated those of a previous review in 2017. All studies examining associations between domain-specific PA and specified mental health outcomes were included. Results 372 studies met inclusion criteria and 361 were included in the meta-analysis. Across the 372 studies (combined sample size of 3 323 711), 338 examined leisure-time PA, 54 work-related PA, 72 transport-related PA, 44 household PA, 5 school sport and 8 physical education. Multilevel meta-analyses showed that leisure-time PA (r=0.205, 95% CI 0.157 to 0.253), transport-related PA (r=0.138, 95% CI 0.042 to 0.231) and household PA (r=0.096, 95% CI 0.025 to 0.165) were positively associated with mental health . Leisure-time PA (r=−0.149, 95% CI −0.189 to –0.11) and school sport (r=−0.096, 95% CI −0.115 to –0.077) were inversely associated with mental ill health . However, work-related PA (r=0.134 95% CI 0.069 to 0.199) was positively associated with mental ill health . Conclusion The direction of the association between PA and mental health/mental ill health is dependent on the domain in which PA occurs. Promoting PA for leisure purposes is likely to yield the greatest benefits for both promoting mental health and preventing mental ill health. As such, leisure-time PA should be prioritised in messaging, guidelines and interventions/programmes designed to support mental health through PA. PROSPERO registration number CRD42024510303.