Citations

Abstract The brain avidly consumes glucose to fuel neurophysiology. Cancers of the brain, such as glioblastoma (GBM), relinquish physiological integrity and gain the ability to proliferate and invade healthy tissue. How brain cancers rewire glucose utilization to drive aggressive growth remains elusive. Here, we infused 13C-labeled glucose into patients and mice with brain cancer, coupled with quantitative metabolic flux analysis, to map the fates of glucose-derived carbon in tumor vs. cortex. Through the first direct and comprehensive measurements of carbon and nitrogen labeling in both cortex and glioma tissues, we uncover profound metabolic transformations. In human cortex, glucose carbons fuel essential physiologic processes including TCA cycle oxidation and neurotransmitter synthesis. Conversely, gliomas downregulate these processes and scavenge alternative carbon sources such as amino acids from the environment, repurposing glucose-derived carbons to generate molecules needed for proliferation and invasion. Targeting this metabolic rewiring in mice through dietary amino acid modulation selectively alters GBM metabolism, slows tumor growth, and augments the efficacy of standard-of-care treatments. These findings illuminate how aggressive brain tumors exploit glucose to suppress normal physiological activity in favor of malignant expansion and offer potential therapeutic strategies to enhance treatment outcomes.

Abstract Senile osteoporosis (SOP) is a systemic bone disease characterized by increased susceptibility to fractures. In this study, it is found that senescent bone marrow mesenchymal stem cells (BMSCs) exhibit increased sensitivity to ferroptosis, a phenomenon associated with Steap4, a nicotinamide adenine dinucleotide phosphate hydrogen (NADPH)‐dependent metalloreductase that reduces Fe 3+ to Fe 2+ . Therefore, it is aimed to innovatively elucidate how Steap4 affects ferroptosis in senescent BMSCs. These findings indicate that Steap4 promotes intracellular Fe 2+ accumulation and elevates reactive oxygen species (ROS) levels, collectively driving the upregulation of CCAAT/enhancer binding protein beta (C/EBPβ) expression. Interestingly, a functional C/EBPβ binding site is identified within the Steap4 promoter region. Mechanistically, knockdown studies demonstrated that C/EBPβ depletion attenuated Steap4 expression, whereas C/EBPβ overexpression conversely upregulated Steap4 levels. These regulatory processes establish a self‐amplifying Steap4/Fe 2+ ‐ROS/C/EBPβ positive feedback loop. Notably, a large number of adipocytes are also observed in the bone marrow of aged mice. Knockdown of Steap4 and C/EBPβ suppressed the differentiation of BMSCs into adipocytes. Knockdown of Steap4 or deferoxamine (DFO) treatment in animal experiments effectively relieves SOP. In conclusion, Steap4 accelerates the onset of ferroptosis in senescent BMSCs and promotes their differentiation to adipocytes through the Steap4/Fe 2+ ‐ROS/C/EBPβ axis, ultimately impairing their osteogenic capacity.

Background: Cardiac disease is the leading cause of premature mortality in Friedreich Ataxia (FA). However, validated prognostic indicators or early imaging biomarkers that can reliably predict cardiac events are lacking. To address this gap, we created a multicenter FA cardiac registry to characterize disease progression and identify early echocardiographic (echo) predictors of major adverse cardiac events (MACE). Methods: FA subjects from 6 North American centers with at least two echos performed ≥5 years apart were included. Demographics, FA-specific disease characteristics, and MACE (a composite of death, heart failure hospitalization, aborted sudden cardiac death, and/or life-threatening arrhythmia) were abstracted. Digital echo images were re-read at a centralized core lab. Paired T tests were utilized for comparison of the normally distributed first and second echo parameters, and logistic regression models were utilized to evaluate for predictors of MACE. Results: Of 115 subjects, 52 (45%) were female. Mean age at diagnosis was 10 ± 3 years, median GAA1 repeat length (a marker of genetic severity) was 700 (range 66–1200), and median age at first analyzed echo was 12 years (range 4-36). MACE occurred in 35 (30%) of the cohort. Serial echocardiograms demonstrated progressive worsening of multiple cardiac parameters over time, including markers of ventricular thickness and hypertrophy, chamber size, systolic function, and novel markers of atrial and ventricular strain (Table 1). On univariate analysis, several markers of hypertrophy and systolic function from the earliest analyzed echo predicted MACE. However, after adjusting for age of diagnosis, GAA1 repeat length, and collinearity among variables, only posterior wall thickness in diastole (PWTd) remained an independent predictor of MACE (odds ratio 1.8 per mm increase, 95% CI 1.1–3.3, p = 0.028). Conclusions: This multicenter FA registry identifies early posterior wall thickness as a significant and independent predictor of major adverse cardiac events in FA. Each millimeter increase in PWTd on early echo is associated with nearly double the odds of MACE. This commonly acquired parameter is often overlooked and may serve as a powerful early marker of cardiac risk. Further investigation of PWTd as a potential surrogate endpoint for therapeutic trials in FA is warranted.

Background: Susceptibility to cardiometabolic diseases (CDs) is shaped by genetic, socio-environmental, and lifestyle factors. We evaluated the associations of disease-specific polysocial risk scores (PSSs)— derived from social determinants of health (SDOH) and lifestyle factors—and genetic risk factors with six CDs, across self-identified race/ethnicity (SIRE) groups in the eMERGE IV cohort. Methods: The six CDs included: atrial fibrillation, coronary heart disease, chronic kidney disease, hyperlipidemia, obesity, and type 2 diabetes. Disease-specific PSSs were developed using machine-learning models based on 65 SDOH and lifestyle factors. Participants were grouped based on their SIRE as Asian, Black, Hispanic/Latino, and White. We evaluated PSS distributions across SIRE groups. Genetic risk was assessed through disease-specific polygenic risk scores (PRSs), monogenic etiologies, and family history (FamHx). High PSS and high PRS were defined as the top 5th percentile. Associations were tested using multivariable logistic regression; model performance was assessed using the C-statistic. Results: Among 20178 participants (50±15 years, 68% female, 40% non-White), the most frequently associated PSS components were physical activity, sleep, self-perceived health, and income. PSS distributions varied across SIRE groups, with Whites having the lowest and Black and Hispanic/Latinos the highest scores for most CDs (Figure 1). All PSSs were significantly associated with their respective CDs, independent of confounders (OR per 1 SD increase: 1.19–2.68; Figure 2-A). No significant interactions were observed between PSSs and PRSs or monogenic etiologies. However, significant interactions were noted between PSSs and FamHx for CKD and obesity. PSS and PRS effects on the odds of CDs were independent (Figure 2-B) and additive (Figure 2-C). Incorporating PSSs into models that included clinical and genetic risk factors improved predictive performance of risk prediction models (Figure 3). Conclusion: PSSs, reflecting socio-environmental and lifestyle disadvantage, varied significantly across SIRE groups and were associated with risk of six CDs, independent of and additive to PRS. Given the substantial variation in socio-environmental and lifestyle risks across SIRE groups, integrating these factors into risk prediction models for CDs may help address health disparities and enhance preventive efforts, especially in marginalized populations.

Background: We investigated the impact of genetic risk factors for coronary heart disease (CHD) -polygenic risk score (PRS), familial hypercholesterolemia (FH), and family history (FamHx)- on CHD risk estimates, across the age spectrum, in two diverse cohorts of US adults -eMERGE IV (eIV) and All of Us (AoU). Methods: CHD was defined as myocardial infarction, unstable angina, and coronary revascularization. Self-identified race/ethnicity (SIRE) was used as a population descriptor. We calculated a PRS for CHD (PRS CHD , PGS004698), ascertained FH as the presence of pathogenic/likely pathogenic variants in FH genes, and defined FamHx as early-onset CHD in first-degree relatives. We employed Pooled Cohort Equations (PCE) to estimate the 10y risk of CHD for adults ≥40y and modeled the association of conventional risk factors with CHD in adults <40y. AoU served as the training set, and eIV as the testing set. We analyzed the impact of PRS CHD and FamHx on CHD risk estimates by a) using multivariable logistic regression and Cox proportional hazard models, assessing discrimination and the extent of risk reclassification; and b) net benefit analysis and decision curves to assess the performance of prediction models across actionable thresholds. Results: We analyzed data for 19348 participants from eIV (age 50±15, 68% female, 41% non-White) and 239645 participants from AoU (age 55±17, 61% female, 48% non-White). Genetic risk factors were significantly associated with CHD. PRS CHD performance varied by SIRE groups, while FamHx was consistent. The effects of PRS CHD and FamHx on CHD were independent and additive (Figure 1). In adults ≥40y, incorporating PRS CHD and FamHx into PCE improved discrimination (C-statistic increased from 0.719 to 0.753; P -diff=9.1×10 -3 , Figure 2) and reclassified risk in 19% and 20% of participants at the 7.5% and 10% 10y CHD risk thresholds, respectively. Between the 7.5% and 10% 10y CHD risk thresholds, incorporating PRS CHD and FamHx into the PCE improved the net benefit of the risk prediction models across White, Black, and Hispanic/Latino groups (Figure 3). Conclusion: PRS CHD and FamHx were independently and additively associated with CHD in two large diverse cohorts in the US. Incorporating PRS CHD and FamHx into PCE improved risk discrimination, reclassified risk in a significant portion of participants, and improved net benefit of the PCE across all three major SIRE groups, motivating the addition of these factors to clinical risk calculators.

Abstract With the rapid advancements in large language model technology and the emergence of bioinformatics‐specific language models (BioLMs), there is a growing need for a comprehensive analysis of the current landscape, computational characteristics, and diverse applications. This survey aims to address this need by providing a thorough review of BioLMs, focusing on their evolution, classification, and distinguishing features, alongside a detailed examination of training methodologies, datasets, and evaluation frameworks. We explore the wide‐ranging applications of BioLMs in critical areas such as disease diagnosis, drug discovery, and vaccine development, highlighting their impact and transformative potential in bioinformatics. We identify key challenges and limitations inherent in BioLMs, including data privacy and security concerns, interpretability issues, biases in training data and model outputs, and domain adaptation complexities. Finally, we highlight emerging trends and future directions, offering valuable insights to guide researchers and clinicians toward advancing BioLMs for increasingly sophisticated biological and clinical applications.

Background: Estimating operative time with low accuracy and precision leads to suboptimal scheduling of available operating rooms (ORs) and waste of healthcare resources. We aimed to develop a predictive machine learning (ML) model for operative time using our institutional data to improve the scheduling of neurosurgical ORs, minimize waste, and maximize resource utilization. Methods: We developed a predictive model using our institution’s multicenter registry, which included diverse neurosurgical cases across three tertiary hospitals. We applied multiple ML techniques, including linear regression, support vector regression, deep neural networks, and extreme gradient boosting regression (XGBR). The best model was combined with the first fit decreasing bin-packing algorithm to create a practical OR scheduling system based on prior data. Results: The XGBR model exhibited the best performance, with lower mean absolute errors (MAEs) (root mean squared error: 52.24, MAE: 37.25) and higher R2 (0.76) values than other models. Implementing this model in a simulated OR scheduling scenario using institutional data, we successfully scheduled 30 random procedures with minimal residual time, such that the model ran out of procedures for the week with 86 and 171 min remaining in the cranial and spinal ORs, respectively, demonstrating potential efficiency gains. The cranial procedures were scheduled over 4 days and spinal procedures over 6 days, optimizing available OR time. Conclusion: Integrating ML models into operative time prediction can significantly improve the accuracy and efficiency of surgical schedules. This approach minimizes wasted OR time and enhances resource allocation, potentially improving patient outcomes and satisfaction by reducing waiting times.

ImportanceStudies suggest that everolimus may reduce the risk of rejection, cardiac allograft vasculopathy (CAV), chronic kidney disease (CKD), and cytomegalovirus (CMV) after heart transplant. Everolimus use is controversial because of data demonstrating higher infection deaths when everolimus is introduced de novo after transplant. It is unclear whether everolimus is safe and effective when initiated at 6 months posttransplant in children, a population in which median graft survival is limited to 15 years and randomized clinical trials are lacking.ObjectiveTo evaluate the safety and efficacy of everolimus combined with low-dose tacrolimus to prevent major adverse transplant events (MATEs) in children after heart transplant.Design, Setting, and ParticipantsMulticenter, randomized, open-label, clinical trial enrolling 211 patients who were alive 6 months after pediatric heart transplant at 25 US sites from February 2018 to August 2020. The last date of follow-up was April 17, 2023.InterventionsParticipants were randomized to receive everolimus and low-dose tacrolimus (n = 107) or standard-dose tacrolimus and mycophenolate mofetil (n = 104) for 30 months.Main Outcomes and MeasuresThe primary efficacy end point was the MATE-3 score at 30 months, a validated composite ordinal end point including acute cellular rejection, CAV, and CKD. The primary safety end point was the MATE-6 score, encompassing the MATE-3 score plus antibody-mediated rejection, infection, and posttransplant lymphoproliferative disorder.ResultsAmong 211 children randomized, the mean age was 8.2 (SD, 6.3) years, 97 (46%) underwent transplant for congenital heart disease, and 49 (23%) were treated for rejection before 6 months. At 30 months, the mean MATE-3 score did not differ between the 2 treatment groups (mean difference, −0.32; 95% CI, −0.90 to 0.20; P = .16). The mean MATE-6 score was no higher in the everolimus group than in the mycophenolate group (baseline-adjusted mean difference, −0.40; 95% CI, −1.81 to 0.93), meeting the success criterion for safety (noninferiority margin &amp;lt;3). There were no differences in graft survival, MATE-free survival, or freedom from any individual MATE. Everolimus was associated with greater improvement in estimated glomerular filtration rate at 12 months (mean difference, 10.5 mL/min/1.73 m2; 95% CI, 1.09-19.91 mL/min/1.73 m2) and a lower incidence of CMV infection (hazard ratio, 0.50; 95% CI, 0.26-0.93).Conclusions and RelevanceAmong 6-month pediatric heart transplant survivors, everolimus and low-dose tacrolimus did not differ from tacrolimus and mycophenolate in preventing the composite of cellular rejection, CAV, and CKD at 30 months. However, everolimus and low-dose tacrolimus appear to be safe based on the total burden of 6 MATEs and may be associated with improved kidney function and less CMV infection.Trial RegistrationClinicalTrials.gov Identifier: NCT03386539

Background/importance Spinal cord stimulation (SCS) is an established therapy for chronic pain conditions when conventional treatments fail. However, global research implementation varies significantly due to patient demographics, healthcare infrastructure, and economic factors, creating disparities in access that directly impact patient care and public health outcomes. Objective To evaluate the global landscape of SCS research by analyzing patient demographics, geographic distribution, funding sources, and pain conditions treated across chronic pain populations aged 18 years and older, with primary focus on associations between geographic regions and patient age demographics. Evidence review We conducted a systematic literature review and meta-analysis following Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, searching five major databases through October 2024. Studies from 2000 onward reporting SCS in adults with chronic pain lasting 3 months or longer were included. We included randomized trials, comparative studies, and case series with at least 10 patients, excluding commentaries, reviews, and insurance databases. Findings Analysis of 187 studies revealed heterogeneity in age distributions across geographic regions, with Asian studies showing higher mean ages compared to North America, while European showed lower mean ages. Research concentration was predominantly in high-income regions, with 57% of studies receiving industry funding and significant associations between funding source and geographic origin. Post-surgical pain syndrome-thoracolumbar emerged as the most prevalent condition globally, representing 44.9% of cases. Conclusions Significant associations exist between geographic regions and patient demographics in SCS research, with notable disparities in research concentration highlighting the need for broader investigation in underrepresented areas to optimize global application of this chronic pain management therapy. PROSPERO registration number CRD42025635147.