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。     

原发性高血压并糖尿病老年患者血压与靶器官损害的关联性研究

目的:探讨原发性高血压合并糖尿病老年患者血压与靶器官损害的关联性。方法:将本院收治的70例高血压合并糖尿病患者纳入A组,68例单纯高血压患者纳入B组,52例单纯糖尿病患者纳入C组,并将同时期的46例健康体检志愿者纳入D组。对四组患者的临床指标进行统计学分析。结果:A组患者的TC、LDL-C以及IMT水平均显著高于B、C两组;与B组比较,A组患者的24 h SBP、n SBP水平显著增高,同时24 h DBP、d DBP显著降低;A组患者的靶器官损害比例也显著高于其他组,比较差异均有统计学意义(P0.05)。结论:原发性高血压合并糖尿病能够显著加重患者的血压变异性以及靶器官的损害程度。     

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   

胸痛影像学评估的前瞻性多中心研究及进展

胸痛评估的前瞻性多中心影像学研究（PROMISE）始于2010年,该研究分析症状稳定的疑似冠心病病人首选冠状动脉CT血管成像（CCTA）进行解剖学检查或首选无创性功能学检查,旨在探究病人临床结局的差异。PROMISE研究了CCTA、功能学检查及冠状动脉钙化积分和CT血流储备分数等无创性检查方法对疑似冠心病的稳定性胸痛病人的预后价值,并探讨了生物标志物、年龄、性别、代谢性疾病对稳定性胸痛病人的影响。此外,PROMISE开发并验证了风险分层模型和个体化选择检查方法的工具。PROMISE对CCTA和功能学检查的应用产生了很大的影响,为疑似冠心病病人无创检查方法的选择提供了重要循证依据。     

Real-time toxicity prediction of Aconitum stewing system using extractive electrospray ionization mass spectrometry

Due to numerous obstacles such as complex matrices, real-time monitoring of complex reaction systems (e.g., medicinal herb stewing system) has always been a challenge though great values for safe and rational use of drugs. Herein, facilitated by the potential ability on the tolerance of complex matrices of extractive electrospray ionization mass spectrometry, a device was established to realize continuous sampling and real-time quantitative analysis of herb stewing system for the first time. A complete analytical strategy, including data acquisition, data mining, and data evaluation was proposed and implemented with overcoming the usual difficulties in real-time mass spectrometry quantification. The complex Fuzi (the lateral root of Aconitum)–meat stewing systems were real-timely monitored in 150 min by qualitative and quantitative analysis of the nine key alkaloids accurately. The results showed that the strategy worked perfectly and the toxicity of the systems were evaluated and predicated accordingly. Stewing with trotters effectively accelerated the detoxification of Fuzi soup and reduced the overall toxicity to 68%, which was recommended to be used practically for treating rheumatic arthritis and enhancing immunity. The established strategy was versatile, simple, and accurate, which would have a wide application prospect in real-time analysis and evaluation of various complex reaction systems.