白内障术后视功能预测的研究进展

对白内障术后视功能的预测主要涉及以视网膜为起始系统进行视网膜视力的测量,视网膜视力的预测方法有很多种,本文对各种视网膜视力预测方法进行分类并阐述了其工作原理及使用方法,比较了各种方法的应用价值及优缺点.     

影像组学及其在肝癌中的进展

肝癌(hepatocellular carcinoma,HCC)是全球癌症相关死亡的第二大原因,因此早期发现和治疗反应的预测对HCC患者有很大益处。 目前,穿刺活检和常规医学成像在HCC患者的管理中发挥重要且基础的作用,而这两种方法分别存在样本误差和操作者依赖性的不足。 影像组学是一种新兴的非侵入性技术,可以突破时空限制来获取肿瘤的综合信息,用以反映肿瘤的情况,从而弥补上述方法的不足。影像组学的基本步骤包括图像的获取,感兴趣区的划分与重建,特征的提取、划分与分类,模型的建立与效能评价。影像组学在HCC的诊断、治疗和评价方面取得了一定的进展,具有应用前景。     

基于文献的中医经方靶点预测模型研究

由于经方由药材构成,药材又由成分组成,成分和靶点在靶点数据库中可以建立直接联系.因此先提取经方配方和经方成分;然后进行靶点提取,借助靶点数据库构建靶点筛选模型;最后将不同来源的靶点汇总,计算靶点置信度,绘制经方靶点预测.根据经方到靶点的不同路径建立从经方到靶点之间的联系,构建经方靶点预测模型,并以芍药甘草汤和大黄黄连泄...     

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。     

Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree

Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can reduce the overall mortality rate and cost of sepsis treatment. Some studies have predicted mortality and development of sepsis using machine learning models. However, there is a gap between the creation of different machine learning algorithms and their implementation in clinical practice.This study utilized data from the Medical Information Mart for Intensive Care III. We established and compared the gradient boosting decision tree (GBDT), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM).A total of 3937 sepsis patients were included, with 34.3% mortality in the Medical Information Mart for Intensive Care III group. In our comparison of 5 machine learning models (GBDT, LR, KNN, RF, and SVM), the GBDT model showed the best performance with the highest area under the receiver operating characteristic curve (0.992), recall (94.8%), accuracy (95.4%), and F1 score (0.933). The RF, SVM, and KNN models showed better performance (area under the receiver operating characteristic curve: 0.980, 0.898, and 0.877, respectively) than the LR (0.876).The GBDT model showed better performance than other machine learning models (LR, KNN, RF, and SVM) in predicting the mortality of patients with sepsis in the intensive care unit. This could be used to develop a clinical decision support system in the future.     

The role of metabolomic markers for patients with infectious diseases: implications for risk stratification and therapeutic modulation

Introduction: Metabolomics is a rapidly growing area of research. Metabolomic markers can provide information about the interaction of different organ systems, and thereby improve the understanding of physio-pathological processes, disease risk, prognosis and therapy responsiveness in a variety of diseases.

Areas covered: In this narrative review of recent clinical studies investigating metabolomic markers in adult patients presenting with acute infectious disease, we mainly focused on patients with sepsis and lower respiratory tract infections. Currently, there is a growing body of literature showing that single metabolites from distinct metabolic pathways, as well as more complex metabolomic signatures are associated with disease severity and outcome in patients with systemic infections. These pathways include, among others, metabolomic markers of oxidative stress, steroid hormone and amino acid pathways, and nutritional markers.

Expert commentary: Metabolic profiling has great potential to optimize patient management, to provide new targets for individual therapy and thereby improve survival of patients. At this stage, research mainly focused on the identification of new predictive signatures and less on metabolic determinants to predict treatment response. The transition from observational studies to implementation of novel markers into clinical practice is the next crucial step to prove the usefulness of metabolomic markers in patient care.     

Independent validation of the ICU requirement score in a cohort of acutely poisoned adults

Objective: To independently validate the predictive value of the intensive care requirement score (IRS) in unselected poisoned patients.

Design: Retrospective chart review.

Patients and methods: Five hundred and seventeen out of 585 admissions for acute intoxications could be analyzed. Eleven were excluded for a condition already requiring intensive care unit (ICU) support at admission (e.g., preclinical intubation). A further 57 admissions were excluded due to missing data. The IRS was calculated using a point-scoring system including age, Glasgow Coma Scale, heart rate, type of intoxication, and preexisting conditions. It was then compared to a composite endpoint indicating an ICU requirement (death in hospital, vasopressors, need for ventilation). The endpoint and the point-scoring system were identical to the original publication of the score.

Results and conclusion: Twenty-three out of 517 patients had a complicated clinical course as defined by meeting the endpoint definition. Twenty-one out of 23 complicated courses had a positive IRS (defined as greater or equal 6 points), as compared to 255/494 patients with an uncomplicated clinical course (p?相似文献