Citations

Abstract To characterize invasive neuromodulation for insular drug‐resistant epilepsy, we retrospectively evaluated 22 patients treated with responsive neurostimulation (RNS; n = 14) or chronic subthreshold stimulation (CSS; n = 8). Stereoelectroencephalography was performed in 20 cases (91%). Median baseline seizure frequency was 10.5/month. Forty‐one percent had prior epilepsy surgery. At year 3, median seizure reduction (MSR) was 83% ( p = .01, Wilcoxon signed‐rank test), with a responder rate (RR) of 77%; MSR was 92% for CSS and 75% for RNS. There were no significant differences in MSR ( p ≥ .4) or RR ( p > .9) when comparing RNS versus CSS. Three patients achieved seizure freedom by year 3. Patients undergoing combined insular–thalamic stimulation ( n = 5) demonstrated an MSR of 84%, 86%, and 88% at 1, 2, and 3 years; seizure reduction in thalamocortical versus cortical‐only stimulation was not different ( p ≥ .1). Parasagittal lead trajectories achieved closer insular proximity than transopercular trajectories ( p < .0001, Mann–Whitney U ‐test). Median charge density at last follow‐up was 1.3 μC/cm 2 . There were no acute surgical complications and all stimulation‐related side effects resolved with programming adjustments. This study expands the limited literature on insular neuromodulation by demonstrating effectiveness of RNS and CSS, suggesting potential benefit from combined thalamocortical targeting, and defining parasagittal trajectories as a surgical approach for optimal lead proximity.

Background Diastolic dysfunction is common in patients with aortic stenosis and may influence outcomes following surgical aortic valve replacement. We aimed to examine the association of preoperative artificial intelligence (AI)‐generated diastolic function grades with early and late outcomes following aortic valve replacement and how postoperative progression influence prognosis. Methods We identified 5503 patients undergoing aortic valve replacement between 2000 and 2023. Diastolic function was assessed using a validated deep‐learning AI model applied to 12‐lead ECGs done preoperatively and on postoperative follow‐up. Diastolic grades were classified by AI into Grades 1 to 3. Longitudinal trend analyses and multivariable regression models were used to assess study end points. Results Among 5503 patients (mean age 72.4±10.8 years; 39% female), higher AI ECG diastolic grades were associated with greater comorbidity burden, including diabetes, renal disease, and heart failure. AI ECG diastolic Grade 3 was independently associated with higher in‐hospital mortality (odds ratio, 2.5; P =0.007) and other complications. At 5‐year follow‐up, patients with Grade 3 showed the least improvement in diastolic function by both ECG and echocardiography. Grades 2 and 3 diastolic function at baseline were independently associated with increased late mortality (hazard ratio, 1.3 and 2.45, respectively; both P <0.001). Additionally, lack of improvement in AI ECG diastolic grade by 1 year was also independently associated with late mortality. Conclusions AI ECG–derived diastolic function grades strongly correlates with early complications and long‐term mortality and diastolic progression after aortic valve replacement. AI ECG provides a powerful, noninvasive tool for risk stratification and longitudinal monitoring of patients with aortic stenosis.

Background Pericarditis may be associated with significant morbidity and mortality and can be a clinically challenging entity to diagnose. There is a clinical need for automated artificial intelligence (AI) algorithms to act as a screening aid to assist with the early and accurate detection of pericarditis. Methods We developed p-Cal, a browser-based pericarditis risk calculator that fuses the patient clinical data and 12-lead ECG image to estimate risk for pericarditis. The ECG encoder was designed based on a pretrained MedCLIP Vision Transformer and random forest classifier was used for the clinical tabular data. These two modalities were fused at the decision-level using a meta classifier. Model reasoning for both modalities was assessed using advanced explainable AI techniques—attention maps from the transformer model were used to interpret ECG images, while SHapley Additive exPlanations (SHAP) force plots were applied to analyse the tabular data. Results A total of 6508 patients (mean age 54.8±16.1 years, 50.9% female) were used for training and internal validation of the AI model, of whom 902 patients had a confirmed diagnosis of pericarditis (13.8%) and the remainder were normal controls. On 1302 hold-out test patients, the Fusion Model demonstrated the best discriminatory performance, achieving an area under the curve (AUC) of 0.89, which surpassed the ECG (AUC = 0.85) and the clinical data only models (AUC = 0.74) performance. On external validation in the Medical Information Mart for Intensive Care (MIMIC) Database, the fusion model achieved an AUC of 0.81. Conclusion A late fusion AI model integrating tabular clinical electronic medical record data and ECG signal has robust predictive capability for pericarditis as a screening tool.

Chimeric antigen receptor (CAR) T-cell therapy is a transformative modality for refractory hematologic malignancies; however, its adoption in Latin America and the Caribbean (LAC) is severely constrained by prohibitive manufacturing costs, limited specialized infrastructure, and fragmented regulatory frameworks. To address this translational gap, we propose a pragmatic, cost-adapted model for establishing Good Manufacturing Practice (GMP) laboratories tailored to the socioeconomic realities of LAC. This model advocates for decentralized, modular cleanroom facilities that integrate fully closed automated manufacturing platforms with risk-based quality management systems. Furthermore, we emphasize the strategic implementation of artificial intelligence (AI) and digital health tools—such as electronic batch records and predictive analytics—to optimize resource utilization and ensure stringent regulatory compliance. Essential to this framework is the cultivation of a specialized local workforce and the promotion of regional regulatory harmonization through initiatives like PAHO’s Red PARF. Ultimately, this scalable approach provides a realistic roadmap to democratize CAR T-cell therapy in LAC, fostering regional biomanufacturing autonomy and promoting equitable patient access.

ABSTRACT Endoluminal flow diverters are increasingly used to treat cerebral aneurysms. Despite good aneurysm occlusion efficacy, delayed ischemic complications due to side branch or perforator occlusion remain a serious concern. However, the mechanisms leading to side branch occlusion are poorly understood. To investigate these mechanisms, a combination of animal and computational models was utilized. Flow diverters were deployed in four rabbit superior mesenteric artery (SMA) and studied with computational fluid dynamics (CFD) and a previously developed fibrin dynamics model. Subject‐specific vascular models were reconstructed from 3D rotational angiography images and flow conditions were derived from Doppler ultrasound velocity measurements. Flow diverting devices were virtually deployed within the vascular models under guidance from angiography images. Coupled flow and fibrin production, transport, and adhesion simulations were carried out and the effects of fibrin accumulation at the origin of jailed side branches after flow diverter implantation were analyzed. The results of the computational models suggest significant fibrin accumulation across the struts of all flow diverting devices jailing side branches. Larger accumulation was observed at the more distal branches. However, minor flow alterations were produced which were not detectable in angiography sequences. These results were in agreement with experimental observations. On the other hand, fibrin accumulation alone did not explain complete side branch occlusions, which are likely the result of the interaction between initial fibrin accumulation and subsequent cell migration from the peripheral parent artery.

Obesity is common among critically ill adults, but its association with outcomes during venovenous extracorporeal membrane oxygenation (VV ECMO) remains controversial. We evaluated associations of body mass index (BMI) with mortality and Extracorporeal Life Support Organization (ELSO)–defined complications in adults supported with VV ECMO. We conducted a retrospective multicenter cohort study of adults receiving VV ECMO at four centers (2017–2025). The primary outcome was in-hospital mortality; secondary outcomes were ELSO-defined complications. Multivariable logistic regression was adjusted for age, sex, and hospital site; BMI was evaluated as continuous, dichotomized (≥30 vs . <30 kg/m 2 ), and World Health Organization (WHO) obesity class exposures, with interaction testing by coronavirus disease 2019 (COVID-19) status. Among 420 patients (median BMI, 31 kg/m 2 ), in-hospital mortality was 62%. Higher BMI was associated with mortality in non–COVID-19 acute respiratory distress syndrome (ARDS; odds ratio [OR] = 1.06 per 1 kg/m 2 , 95% confidence interval [CI] = 1.02–1.10, p = 0.003) but not in COVID-19 ARDS (OR = 1.00, 95% CI = 0.94–1.05); there was insufficient evidence of interaction between BMI and COVID-19 ( p = 0.07). Higher BMI was associated with renal complications in both groups and infectious complications in non–COVID-19 ARDS (OR = 1.05, 95% CI = 1.02–1.09, p = 0.003). No significant BMI × COVID-19 interaction was detected for any outcomes. These findings do not support an obesity survival paradox during VV ECMO.

Abstract Glioblastoma (GBM) remains the most lethal primary brain cancer due to its remarkable metabolic plasticity and therapeutic resistance. Here, we identify cholesterol dependency as a therapeutically exploitable vulnerability in GBM using two FDA-approved drugs: the H1 histamine antagonist clemastine and the retinoid X receptor agonist bexarotene. Combined treatment induces potent synergistic anti-tumor activity across patient-derived glioma models, suppressing proliferation, stemness, and survival at sub-IC50 concentrations. Mechanistically, this therapy disrupts cholesterol biosynthesis, transport, and homeostasis, triggering endoplasmic reticulum stress and activation of the unfolded protein response, ultimately leading to autophagy and apoptotic cell death. Orthotopic patient-derived glioma models recapitulate these mechanisms in vivo, where local intracranial administration significantly reduces tumor progression and prolongs survival using fourfold lower doses than systemic intraperitoneal delivery. Single-cell RNA sequencing revealed activation of regeneration and plasticity programs, accompanied by immune microenvironment remodeling and enhanced inflammatory signaling. Importantly, syngeneic models preserved immune cell composition, supporting future integration with immunotherapeutic strategies. Together, these findings establish cholesterol dysregulation–induced metabolic collapse as a promising therapeutic approach for GBM.

ABSTRACT Background and Aim Significant health disparities persist in alcohol‐associated liver disease (ALD), largely driven by social determinants of health (SDOH). However, the influence of neighbourhood‐level SDOH on ALD outcomes remains underexplored. In this study, we aimed to evaluate how neighbourhood‐level SDOH are associated with disease burden, comorbid conditions, and mortality in individuals with ALD. Methods We conducted a retrospective cohort study of patients with ALD in the Banner Health System, representing hospitals across Arizona, California, Nevada, Wyoming and Colorado, from January 2012 to October 2024 using ICD codes. Neighbourhood‐level SDOH were quantified using the Social Deprivation Index (SDI). Patients were stratified into quartiles based on SDI score, with Quartile 4 representing the most socioeconomically disadvantaged neighbourhoods. Primary outcomes included incidence of mortality, cirrhosis, major adverse liver outcome (MALO), Type 2 diabetes mellitus, any cancer, and major adverse cardiovascular events (MACE). A multivariable competing risk analysis was performed adjusting for age, sex, race/ethnicity, insurance type, primary language, smoking status, Type 2 diabetes mellitus, hypertension, hyperlipidemia, aspirin use, statin use, alcohol use disorder and a composite mental‐health/substance‐use disorder variable encompassing depression, anxiety disorders, post‐traumatic stress disorder, bipolar disorder, schizophrenia, and non‐alcohol substance use disorders. Results Among 11,394 patients with ALD and available SDI data, 6747 had ≥ 365 days of follow‐up. The average age was 51 years and 65.6% were female; 68.8% were non‐Hispanic White, 19.7% Hispanic, 5.9% Native American/Alaskan Native (NA), 3.2% Black, and 0.4% Asian/Pacific Islanders. Patients residing in the most disadvantaged neighbourhoods (Quartile 4) were significantly younger, more likely to be female, had a higher median BMI, and more frequently reported a non‐English primary language compared with those in the least disadvantaged areas (Quartile 1). Patients in the most deprived neighbourhoods were more likely to be Hispanic, Black, or Native American/Alaska Native and to be uninsured or on Medicaid, whereas those in the least disadvantaged neighbourhoods predominantly had private insurance. Individuals in Quartile 4 had increased mortality (adjusted hazard ratio [aHR]: 1.48, 95% Confidence Interval [CI]: 1.09–2.01) compared with individuals in Quartile 1. Conclusions In this large and demographically diverse ALD cohort, residing in socioeconomically disadvantaged neighbourhoods was associated with increased risk of mortality. These findings support the need for contextually tailored clinical interventions and broader treatment strategies that target upstream sociocultural and environmental factors that are associated with health outcomes. Addressing neighbourhood‐level SDOH through community engagement, policy reform, and cross‐sector partnerships to improve collaborative care efforts is needed to reduce health disparities in ALD.

Background Immune checkpoint inhibitors (ICI) improve cancer outcomes but are associated with increased atherosclerotic cardiovascular risk. Opportunistic coronary artery calcium (CAC) assessment from routine non-gated computed tomography (CT) using artificial intelligence (AI) may enable rapid cardiovascular risk stratification in this population. We evaluated the association between AI-derived CAC (AI-CAC) scores and 1-year myocardial infarction (MI) and all-cause mortality in patients receiving ICI therapy. Methods We conducted a retrospective multicenter study across three Mayo Clinic sites including patients who underwent chest CT within 3 months prior to ICI initiation between 2011 and 2022. Patients with prior coronary revascularization were excluded. AI-CAC scores derived from non-gated CT scans were categorized as 0, 1–99, 100–399, and ≥400. Cox regression analyses assessed associations with 1-year MI and all-cause mortality. Results Among 1,873 patients (mean age 66 years; 43% women), lung cancer was most common (50.3%), and 91% had at least one cardiovascular risk factor. Higher CAC burden was associated with increased 1-year MI risk (overall p=0.0032). AI-CAC was not associated with all-cause mortality (p=0.79), likely reflecting high cancer-related mortality (50.7% at 1 year). Only 15% of patients were receiving statins despite one-third having CAC >100. Conclusions Opportunistic AI-CAC assessment from routine CT scans provides modest prognostic value for MI risk among patients receiving ICI therapy and may identify opportunities to optimize preventive cardiovascular care. Elevated CAC should prompt risk factor modification rather than influence ICI treatment decisions. Prospective studies are warranted to evaluate clinical implementation and outcomes.