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简介：AbstractPurpose:Traumatic brain injury (TBI) generally causes mortality and disability, particularly in children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool. The present study aims to assess the predictability of ML for the functional outcomes of pediatric TBI.Methods:A retrospective cohort study was performed targeting children with TBI who were admitted to the trauma center of southern Thailand between January 2009 and July 2020. The patient was excluded if he/she (1) did not undergo a CT scan of the brain, (2) died within the first 24 h, (3) had unavailable complete medical records during admission, or (4) was unable to provide updated outcomes. Clinical and radiologic characteristics were collected such as vital signs, Glasgow coma scale score, and characteristics of intracranial injuries. The functional outcome was assessed using the King's Outcome Scale for Childhood Head Injury, which was thus dichotomized into favourable outcomes and unfavourable outcomes: good recovery and moderate disability were categorized as the former, whereas death, vegetative state, and severe disability were categorized as the latter. The prognostic factors were estimated using traditional binary logistic regression. By data splitting, 70% of data were used for training the ML models and the remaining 30% were used for testing the ML models. The supervised algorithms including support vector machines, neural networks, random forest, logistic regression, naive Bayes and k-nearest neighbor were performed for training of the ML models. Therefore, the ML models were tested for the predictive performances by the testing datasets.Results:There were 828 patients in the cohort. The median age was 72 months (interquartile range 104.7 months, range 2-179 months). Road traffic accident was the most common mechanism of injury, accounting for 68.7%. At hospital discharge, favourable outcomes were achieved in 97.0% of patients, while the mortality rate was 2.2%. Glasgow coma scale score, hypotension, pupillary light reflex, and subarachnoid haemorrhage were associated with TBI outcomes following traditional binary logistic regression; hence, the 4 prognostic factors were used for building ML models and testing performance. The support vector machine model had the best performance for predicting pediatric TBI outcomes: sensitivity 0.95, specificity 0.60, positive predicted value 0.99, negative predictive value 1.0; accuracy 0.94, and area under the receiver operating characteristic curve 0.78.Conclusion:The ML algorithms of the present study have a high sensitivity; therefore they have the potential to be screening tools for predicting functional outcomes and counselling prognosis in general practice of pediatric TBIs.

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简介：摘要：传统上，国内高等教育课程文件是依靠在院校教学大纲完成的，即将踏入临床护生所学内容与临床活动内容往往存在巨大差异。随着护理学科的发展，同时，对护理人员也提出了更高的要求，实习护士如何做好自身准备也成为我们一直探讨的问题。传统背景下，护生该如何找到突破口，提高实习质量，护生面临更大挑战，菲律宾圣托马斯大学理学硕士提出高等教育的质量推动转向OBL学习方法，即护生带着“wish list{1}进入临床活动，根据自身能力制定学习计划，是护生主动将所学的理论知识与实践相结合并巩固加深的重要环节，使护生在学校学习的理论知识应用于临床实践，培养和提高临床思维分析和独立自主解决问题的能力。

简介：摘 要:在中考体育项目之中有800m和1000m跑，其是无氧运动和有氧运动的混合。对于成绩比较差的学生来说可以依靠技术训练提高体育成绩。在此，笔者主要就如何提高中考体育800m和1000m中学生体育成绩谈一谈自己的看法。

简介：AbstractBackground:A deep learning model (DLM) that enables non-invasive hypokalemia screening from an electrocardiogram (ECG) may improve the detection of this life-threatening condition. This study aimed to develop and evaluate the performance of a DLM for the detection of hypokalemia from the ECGs of emergency patients.Methods:We used a total of 9908 ECG data from emergency patients who were admitted at the Second Affiliated Hospital of Nanchang University, Jiangxi, China, from September 2017 to October 2020. The DLM was trained using 12 ECG leads (lead I, II, III, aVR, aVL, aVF, and V1-6) to detect patients with serum potassium concentrations <3.5 mmol/L and was validated using retrospective data from the Jiangling branch of the Second Affiliated Hospital of Nanchang University. The blood draw was completed within 10 min before and after the ECG examination, and there was no new or ongoing infusion during this period.Results:We used 6904 ECGs and 1726 ECGs as development and internal validation data sets, respectively. In addition, 1278 ECGs from the Jiangling branch of the Second Affiliated Hospital of Nanchang University were used as external validation data sets. Using 12 ECG leads (leads I, II, III, aVR, aVL, aVF, and V1-6), the area under the receiver operating characteristic curve (AUC) of the DLM was 0.80 (95% confidence interval [CI]: 0.77-0.82) for the internal validation data set. Using an optimal operating point yielded a sensitivity of 71.4% and a specificity of 77.1%. Using the same 12 ECG leads, the external validation data set resulted in an AUC for the DLM of 0.77 (95% CI: 0.75-0.79). Using an optimal operating point yielded a sensitivity of 70.0% and a specificity of 69.1%.Conclusions:In this study, using 12 ECG leads, a DLM detected hypokalemia in emergency patients with an AUC of 0.77 to 0.80. Artificial intelligence could be used to analyze an ECG to quickly screen for hypokalemia.

简介：AbstractBackground:Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animalorigin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes.Methods:A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models.Results:The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5-25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor.Conclusions:Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic.

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简介：摘要：随着科学技术的发展，世界各地的信息技术已经进入了智能化和信息化的时代，全民信息化正在改变着人们的方方面面，包括人们的生活、工作、学习和教育方式。现代技术对教育的影响和促进是显而易见的。知识的积累不能再单纯依靠记忆。科学技术和手段帮助人们更好更快地获得知识。学校教育不是唯一的选择，教育的模式不是一成不变的，而是多元化教学模式和手段的结合。PLB教学法不仅要让学生成为知识的所有者，还要帮助学生成为知识的更新者和创造者，也就是说，教育必须让人们学会学习，成为具有用新技术积极探索、获取和处理信息、分析和解决问题、与他人合作、终身学习能力的人才。

简介：摘要：近年来，国内外上市公司发生了一系列高管薪酬激励不当的问题。而随着我国市场经济逐步深入发展，上市公司ECI与公司治理问题日益突出，即将成为决定现代企业经营成败的重要问题。本文通过M公司的案例对ECI过度这一问题进行剖析，以期为上市公司制定高管薪酬激励计划带来一定的指导意义。

简介：摘要：老虎台矿-680m703#立井煤仓是老虎台矿东部唯一的运输线，受采动影响及运输煤流长期冲刷，造成井筒下部井壁全部脱落，脱落的井壁经常堵塞煤仓下口，影响生产。

简介：摘要：在当前的发展阶段下，信息技术正在不断向教育领域延伸，相关技术的应用在很大程度上改变了传统的教学模式以及教学思路，在这个过程中高等院校也需要重视信息技术在教学改革中的应用，结合教学改革的要求，以及人才培养的目标将E-learning引入到教学中，从而提升人才培养的质量，并促进高等院校教学改革的设施。基于以上认识，本文从E-learning课程实施方式出发，探讨如何通过加强学生计算机基础、学习支撑环境建设来充分发挥E-learning在高等院校教学改革中的作用，提升高等院校教学的信息化水平。

简介：摘要m6A修饰是真核生物中最常见、最丰富的修饰形式之一，在不改变碱基序列的情况下对基因的转录后表达水平发挥调控作用。该修饰过程是动态可逆的，由甲基转移酶、去甲基化酶和相应的读取蛋白协同调控，参与m6A修饰的酶出现异常会导致肿瘤等一系列疾病的发生。本文综述了m6A修饰相关蛋白的组成与功能，以及m6A修饰在肿瘤增殖、侵袭与转移、血管生成、炎症反应、免疫反应、基因组不稳定、细胞代谢中的功能和调节机制。

简介：摘要N6-甲基腺嘌呤(m6A)甲基化修饰的生物学作用已被逐渐深入研究，在肿瘤中显示出越来越高的价值。近年来，随着对表观遗传学在RNA修饰方面的深入研究，诸多研究表明m6A甲基化修饰在肺癌的发生与发展中发挥了重要作用。m6A相关修饰蛋白具有成为肺癌临床诊治靶标的潜在应用价值。

简介：摘要：800M跑是径赛的项目之一，是以最小时间完成比赛距离的耐力速度比赛项目。是全国很多省市的初中女子中考体育项目，因此对初中女子800M跑的训练很关键。800M的训练必须具备心理训练、跑的动作技术训练、速度耐力训练等，现就如何发展初中女子800M训练谈几点看法：