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Homonuclear/Heteronuclear Bimetallic Conjugated Coordination Polymers with Customized Oxygen Evolution Pathway
PM2.5 exposure modifies the association of physical activity with depressive symptoms and glaucoma in middle aged and elderly Chinese
Sulfenylcarbene-Mediated Carbon Atom Insertion for the Late-Stage Functionalization of <i>N</i>-Heterocycles
Role of lactate dehydrogenase A in the regulation of podocyte metabolism and glucose uptake under hyperglycemic conditions
Abstract Lactate is a cellular product of glycolytic metabolism, serving as both an additional oxidative energy substrate and a signaling molecule in metabolic regulation. Plasma lactate levels are elevated in diabetes, and chronic extracellular lactic acidosis is recognized as a negative prognostic marker for the disease. The development of diabetic kidney disease is closely associated with podocyte injury, which forms a crucial layer of the glomerular filtration barrier. Given that high extracellular glucose concentrations also induce lactate production and excretion in podocytes, we hypothesize that an appropriate LDH expression pattern is crucial for maintaining proper podocyte metabolism and function. Our research shows that hyperglycemia significantly decreases lactate dehydrogenase activity in podocytes. Specifically, reduced LDHA expression under hyperglycemic conditions contributes to metabolic disturbances in these cells. Lower LDH activity results in decreased glycolytic activity, altered expression of monocarboxylate transporters, reduced insulin-dependent glucose uptake, and a decrease in the number of podocyte foot processes. These findings underscore the essential role of LDHA in the metabolic adaptation of podocytes to elevated glucose levels typical of diabetes. By elucidating the molecular mechanisms underlying podocyte injury, our study provides new insights into potential therapeutic targets for preventing or mitigating diabetic kidney disease.
Magnetite Nanodiscs Activate Mechanotransductive Calcium Signaling in Diverse Cell Types
Effect of maternal gestational diabetes mellitus on neurodevelopment in late preterm infants at the corrected age of 12 months
Versatile Solid-State Medical Superglue Precursors of α-Lipoic Acid
Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis
Abstract Despite the strides made in medical science, pancreatic cancer continues to be a threat, highlighting the urgent need for creative strategies to address this concern. Recently, a potential approach that has attracted significant attention is using machine learning in clinical decision-making. This research aims to analyze six machine learning algorithms, and an ensemble voting classifier, develop hybrid models for the early detection of pancreatic cancer based on several clinical characteristics and interpret their performance with Shapley Additive Explanations (SHAP). A publicly available dataset composed of 590 patient urine samples was utilized to develop six conventional models for the classification of cancerous from non-cancerous pancreatic cases through the analysis of specific attributes. An ensemble voting classifier was developed from the best-performed single models, which were later hybridized to form six novel hybrid models. The ensemble voting classifier outperformed all stand-alone models with an accuracy of 96.61% and a precision of 98.72%. The six novel hybrid models exhibited higher performance than single models with voting classifier random forest hybridized model outperforming others with an AUC of 99.05% (95% confidence interval (CI): 0.93-1.00) and an interpretation was given by SHAP showing top influential features in pancreatic cancer diagnosis that exhibited the greatest positive SHAP values. Employing rapid sophisticated models with high accuracy and precision holds significant promise in facilitating the effective detection of various diseases, including pancreatic cancer.