Severe/critical COVID-19 early warning system based on machine learning algorithms using novel imaging scores

BACKGROUND Early identification of severe/critical coronavirus disease 2019(COVID-19)is crucial for timely treatment and intervention.Chest computed tomography(CT)score has been shown to be a significant factor in the diagnosis and treatment of pneumonia,however,there is currently a lack of effective early warning systems for severe/critical COVID-19 based on dynamic CT evolution.AIM To develop a severe/critical COVID-19 prediction model using a combination of imaging scores,clinical features,and biomarker levels.METHODS This study used an improved scoring system to extract and describe the chest CT characteristics of COVID-19 patients.The study also took into consideration the general clinical indicators such as dyspnea,oxygen saturation,alternative lengthening of telomeres(ALT),and androgen suppression treatment(AST),which are commonly associated with severe/critical COVID-19 cases.The study employed lasso regression to evaluate and rank the significance of different disease characteristics.RESULTS The results showed that blood oxygen saturation,ALT,IL-6/IL-10,combined score,ground glass opacity score,age,crazy paving mode score,qsofa,AST,and overall lung involvement score were key factors in predicting severe/critical COVID-19 cases.The study established a COVID-19 severe/critical early warning system using various machine learning algorithms,including XGBClassifier,Logistic Regression,MLPClassifier,RandomForestClassifier,and AdaBoost Classifier.The study concluded that the prediction model based on the improved CT score and machine learning algorithms is a feasible method for early detection of severe/critical COVID-19 evolution.CONCLUSION The findings of this study suggest that a prediction model based on improved CT scores and machine learning algorithms is effective in detecting the early warning signals of severe/critical COVID-19.

Cr、Ti掺杂Cu/石墨烯界面结合性能的第一性原理预测（英文）

基于密度泛函理论,对过渡金属Cr、Ti掺杂的Cu/石墨烯界面结合性能进行第一性原理预测,构建并对比一系列不同Cu/石墨烯界面的三明治和表面模型。计算结果表明,界面位相关系对界面结合强度影响不大。两种界面模型的计算结果均显示top-fcc配位模型是最稳定的界面结构,并具有较低的界面结合能。Cr掺杂倾向于偏析到界面上取代Cu,而Ti惨杂倾向于占据界面处的填隙位。虽然这两种元素的偏析趋势都较弱,其偏析可以显著提高界面的结合性能,从而强化界面。

密排六方钛中基面堆垛层错导致的孪晶界滑移、孪生台阶及孪晶生长:第一性原理研究

基于第一性原理计算研究密排六方结构钛中{101n}共格孪晶界和滑移孪晶界的结构和能量,探讨滑移孪晶界的形成机理及其与孪晶生长的关系。结果表明,共格孪晶界与基面堆垛层错的相互作用可使共格孪晶界产生滑移,从而形成对应的滑移孪晶界。这种滑移最终能在孪晶界处形成一对单层孪生台阶,并恢复共格孪晶界的结构。孪生台阶的塞积可导致高分辨率透射电镜观察到的孪晶界上的台阶宽化,进一步促进孪晶的生长。此外,还评估多种合金化元素对孪晶界滑移的钉扎效应,为钛合金的强化设计提供指导。