Dependence of Hippocampal Function on ERRγ-Regulated Mitochondrial Metabolism

SummaryNeurons utilize mitochondrial oxidative phosphorylation (OxPhos) to generate energy essential for survival, function, and behavioral output. Unlike most cells that burn both fat and sugar, neurons only burn sugar. Despite its importance, how neurons meet the increased energy demands of complex behaviors such as learning and memory is poorly understood. Here we show that the estrogen-related receptor gamma (ERRγ) orchestrates the expression of a distinct neural gene network promoting mitochondrial oxidative metabolism that reflects the extraordinary neuronal dependence on glucose. ERRγ−/− neurons exhibit decreased metabolic capacity. Impairment of long-term potentiation (LTP) in ERRγ−/− hippocampal slices can be fully rescued by the mitochondrial OxPhos substrate pyruvate, functionally linking the ERRγ knockout metabolic phenotype and memory formation. Consistent with this notion, mice lacking neuronal ERRγ in cerebral cortex and hippocampus exhibit defects in spatial learning and memory. These findings implicate neuronal ERRγ in the metabolic adaptations required for memory formation

Educational Disparities in Rates of Smoking Among Diabetic Adults: The Translating Research Into Action for Diabetes Study

Objectives. We assessed educational disparities in smoking rates among adults with diabetes in managed care settings. Methods. We used a cross-sectional, survey-based (2002–2003) observational study among 6538 diabetic patients older than 25 years across multiple managed care health plans and states. For smoking at each level of self-reported educational attainment, predicted probabilities were estimated by means of hierarchical logistic regression models with random intercepts for health plan, adjusted for potential confounders. Results. Overall, 15% the participants reported current smoking. An educational gradient in smoking was observed that varied significantly (P<.003) across age groups, with the educational gradient being strong in those aged 25 to 44 years, modest in those aged 45 to 64 years, and nonexistent in those aged 65 years or older. Of particular note, the prevalence of smoking observed in adults aged 25–44 years with less than a high school education was 50% (95% confidence interval: 36% to 63%). Conclusions. Approximately half of poorly educated young adults with diabetes smoke, magnifying the health risk associated with early-onset diabetes. Targeted public health interventions for smoking prevention and cessation among young, poorly educated people with diabetes are needed

Educational disparities in health behaviors among patients with diabetes: the Translating Research Into Action for Diabetes (TRIAD) Study

<p>Abstract</p> <p>Background</p> <p>Our understanding of social disparities in diabetes-related health behaviors is incomplete. The purpose of this study was to determine if having less education is associated with poorer diabetes-related health behaviors.</p> <p>Methods</p> <p>This observational study was based on a cohort of 8,763 survey respondents drawn from ~180,000 patients with diabetes receiving care from 68 provider groups in ten managed care health plans across the United States. Self-reported survey data included individual educational attainment ("education") and five diabetes self-care behaviors among individuals for whom the behavior would clearly be indicated: foot exams (among those with symptoms of peripheral neuropathy or a history of foot ulcers); self-monitoring of blood glucose (SMBG; among insulin users only); smoking; exercise; and certain diabetes-related health seeking behaviors (use of diabetes health education, website, or support group in last 12 months). Predicted probabilities were modeled at each level of self-reported educational attainment using hierarchical logistic regression models with random effects for clustering within health plans.</p> <p>Results</p> <p>Patients with less education had significantly lower predicted probabilities of being a non-smoker and engaging in regular exercise and health-seeking behaviors, while SMBG and foot self-examination did not vary by education. Extensive adjustment for patient factors revealed no discernable confounding effect on the estimates or their significance, and most education-behavior relationships were similar across sex, race and other patient characteristics. The relationship between education and smoking varied significantly across age, with a strong inverse relationship in those aged 25–44, modest for those ages 45–64, but non-evident for those over 65. Intensity of disease management by the health plan and provider communication did not alter the examined education-behavior relationships. Other measures of socioeconomic position yielded similar findings.</p> <p>Conclusion</p> <p>The relationship between educational attainment and health behaviors was modest in strength for most behaviors. Over the life course, the cumulative effect of reduced practice of multiple self-care behaviors among less educated patients may play an important part in shaping the social health gradient.</p

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Educational disparities in health behaviors among patients with diabetes: the Translating Research Into Action for Diabetes (TRIAD) Study.

BackgroundOur understanding of social disparities in diabetes-related health behaviors is incomplete. The purpose of this study was to determine if having less education is associated with poorer diabetes-related health behaviors.MethodsThis observational study was based on a cohort of 8,763 survey respondents drawn from ~180,000 patients with diabetes receiving care from 68 provider groups in ten managed care health plans across the United States. Self-reported survey data included individual educational attainment ("education") and five diabetes self-care behaviors among individuals for whom the behavior would clearly be indicated: foot exams (among those with symptoms of peripheral neuropathy or a history of foot ulcers); self-monitoring of blood glucose (SMBG; among insulin users only); smoking; exercise; and certain diabetes-related health seeking behaviors (use of diabetes health education, website, or support group in last 12 months). Predicted probabilities were modeled at each level of self-reported educational attainment using hierarchical logistic regression models with random effects for clustering within health plans.ResultsPatients with less education had significantly lower predicted probabilities of being a non-smoker and engaging in regular exercise and health-seeking behaviors, while SMBG and foot self-examination did not vary by education. Extensive adjustment for patient factors revealed no discernable confounding effect on the estimates or their significance, and most education-behavior relationships were similar across sex, race and other patient characteristics. The relationship between education and smoking varied significantly across age, with a strong inverse relationship in those aged 25-44, modest for those ages 45-64, but non-evident for those over 65. Intensity of disease management by the health plan and provider communication did not alter the examined education-behavior relationships. Other measures of socioeconomic position yielded similar findings.ConclusionThe relationship between educational attainment and health behaviors was modest in strength for most behaviors. Over the life course, the cumulative effect of reduced practice of multiple self-care behaviors among less educated patients may play an important part in shaping the social health gradient

Gli as a Novel Therapeutic Target in Malignant Pleural Mesothelioma

<div><p>Malignant pleural mesothelioma (MPM) is a highly aggressive tumor with poor prognosis. Current treatment is rarely curative, thus novel meaningful therapies are urgently needed. Inhibition of Hedgehog (Hh) signaling at the cell membrane level in several cancers has shown anti-cancer activity in recent clinical studies. Evidence of Hh-independent Gli activation suggests Gli as a more potent therapeutic target. The current study is aimed to evaluate the potential of Gli as a therapeutic target to treat MPM. The expression profiles of Gli factors and other Hh signaling components were characterized in 46 MPM patient tissue samples by RT-PCR and immunohistochemistry. Cultured cell lines were employed to investigate the requirement of Gli activation in tumor cell growth by inhibiting Gli through siRNA or a novel small molecule Gli inhibitor (Gli-I). A xenograft model was used to evaluate Gli-I <i>in vivo</i>. In addition, a side by side comparison between Gli and Smoothened (Smo) inhibition was conducted <i>in vitro</i> using siRNA and small molecule inhibitors. Our study reported aberrant Gli1 and Gli2 activation in a large majority of tissues. Inhibition of Gli by siRNAs or Gli-I suppressed cell growth dramatically both <i>in vitro</i> and <i>in vivo</i>. Inhibition of Gli exhibited better cytotoxicity than that of Smo by siRNA and small molecule inhibitors vismodegib and cyclopamine. Combination of Gli-I and pemetrexed, as well as Gli-I and vismodegib demonstrated synergistic effects in suppression of MPM proliferation <i>in vitro</i>. In summary, Gli activation plays a critical role in MPM. Inhibition of Gli function holds strong potential to become a novel, clinically effective approach to treat MPM.</p> </div