Citations

Background: Tobacco use is a known risk factor for poor healing after rotator cuff repair (RCR), but the effects of nontobacco nicotine products remain unclear. Purpose: To compare postoperative complications among nontobacco nicotine users, tobacco users, and nonusers undergoing RCR. Study Design: Cohort study; Level of evidence, 3. Methods: Using the PearlDiver database, the authors identified patients who underwent arthroscopic RCR with ≥2 years of follow-up. Three 1:1 propensity-matched comparisons were conducted: nontobacco nicotine users versus nonusers (n = 4844), tobacco users versus nonusers (n = 19,182), and nontobacco nicotine users vs tobacco users (n = 4882). Outcomes included 90-day complications and 2-year revision surgeries. Results: Nontobacco nicotine users had mildly higher rates of lysis of adhesions than controls (0.7% vs 0.4%; OR, 1.95; P = .033) but no other significant differences. Tobacco users showed increased rates of acute kidney injury (AKI), pneumonia, infections, emergency department (ED) visits, readmissions, and revision surgery compared to controls. Compared to tobacco users, nontobacco nicotine users had moderately strong lower risks of pneumonia (0.6% vs 1.2%; OR, 0.46), ED visits (8.8% vs 13.6%; OR, 0.62), and readmissions (0.8% vs 1.5%; OR, 0.54) (all P < .01). Conclusion: While nontobacco nicotine use was associated with a modestly increased risk of lysis of adhesions after RCR, it did not significantly elevate the risk of major medical complications, infections, or revision surgery when compared to nonnicotine users. In contrast, tobacco users demonstrated consistently worse postoperative outcomes across multiple domains, including higher rates of AKI, pneumonia, surgical site infection, ED visits, readmissions, and revision procedures. Notably, when compared directly to tobacco users, nontobacco nicotine users had significantly lower rates of pneumonia, ED utilization, and readmissions. These findings suggest that although nontobacco nicotine products may not be entirely benign, their perioperative risk profile appears to be less severe than that of traditional tobacco use.

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.

Importance Timely identification of aortic stenosis (AS) is essential for appropriate clinical management, yet screening remains limited by dependence on comprehensive echocardiography and trained imaging personnel. Objective To develop and validate a deep learning algorithm for detection of moderate or greater AS and prospectively evaluate its performance using artificial intelligence (AI)–guided focused cardiac ultrasound (FoCUS) acquired by novice operators. Design, Setting, and Participants This diagnostic study included retrospective algorithm development and validation and prospective evaluation of AI-guided FoCUS across Mayo Clinic sites in the Midwest, Arizona, and Florida. The model was developed using 6753 patients and evaluated in internal validation (n = 852), internal test (n = 844), and validation (n = 1912) cohorts. Performance was assessed on FoCUS acquired by experienced sonographers (n = 602) and prospectively by novice operators (n = 1302). The retrospective model development and validation cohorts comprised studies performed from January 2005 through September 2022. Prospective study was conducted in 2 enrollment periods from June to August 2024 and from June to September 2025. Participants from both periods were combined to comprise the final prospective cohort. Exposure AI-guided FoCUS acquisition and automated deep learning–based assessment for detection of moderate or greater AS. Main Outcomes and Measures Detection of moderate or greater AS. Performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and positive predictive value. Results The model demonstrated excellent discrimination in the internal test cohort (AUROC, 0.99; 95% CI, 0.98-1.00) and geographically distinct validation cohorts in Arizona (AUROC, 0.99; 95% CI, 0.97-1.00) and Florida (AUROC, 0.99; 95% CI, 0.96-1.00). Among FoCUS examinations acquired by experienced sonographers, sensitivity was 95% (95% CI, 82-99) and specificity was 97% (95% CI, 95-98). In the prospective novice-operator cohort, 1258 of 1302 examinations (96.6%) were suitable for automated analysis. Sensitivity was 93% (95% CI, 82-99) and specificity was 96% (95% CI, 95-97). Expert review of AI-positive and uninterpretable examinations increased the positive predictive value from 49.4% to 91.1%, with sensitivity of 85.4%. Conclusions and Relevance A deep learning algorithm accurately detected moderate or greater AS across validation cohorts in this study. In prospective evaluation, novice operators were able to acquire AI-guided FoCUS examinations that enabled accurate detection of moderate or greater AS. These findings support a scalable strategy that may expand access to AS detection in settings with limited echocardiography resources.

Tissue harmonic and spatial compound imaging, widely used US techniques, improve image quality by reducing noise and artifacts through complementary mechanisms.

Abstract Identification of gene expression changes in post-mortem brain tissue of Alzheimer’s disease donors compared to controls have implicated numerous biological pathways for Alzheimer’s disease pathophysiology. Nonetheless, there is still limited understanding of how gene expression dysregulation underpins specific proteinopathies core to Alzheimer’s disease. Here we investigate brain transcriptomic changes in a well characterized cohort of Alzheimer’s disease donors to identify genes and networks that associate with Alzheimer’s disease endophenotypes including neuropathology measures (Braak stage, Thal phase and cerebral amyloid angiopathy score) and Alzheimer’s disease-related brain protein levels (Apolipoprotein E, Amyloid-β 40, Amyloid-β 42, tau, and phospho-Tau). Bulk transcriptome measures were collected from the temporal cortex tissue of 477 Alzheimer’s disease donors. Following quality control, transcriptome-wide association studies were performed for each endophenotype. We used weighted gene co-expression network analysis to build co-expression networks and integrated transcriptome with epigenetic and genetic data from the same donors. We detected a total of 5,740 Bonferroni significant temporal cortex gene associations with Alzheimer’s disease endophenotypes, most of which were with brain tau levels. We discovered tau-associated co-expression modules enriched in known and novel Alzheimer’s disease pathways. We found that a beneficial (or neutral) brain biochemical state of higher total tau and lower phospho-Tau are associated with increased levels of synaptic, DNA damage/repair, nucleic acid metabolism and myelin processes. In contrast, in a detrimental state of lower total and higher phospho-Tau, there is upregulation of vascular and immune, and downregulation of mitochondrial and myelin pathways. There are brain gene expression perturbations that are associated with Alzheimer’s disease endophenotypes. While some of these associations are common across multiple endophenotypes, many are distinct for different Alzheimer’s disease-related proteins. Based on these findings, we propose a hypothetical model of dynamic brain gene expression changes that track with progressive Alzheimer’s disease proteostasis. These expression changes hold potential to serve as dynamic, precision biomarkers of brain Alzheimer’s disease progression. This study demonstrates the potential of integrative multiomics and deep Alzheimer’s disease endophenotype analyses in well-characterized brain tissues to uncover with precision the complex biology of Alzheimer’s disease.

CD19-directed CAR T-cells (CTL019) can produce durable remissions in chronic lymphocytic leukemia (CLL), but therapeutic success depends on whether autologous T-cells expand, persist, and retain cytotoxic function after manufacturing. Failure of CLL T-cells is often attributed to exhaustion, although many dysfunctional CLL T-cells retain inflammatory cytokine production. We tested whether this paradox reflects immunosenescence, an aging-like program defined by costimulatory loss, DNA damage, inflammatory secretion, repertoire restriction, and proliferative arrest. In response-linked preinfusion CTL019 products, nonresponders and short partial responders had higher senescence and senescence-associated secretory phenotype (SASP) programs than the functional-responder group, comprising complete responders and partial responders with transformed disease. These programs were detectable before infusion and became more prominent during manufacture in susceptible products. They tracked with weak in vivo CAR T-cell expansion and were associated with inferior survival. Serum proteomics revealed a circulating, CLL-associated SASP-like inflammatory milieu in nonresponders. Flow cytometry and T-cell receptor (TCR) profiling showed that poor responders carried CD27⁻CD28⁻ and KLRG1⁺ CD8 T-cells at apheresis, lower product CD27, and reduced product TCR diversity. Research-manufactured CAR T-cells from treatment-naïve CLL samples displayed the same state, most prominently in CD4 CAR T-cells, with SA-β-gal, p16, p53, DNA damage, depletion of less-differentiated states, and senescence-enriched transcriptomes. Under repeated CD19 stimulation, CLL-derived products reached a proliferative ceiling, and p53-high products lost cytotoxic reserve. Ibrutinib improved proliferative fitness, attenuated senescence-associated features and SASP output in paired patient and direct-exposure assays, and enhanced CAR T-cell expansion in an ibrutinib-resistant CLL model. Together, these data identify immunosenescence as a measurable and functionally consequential barrier to CAR T-cell efficacy in CLL and a candidate for therapeutic modulation.

Background: Several breast cancer (BC) risk prediction models have been developed to provide personal risk assessments. Though individually validated, their performance has not been systematically evaluated across a wide range of populations or ages. Methods: We harmonized individual-level baseline questionnaire data and incident BC diagnoses from 21 cohorts from North America, Europe, and Australia participating in the Breast Cancer Risk Prediction Project. Five-year absolute risk of invasive BC was estimated for five established risk prediction models using classical risk factors only. Discrimination was evaluated by area under the curve (AUC). Calibration was assessed using average and risk-decile specific expected to observed (E/O) ratios. Performance metrics were meta-analyzed across cohorts and models. Metaregression tested associations between cohort characteristics and performance metrics. Results: This analysis included 1,595,977 women aged 20-75 years, enrolled in studies between 1976-2015, with 19,062 (1.2%) invasive BC cases ascertained within 5 years from exposure assessment. Age-adjusted AUCs were similar across models and cohorts (pooled AUCs by model: 0.57-0.58), while E/O ratios varied substantially (pooled E/O ratios by model: 0.83-1.25). Overestimation was common among predicted high-risk individuals (>3%). No appreciable differences in model performance by cohort age, birth year, race, and variable missingness emerged. Calibration improved after assigning race-specific incidence rates. Conclusion: Existing BC risk prediction models provided similar risk discrimination across multiple cohorts, although there was overestimation of risk for high-risk individuals. Performance variation across cohorts was not driven by specific characteristics, which supports development of a unified risk model for diverse populations that leverages appropriate incidence rates.

Abstract Objective To assess the efficacy and safety of fenfluramine in patients with Dravet syndrome (DS) stratified by age, number of previously attempted antiseizure medications (ASMs), and SCN1A pathogenic variant status. Methods In this post hoc analysis, data from three randomized controlled trials (RCTs) in patients with DS (2–18 years) were pooled and stratified by age (<4; ≥4 years), number of previous ASMs (1–3; 4–6; ≥7), and SCN1A pathogenic variant status ( SCN1A +; SCN1A −). Stratified groups were assessed and compared with the pooled placebo group (change in monthly convulsive seizure frequency [MCSF], longest convulsive seizure‐free interval, and Clinical Global Impression–Improvement [CGI–I] scale scores rated by parents/caregivers and investigators), and safety (treatment‐emergent adverse events [TEAEs]: frequency, days to onset, and proportion resolved). Results Among 348 patients included in the RCTs, 216 were randomized to fenfluramine (0.7 mg/kg/day, n = 88; 0.4 mg/kg/day [with stiripentol], n = 43; 0.2 mg/kg/day, n = 85) and 132 to placebo. Compared with placebo, fenfluramine treatment (all doses combined) resulted in greater MCSF reductions, greater increases in longest convulsive seizure‐free intervals, and a higher proportion of parents/caregivers and investigators reporting clinically meaningful improvement (“Much Improved”, “Very Much Improved”) on CGI–I scores across all stratified groups. CGI–I scores were consistent across fenfluramine doses in most stratified groups, but patients with the fewest number of previous ASMs had the greatest frequency of clinically meaningful improvement on investigator‐rated CGI–I scores. Safety outcomes were similar across all strata. Most TEAEs resolved by end‐of‐study. Significance Fenfluramine treatment was associated with improved seizure outcomes and global functioning compared with placebo regardless of age, number of previous ASMs, and SCN1A status in patients with DS. Fenfluramine was well‐tolerated; no new safety signals were identified. Further studies with larger sample sizes (including adults) and a priori inferential analyses of stratified groups are warranted. Plain Language Summary Patients with Dravet syndrome struggle with seizures and everyday life. In three studies, patients aged 2–18 years received fenfluramine or placebo (sugar pill). Fenfluramine lowered seizures without many side effects. Researchers combined results from these studies to see how fenfluramine worked in different patient groups based on age, number of previous medications, and a gene called SCN1A . They looked at seizure reduction and whether doctors felt patients had improved. In all groups, fenfluramine worked better than placebo, with similar side effects. Researchers believe fenfluramine helped these patients, but some groups were small, so these results need to be confirmed.