基于UPLC-Q-TOF-MS指纹图谱和分子对接技术筛选藿香正气水抗新冠病毒潜在质量标志物

目的 基于超高效液相色谱-四极杆-飞行时间串联质谱(UPLC-Q-TOF-MS)指纹图谱和分子对接技术,确定藿香正气水(Huoxiang Zhengqi Shui,HZS)抗新型冠状病毒(SARS-CoV-2)的潜在质量标志物(quality markers,Q-Marker)。方法 对27批HZS样品建立UPLC-Q-TOF-MS指纹图谱,结合化学计量学方法,筛选出HZS的差异性成分;以瑞德西韦为阳性对照,将HZS的差异性成分与SARS-CoV-2主蛋白酶(main protease,Mpro)进行分子对接,进一步确定HZS的潜在Q-Marker。结果 通过建立27批HZS样品的UPLC-Q-TOF-MS指纹图谱,标定了27种共有化合物;结合层次聚类分析(hierarchical clustering analysis,HCA)和主成分分析(principal component analysis,PCA),确定了其中14种共有化合物在27批HZS样品中具有较大的差异性,并鉴定出了橙皮苷、氧化前胡素、新比克白芷内脂、甜橙素、甘草酸、3,5,6,7,8,3'',4''-七甲氧基黄酮、桔皮素、欧前胡素、珊瑚菜素9种差异性化合物;9种差异性化合物的分子对接结果显示,橙皮苷、氧化前胡素、新比克白芷内脂、甘草酸、欧前胡素、珊瑚菜素6种化合物能与SARS-CoV-2 Mpro的活性氨基酸结合,具有抑制SARS-CoV-2 Mpro的潜能,可作为HZS的潜在Q-Marker。结论 将UPLC-Q-TOF-MS指纹图谱、化学计量学分析和分子对接技术交叉使用,确定了HZS的潜在Q-Marker,该方法为药物成分鉴定、同一类药物成分差异性分析,及其功效研究方面提供一定参考。     

基于人工智能的药物-靶标相互作用预测

目的 建立一个高效的药物-靶标相互作用预测分类模型，为生物实验提供有力的补充工具。方法 研究开发一种基于深度学习的方法来预测药物-靶标相互作用：通过引入高维分子指纹和蛋白质描述符，并应用概率矩阵分解算法生成负样本集，构建一个高效的药物-靶标相互作用预测分类模型。结果 与其他已报道的方法相比，本方法具有可比性或优越性，预测准确性、特异性、敏感性以及AUC值均>90%，提示该方法在药物靶标预测方面具有良好的应用前景。结论 人工智能深度学习模型以及概率矩阵分解算法的结合有助于解决药物-靶标相互作用预测精度低、负样本选择不合理等问题。     

Immunoinformatics: an overview of computational tools and techniques for understanding immune function

In recent years, there has been a rapid expansion in the application of information technology to biological data. Although the use of information science techniques is less common for the discipline of immunology, this field has seen great strides in recent years. This review addresses why in silico modeling is needed in immunology research, highlights some of the major areas of research and suggests what may be important for the future of immunoinformatics.     

Longitudinal Prediction of Suicide Attempts for a Diverse Adolescent Sample of Native Hawaiians,Pacific Peoples,and Asian Americans

The objective of this study was to determine the longitudinal predictors of past-6-month suicide attempts for a diverse adolescent sample of Native Hawaiians, Pacific peoples, and Asian Americans. The study used longitudinal data from the Hawaiian High Schools Health Survey (N?=?2,083, 9th to 11th graders, 1992–1993 and 1993–1994 school years). A stepwise multiple logistic regression was conducted. The final model consisted of three statistically significant predictors: (1) Time 1 suicide attempt, odds ratio?=?30.6; (2) state anxiety, odds ratio?=?4.9; and (3) parent expectations, odds ratio?=?1.9. Past suicide attempt was by far the strongest predictor of future suicide attempts. Implications are discussed, including the need for screening of prior suicide attempts and focused interventions after suicide attempts.     

Development of a clinical prediction model for an international normalised ratio ≥ 4·5 in hospitalised patients using vitamin K antagonists

Vitamin K antagonists (VKAs) used for the prevention and treatment of thromboembolic disease, increase the risk of bleeding complications. We developed and validated a model to predict the risk of an international normalised ratio (INR) ≥ 4·5 during a hospital stay. Adult patients admitted to a tertiary hospital and treated with VKAs between 2006 and 2010 were analysed. Bleeding risk was operationalised as an INR value ≥4·5. Multivariable logistic regression analysis was used to assess the association between potential predictors and an INR ≥ 4·5 and validated in an independent cohort of patients from the same hospital between 2011 and 2014. We identified 8996 admissions of patients treated with VKAs, of which 1507 (17%) involved an INR ≥ 4·5. The final model included the following predictors: gender, age, concomitant medication and several biochemical parameters. Temporal validation showed a c statistic of 0·71. We developed and validated a clinical prediction model for an INR ≥ 4·5 in VKA‐treated patients admitted to our hospital. The model includes factors that are collected during routine care and are extractable from electronic patient records, enabling easy use of this model to predict an increased bleeding risk in clinical practice.     

Development and Validation of Equations to Link Pediatric Evaluation of Disability Inventory (PEDI) Functional Skills Scores to PEDI-Computer Adaptive Test Scores for Youth with Cerebral Palsy

Abstract

Aim: The Pediatric Evaluation of Disability Inventory (PEDI) was revised to the PEDI-Computer Adaptive Test (PEDI-CAT). The PEDI has been used for over two decades to track function in youth, so it is important that follow-up data are not lost with this transition. The purpose of this study was to develop and validate equations for linking PEDI scores to PEDI-CAT scores.

Methods: Caregivers of 101 youth 6.1 to 19.8?years of age with cerebral palsy (CP) and classified at Gross Motor Classification System (GMFCS) Levels I-V completed the PEDI and PEDI-CAT. Scaled score data from this sample were used to develop and validate linking equations using least squares regression and bootstrap cross-validation techniques. Next, equations were tested in an independent sample of 35 children with developmental disabilities.

Results: The equations to predict PEDI-CAT scores exhibited excellent model fit. PEDI Self-care, Mobility, and Social Function explained 92%, 84%, and 85% of the variation in PEDI-CAT Daily Activities, Mobility, and Social/Cognitive domains, respectively. No differences were detected between actual and predicted PEDI-CAT scores across all domains and by GMFCS level for the equation development sample and for an equation validation independent sample.

Conclusions: The model fit was excellent; however, equations should be used cautiously when evaluating changes in function for individual children with ceiling level PEDI scores. Valid score prediction equations for youth with CP will assist with transitioning from the PEDI to the PEDI-CAT.