Extracellular mitochondria released from traumatized brains induced platelet procoagulant activity

Coagulopathy often develops soon after acute traumatic brain injury and its cause remains poorly understood. We have shown that injured brains release cellular microvesicles that disrupt the endothelial barrier and induce consumptive coagulopathy. Morphologically intact extracellular mitochondria accounted for 55.2% of these microvesicles, leading to the hypothesis that these extracellular mitochondria are metabolically active and serve as a source of oxidative stress that activates platelets and renders them procoagulant. In testing this hypothesis experimentally, we found that the extracellular mitochondria purified from brain trauma mice and those released from brains subjected to freeze-thaw injury remained metabolically active and produced reactive oxygen species. These extracellular mitochondria bound platelets through the phospholipid-CD36 interaction and induced α-granule secretion, microvesiculation, and procoagulant activity in an oxidant-dependent manner, but failed to induce aggregation. These results define an extracellular mitochondria-induced and redox-dependent intermediate phenotype of platelets that contribute to the pathogenesis of traumatic brain injury-induced coagulopathy and inflammation

Piperlongumine Blocks JAK2-STAT3 to Inhibit Collagen-Induced Platelet Reactivity Independent of Reactive Oxygen Species†

Piperlongumine (PL) is a compound isolated from the piper longum plant. It possesses anti-cancer activities through blocking the transcription factor STAT3 and by inducing reactive oxygen species (ROS) in cancer, but not normal cells. It also inhibits platelet aggregation induced by collagen, but the underlying mechanism is not known.We conducted in vitro experiments to test the hypothesis that PL regulates a non-transcriptional activity of STAT3 to specifically reduce the reactivity of human platelets to collagen.PL dose-dependently blocked collagen-induced platelet aggregation, calcium influx, CD62p expression and thrombus formation on collagen with a maximal inhibition at 100 μM. It reduced platelet microvesiculation induced by collagen. PL blocked the activation of JAK2 and STAT3 in collagen-stimulated platelets. This inhibitory effect was significantly reduced in platelets pretreated with a STAT3 inhibitor. Although PL induced ROS production in platelets; quenching ROS using excessive reducing agents: 20 μM GSH and 0.5 mM L-Cysteine, did not block the inhibitory effects. The NADPH oxidase inhibitor Apocynin also had no effect.PL inhibited collagen-induced platelet reactivity by targeting the JAK2-STAT3 pathway. We also provide experimental evidence that PL and collagen induce different oxidants that have differential effects on platelets. Studying these differential effects may uncover new mechanisms of regulating platelet functions by oxidants in redox signals

A fuzzy neural network with fuzzy impact grades

Fuzzy rule derivation is often difficult and time-consuming,and requires expert knowledge. Thiscreates a common bottleneck in fuzzy system design. In order to solve this problem, many fuzzy systems that automatically generate fuzzy rules from numerical data have been proposed. In this paper, we propose a fuzzy neural network based on mutual subsethood(MSBFNN)and its fuzzy rule identification algorithms. In our approach, fuzzy rules are described by different fuzzy sets. For each fuzzy set representing a fuzzy rule, the universe of discourse is defined as the summation of weighted membership grades of input linguistic terms that associate with the given fuzzy rule. In this manner, MSBFNN fully considers the contribution of input variables to the joint firing strength of fuzzy rules. Afterwards, the proposed fuzzy neural network quantifies the impacts of fuzzy rules on the consequent parts by fuzzy connections based on mutual subsethood. Furthermore, to enhance the knowledge representation and interpretation of the rules, a linear transformation from consequent parts to output is incorporated into MSBFNN so that higher accuracy can be achieved. In the parameter identification phase, the back propagation algorithm is employed, and proper linear transformation is also determined dynamically. To demonstrate the capability of the MSBFNN, simulations in different areas including classification, regression and time series prediction are conducted. The proposed MSBFNN shows encouraging performance when benchmarked against other models

Timespan-aware dynamic knowledge graph embedding by incorporating temporal evolution

Recently, Knowledge Graph Embedding (KGE) has attracted considerable research efforts, since it simplifies the manipulation while preserving the inherent structure of the KG. However to some extent, most existing KGE approaches ignore the historical changes of structural information involved in dynamic knowledge graphs (DKGs). To deal with this problem, this paper presents a Timespan-aware Dynamic knowledge Graph Embedding Evolution (TDG2E) method that considers temporal evolving process of DKGs. The major innovations of our paper are two-fold. Firstly, a Gated Recurrent Units (GRU) based model is utilized in TDG2E to deal with the dependency among sub-KGs that is inevitably involved in the learning process of the dynamic knowledge graph embedding. Furthermore, we incorporate an auxiliary loss to supervise the learning process of the next sub-KG by utilizing previous structural information (i.e., the hidden state of GRU). In contrast with existing approaches in the literature (e.g., HyTE and t-TransE), TDG2E preserves structural information of current sub-KG and the temporal evolving process of the DKG simultaneously. Secondly, to further deal with the time unbalance issue underlying the DKGs, a Timespan Gate is designed in GRU. It makes TDG2E possible to model the temporal evolving process of DKGs more effectively by incorporating the timespan between adjacent sub-KGs. Extensive experiments on two large temporal datasets (i.e., YAGO11k and Wikidata12k) extracted from real-world KGs validate that the proposed TDG2E significantly outperforms traditional KGE methods in terms of Mean Rank and Hit Rate.Published versio

A deep learning‐based interpretable decision tool for predicting high risk of chemotherapy‐induced nausea and vomiting in cancer patients prescribed highly emetogenic chemotherapy

Abstract Objective This study aims to develop a risk prediction model for chemotherapy‐induced nausea and vomiting (CINV) in cancer patients receiving highly emetogenic chemotherapy (HEC) and identify the variables that have the most significant impact on prediction. Methods Data from Tianjin Medical University General Hospital were collected and subjected to stepwise data preprocessing. Deep learning algorithms, including deep forest, and typical machine learning algorithms such as support vector machine (SVM), categorical boosting (CatBoost), random forest, decision tree, and neural network were used to develop the prediction model. After training the model and conducting hyperparameter optimization (HPO) through cross‐validation in the training set, the performance was evaluated using the test set. Shapley additive explanations (SHAP), partial dependence plot (PDP), and Local Interpretable Model‐Agnostic Explanations (LIME) techniques were employed to explain the optimal model. Model performance was assessed using AUC, F1 score, accuracy, specificity, sensitivity, and Brier score. Results The deep forest model exhibited good discrimination, outperforming typical machine learning models, with an AUC of 0.850 (95%CI, 0.780–0.919), an F1 score of 0.757, an accuracy of 0.852, a specificity of 0.863, a sensitivity of 0.784, and a Brier score of 0.082. The top five important features in the model were creatinine clearance (Ccr), age, gender, anticipatory nausea and vomiting, and antiemetic regimen. Among these, Ccr had the most significant predictive value. The risk of CINV decreased with increased Ccr and age, while it was higher in the presence of anticipatory nausea and vomiting, female gender, and non‐standard antiemetic regimen. Conclusion The deep forest model demonstrated good discrimination in predicting the risk of CINV in cancer patients prescribed HEC. Kidney function, as represented by Ccr, played a crucial role in the model's prediction. The clinical application of this predictive tool can help assess individual risks and improve patient care by proactively optimizing the use of antiemetics in cancer patients receiving HEC