Application of U-Net Network and Training Strategy to Optimal Mesh Refinement in Computational Electromagnetism

International audienc

Reduction of p11 in dorsal raphe nucleus serotonergic neurons mediates depression-like behaviors

Abstract The pathology of depression is related to the imbalance of various neurotransmitters. The dorsal raphe nucleus (DRN), the main brain region producing 5-HT, is crucially involved in the pathophysiology of depression. It contains several neuron types, in which GABAergic neurons are activated by stimuli associated with negative experiences and 5-HT neurons are activated by reward signals. However, little is known about its underlying molecular mechanisms. Here, we found that p11, a multifunctional protein associated with depression, was down-regulated by chronic social defeat stress in 5-HTDRN neurons. Knockdown of p11 in DRN induced depression-like behaviors, while its overexpression in 5-HTDRN neurons alleviated depression-like behavior caused by chronic social defeat stress. Further, p11 regulates membrane trafficking of glutamate receptors in 5-HTDRN neurons, suggesting a possible molecular mechanism underlying the participation of p11 in the pathological process of depression. This may facilitate the understanding of the molecular and cellular basis of depression

Data_Sheet_1_Machine learning-based prediction of the post-thrombotic syndrome: Model development and validation study.docx

BackgroundPrevention is highly involved in reducing the incidence of post-thrombotic syndrome (PTS). We aimed to develop accurate models with machine learning (ML) algorithms to predict whether PTS would occur within 24 months.Materials and methodsThe clinical data used for model building were obtained from the Acute Venous Thrombosis: Thrombus Removal with Adjunctive Catheter-Directed Thrombolysis study and the external validation cohort was acquired from the Sun Yat-sen Memorial Hospital in China. The main outcome was defined as the occurrence of PTS events (Villalta score ≥5). Twenty-three clinical variables were included, and four ML algorithms were applied to build the models. For discrimination and calibration, F scores were used to evaluate the prediction ability of the models. The external validation cohort was divided into ten groups based on the risk estimate deciles to identify the hazard threshold.ResultsIn total, 555 patients with deep vein thrombosis (DVT) were included to build models using ML algorithms, and the models were further validated in a Chinese cohort comprising 117 patients. When predicting PTS within 2 years after acute DVT, logistic regression based on gradient descent and L1 regularization got the highest area under the curve (AUC) of 0.83 (95% CI:0.76–0.89) in external validation. When considering model performance in both the derivation and external validation cohorts, the eXtreme gradient boosting and gradient boosting decision tree models had similar results and presented better stability and generalization. The external validation cohort was divided into low, intermediate, and high-risk groups with the prediction probability of 0.3 and 0.4 as critical points.ConclusionMachine learning models built for PTS had accurate prediction ability and stable generalization, which can further facilitate clinical decision-making, with potentially important implications for selecting patients who will benefit from endovascular surgery.</p