Citations

Key Points Increased protein expression of tissue inhibitor of metalloproteinase-3, glypican 6, synuclein γ , apolipoprotein A-4, and Ecto-5′-nucleotidase was observed in old adults compared with young adults. In old adults, overexpressed proteins were enriched in mitochondrial processes while under expressed proteins enriched in RNA metabolism. Validated differentially expressed proteins were not significantly correlated with kidney pathology after adjusting for age. Background Kidney aging is characterized by a loss of glomeruli, predominantly in the superficial cortex, with a resultant decline in GFR and an increased risk of various kidney-related diseases. The early molecular alterations in glomeruli associated with the aging process are not well studied. Methods We combined laser capture microdissection and mass spectrometry–based unbiased proteomic analysis of nonsclerosed, nonischemic glomeruli in the superficial cortex from young and old adults who underwent a radical nephrectomy for a tumor to understand the age-related molecular changes in glomeruli. Twenty-four young and 30 old adults were used for the discovery dataset, and the significant differentially expressed proteins were further validated using an independent set comprising six young and eight old adults. Results Kidneys from old adults had lower eGFR, less kidney parenchyma on computed tomography imaging, and more glomerulosclerosis and arteriosclerosis on histology. Quantitative proteomic analysis of nonsclerosed, nonischemic glomeruli identified increased expression of tissue inhibitor of metalloproteinase-3, glypican 6, synuclein γ , apolipoprotein A-4, and Ecto-5′-nucleotidase in old adults that were further validated in an independent set. Pathway analysis indicated that proteins with increased expression in old adults were enriched in mitochondrial translational processes, aerobic respiration, and TCA cycle, whereas proteins with decreased expression in old adults were enriched in mRNA splicing, mRNA processing, and nonsense mediated decay. Furthermore, validated differentially expressed proteins did not show any significant correlation with the kidney pathology independent of age group. Conclusions Overall, this study identified proteins that are specifically associated with the aging process in otherwise normal-appearing glomeruli.

Abstract Background The isocitrate dehydrogenase (IDH) mutation status is a key molecular marker in diagnosing and treating brain tumors. Currently, it is determined via invasive tissue biopsy. Recent advances in deep learning (DL) have offered promising non-invasive alternatives for determining IDH status. However, their clinical translation is hindered by a significant gap between DL predictions and their clinical applicability. The limited transparency of many DL-networks and inadequate evaluation metrics hinders trust and adoption, as clinicians require clear and validated insights for determining IDH status. These challenges highlight the need for robust validation and measures of predictive reliability to make DL-predictions clinically actionable. Methods We developed a unique approach for non-invasive prediction of IDH status using MRI. We combine a voxel-wise-segmentation network(MC-net) with Bayesian logistic regression (BLR) to provide an IDH status and estimate confidence scores. We utilized a comprehensive dataset of 2,481 glioma cases from eight institutions. Results Our framework(MC-net + BLR) demonstrated robust performance achieving 96.4% and 95.1% classification accuracies on diverse databases, with an AUC of 0.98. The BLR was implemented exclusively on held-out test data, ensuring that the derived confidence scores are independent of the training or validation phases. The derived confidence scores showed a low Brier score of 0.0125, highlighting its superior calibration and uncertainty quantification. Conclusion The developed framework provides an IDH status and a confidence score, offering clinicians an additional layer of assurance in prediction reliability. It bridges the gap between high-performing DL models and their clinical applicability by addressing the challenges in prediction reliability. Our framework is a significant advancement in non-invasive determination of IDH-status and confidence-informed therapeutic decision-making in neuro-oncology.

ABSTRACT Rationale Longer duration of epilepsy before surgery is a predictor of poor outcome. While referral delays of surgical candidates are well documented, factors causing delay during the presurgical evaluation remain unclear and may vary depending on institutional characteristics. By benchmarking the duration of presurgical evaluation across multiple centers and identifying patient and evaluation characteristics contributing to duration, we can ascertain best practices and address modifiable contributors to reduce delays. Methods We queried the Pediatric Epilepsy Research Consortium Surgery Database, a prospective, observational multicenter study enrolling children 0–18 years at 27 US pediatric epilepsy centers, for all patients undergoing initial presurgical evaluation for drug‐resistant epilepsy (DRE). We included patients with completed evaluations and data on duration from initiation of presurgical evaluation to final surgical decision. We compared patient characteristics and evaluation components between those with long duration evaluations (> 75% quartile) and those with short evaluations (< 25% quartile). Akaike information criteria selection identified variables associated with longer duration. From these, we developed a logistic prediction model for evaluation duration, using a random 80/20 training/testing split of the entire cohort. The model was tested among institutions with ≥ 10 patients in the cohort to assess its accuracy in predicting long durations. Linear models for each site assessed each variable's impact on duration. Variables with < 10% of the patient population at each site were excluded. Beta values were compared to identify intra‐ and inter‐institution variability and to delineate institutions with the shortest added duration for each variable. Results Of 2318 patients undergoing surgical evaluation, 1655 (71%) from 23 sites had complete data. Median evaluation duration was 8 weeks (interquartile range 3–22); 453 (27%) were short‐duration evaluations and 414 (25%) were long‐duration evaluations. Multiple patient and evaluation characteristics were associated with duration (Table 1). Table 6 provides the average duration each variable contributes to evaluation by site, highlighting the shortest durations compared with other groups. Conclusions Duration of presurgical evaluation for DRE can be accurately modeled using multiple patient characteristics and testing strategies commonly employed in epilepsy surgery evaluations. This predictive model can not only estimate evaluation duration but also identify opportunities to improve systemic efficiency. Institution‐level modeling identifies specific program strengths, providing an opportunity to learn from successful processes. Subsequent research will focus on institutional process mapping to better understand systemic practices that lead to improved efficiencies, then sharing these processes across the consortium to shorten evaluation durations.

ImportanceCancer imposes a substantial economic burden on patients that may be worse in patients with higher-stage disease due to the need for more therapy.ObjectiveTo investigate the out-of-pocket costs (OOPCs) attributable to the initial treatment of common cancers among privately insured individuals diagnosed at different stages.Design, Setting, and ParticipantsThis retrospective cohort study used administrative claims data of a large national insurer in the US linked to the Surveillance, Epidemiology, and End Results (SEER) cancer registry to compare OOPCs of individuals diagnosed with breast, colorectal, and lung cancer at different stages with OOPCs of similar individuals without cancer using difference-in-differences methods. Individuals living in the US between 2008 and 2022, aged younger than 65 years, insured through a large national private health insurer, and with 6 or more months of continuous enrollment were included. Data were analyzed from June 2024 through February 2025.ExposureThe presence or absence of an incident cancer diagnosis and disease stage from the SEER registry diagnosed from 2008 to 2019.Main Outcomes and MeasuresThe primary outcome was OOPCs among individuals with breast, colorectal, and lung cancer diagnosed at different disease stages compared with those with no cancer diagnosis.ResultsThe cohort consisted of 46 158 patients (mean [SD] age at diagnosis, 46 [12] years; 30 733 female [66.6%]; 2543 Asian [5.5%], 4114 Black [8.9%], 3590 Hispanic [7.8%], and 31 099 White [67.4%]), including 19 656 patients with cancer and 26 502 patients without cancer in the control group. Among patients with cancer, 14 581 patients had breast cancer, 2842 patients had colorectal cancer, and 2233 patients had lung cancer. An incident cancer diagnosis was associated with a mean increase in OOPCs of $592.53 per month (95% CI, $528.01-$627.04 per month) for the 6 months after the diagnosis. Cost monotonically increased with stage at diagnosis (mean OOPC increase range, $462.01 per month [95% CI, $417.92-$506.11 per month] for stage 0 to $719.97 per month [95% CI, $626.11-$813.83 per month] for stage 4).Conclusions and RelevanceIn this study of individuals with private insurance, patients faced high OOPCs after an incident diagnosis of cancer, with patients with more advanced cancer having the highest OOPCs. Further research is needed to determine the clinical and financial effects of increased OOPCs for patients with cancer.

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.

Abstract The advent of high-throughput sequencing has led to an exponential increase in genomic data, highlighting the need for efficient and accurate models to analyze and interpret this information. Here, we introduce a novel, exomic foundational model that leverages a combination of human reference genome and multispecies data to improve variant detection and interpretation. Our model utilizes a short- range transformer architecture and is trained on a large dataset of human exomic sequences derived from the Tapestry study. Through a series of ablation studies and scaling experiments, we demonstrate the effectiveness of our model in pre- dicting next token accuracy and identifying clinically pathogenic variants. We also show that our model outperforms existing models in a range of downstream tasks, including variant effect prediction and disease state identification. In fact, our largest STRAND variant (1B parameters) surpassed previous benchmarks, demonstrating a mean accuarcy of 0.880 (8.2% improvement over the original NT and 7% improvement over NT-v2). Furthermore, we construct a unique exomic ClinVar dataset to evaluate the model’s performance on pathogenicity and disease states. Our results highlight the potential of this model to improve our understanding of the human exome and its role in disease. The model and its applications have significant implications for genomic based diagnosis and personalized medicine including tailored therapeutic development.

Objective: This study evaluated the feasibility and satisfaction of using a wearable brain activity sensing device for stress reduction among patients experiencing Long COVID (LC). Patients and Methods: Patients with LC (N = 45) were invited to participate in an open-label pilot study. Participants were asked to use a brain-sensing electroencephalogram (S-EEG) wearable device (Muse-S™) daily for 90 days and followed for an additional 90 days (180 days total participation). Study enrollment began June 28, 2022, and ended July 28, 2023. Results: Patients were predominantly female (82%), white (96%), and with an average age of 47.6 (±12.5) years. At the end of treatment (90 days), cognition, sleep, self-efficacy, and quality-of-life quality of life improved ( P < .001 to P = .04). In addition, stress and anxiety were significantly reduced ( P < .001). These significant changes persisted by end-of-study (180 days). For stress ( P = .023) and anxiety ( P = .033), a change from the baseline during follow-up was significantly associated with the amount of S-EEG usage. The end-of-study satisfaction survey indicated that patients were very satisfied with the S-EEG device, and 97% planned to continue using it. Conclusion: Results suggest that the S-EEG device improved cognition in LC, which is one of the main severe symptoms we see in our LC clinic. In addition, it helped reduce stress and anxiety. These findings have important clinical implications for LC patients using S-EEG as an adjunct to concurrent LC clinical care which includes pharmacotherapies.