Differential Diagnosis of Acute Diarrhea

Acute diarrhea is a condition of increased water stool content, stool volume, and number of bowel movements that lasts less than 14 days. Mild diarrhea is usually self-limiting; however, undertreated moderate to severe diarrhea may cause severe dehydration and lead to hypovolemic shock. In order to prevent severe dehydration and treat patients appropriately, it is crucial for health care providers to determine the right diagnosis of patients with acute diarrhea. This article focuses on pathophysiology, general patient presentation, diagnostic tests and differential diagnosis lists of acute diarrhea to discuss which diagnosis should be made based on patient presentation and objective data.     

Using a monotone single-index model to stabilize the propensity score in missing data problems and causal inference

The augmented inverse weighting method is one of the most popular methods for estimating the mean of the response in causal inference and missing data problems. An important component of this method is the propensity score. Popular parametric models for the propensity score include the logistic, probit, and complementary log-log models. A common feature of these models is that the propensity score is a monotonic function of a linear combination of the explanatory variables. To avoid the need to choose a model, we model the propensity score via a semiparametric single-index model, in which the score is an unknown monotonic nondecreasing function of the given single index. Under this new model, the augmented inverse weighting estimator (AIWE) of the mean of the response is asymptotically linear, semiparametrically efficient, and more robust than existing estimators. Moreover, we have made a surprising observation. The inverse probability weighting and AIWEs based on a correctly specified parametric model may have worse performance than their counterparts based on a nonparametric model. A heuristic explanation of this phenomenon is provided. A real-data example is used to illustrate the proposed methods.     

A prognostic signature based on immune-related genes for cervical squamous cell carcinoma and endocervical adenocarcinoma

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is the fourth commonest female malignancy worldwide. CESC progresses in immune-microenvironment mainly composed of infiltrating immune and stromal cells. Here, we performed an integrated analysis incorporating the expression profiles from the Cancer Genome Atlas (TCGA) database and scores of immune and stromal cells calculated by Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm. A two-gene signature (CD1C and CD6 genes) was established to predict the prognosis of CESC. Based on this signature, patients were divided into the high- and low-risk groups, and this signature showed good prognostic performance according to the results of Kaplan-Meier analysis and receiver operating characteristic (ROC) analysis in train set and two validation sets. A nomogram was built for evaluating the clinical applicability of this signature. In addition, based on Tumor Immune Estimation Resource (TIMER) database, 2 hub genes showed negative correlations with tumor purity and positive correlations with infiltrating levels of immune filtrating cells. What’s more, we propose new treatment strategies for the two prognostic subtypes. Low- risk patients were found presenting with a higher level of immune checkpoint molecules and showing higher immunogenicity in immunophenoscore (IPS) analysis, which indicated a better response for immunotherapy. Meanwhile, estimated by Genomics of Drug Sensitivity in Cancer (GDSC) database, the high-risk patients showed sensitive responses to five chemotherapy drugs. Finally, 10 candidate small-molecule drugs for CESC were defined. In summary, the CD1C-CD6 signature can accurately predict the prognosis of CESC.     

全国名中医丁书文益气活血解毒法治疗心房颤动的处方用药规律挖掘＊

目的 丁书文教授创新性地提出了心系疾病热毒学说，运用益气活血解毒法治疗房颤疗效显著。本研究以丁教授多年的处方数据为基础，探讨丁教授益气活血解毒法治疗房颤的用药规律。方法 收集来自山东中医药大学附属医院门诊系统的丁书文教授处方数据，筛选出治疗房颤的372个处方，采用关联规则、点式互信息、复杂网络等数据挖掘方法对丁教授的药物配伍和处方进行分析。结果 本研究利用复杂网络分析方法获得了丁教授治疗房颤的核心处方，提示益气活血解毒法是其主要治则治法；利用处方相似性和社团分析方法，获得15组类方，其中3种类方与丁教授总结的三大治法高度符合；为探寻丁教授清热解毒配伍特点，结合关联规则和点式互信息算法，获得了10个丁教授清热解毒配伍药对。结论 数据挖掘技术提供了名医处方数据分析与提炼的可行方法，对丁教授经验的传承和临床应用提供直接可靠的依据。     

基于3种数据挖掘模型分析16 618例含附子中成药的处方用药规律

目的:通过数据挖掘分析某院含附子的中成药处方,从中医辨证论治的角度分析该类中成药的临床用药特征规律,探究数据挖掘在中医药处方分析中的应用前景。方法:抽取2017年11月至2018年10月某院门诊含附子成分的中成药处方,从患者基本情况、中医疾病、中医证候等方面回顾分析该类中成药的临床使用情况,采用Microsoft Office Excel 2016进行基本统计,并结合Microsoft SQL Server Analysis Services 2012的3种数据挖掘算法解析处方用药特征。结果:某院使用含附子中成药共8种,14岁以下患者用该类药最多,其中小儿肺咳颗粒用量最大,中医疾病多为咳嗽、感冒、咳喘,中医证型多为寒热错杂证、风痰证、外感风邪。温补肾阳的中成药处方包含金匱肾气丸、龙鹿胶囊、尪痹胶囊、桂附地黄丸,中医疾病多为痹症、眩晕、虚劳等,证型主要为肝肾不足、肾虚血瘀、脾肾不足。决策树分析显示使用含附子的中成药处方具有年龄分布特点,其中中医疾病及证型为主要决策点;聚类分析根据该类处方患者年龄、性别、中西医诊断、中医证型将中成药处方分为10类;关联性分析显示小儿肺咳颗粒联用情况最多,但关联度不高,金匱肾气丸、芪苈强心胶囊、龙鹿胶囊和尪痹胶囊有各自的强关联用药。结论:数据挖掘算法纳入多因素分析临床处方的应用更接近中医临床辩证论治思维,结合基础统计可知某院含附子的处方整体用药符合中医辨证论治的特点。     

Follow-up on: optimizing lesion detection in small bowel capsule endoscopy and beyond: from present problems to future solutions

Introduction: This review presents noteworthy advances in clinical and experimental Capsule Endoscopy (CE), focusing on the progress that has been reported over the last 5 years since our previous review on the subject.

Areas covered: This study presents the commercially available CE platforms, as well as the advances made in optimizing the diagnostic capabilities of CE. The latter includes recent concept and prototype capsule endoscopes, medical approaches to improve diagnostic yield, and progress in software for enhancing visualization, abnormality detection, and lesion localization.

Expert commentary: Currently, moving through the second decade of CE evolution, there are still several open issues and remarkable challenges to overcome.