重庆市中医医疗机构新型冠状病毒肺炎的临床及影像特征分析

目的分析重庆市中医医疗机构新型冠状病毒肺炎(Novel Coronavirus Pneumonia,NCP)的临床及影像特征,以期为中医辨证施治提供参考。方法对重庆市中医医疗机构2019年12月19日至2020年2月2日收治的18例新型冠状病毒肺炎的临床及胸部CT影像资料进行总结分析,包括流行病学史、临床表现、中医辨证、影像特征等。结果 18例患者中,发病前14 d内与疫区相关人员有密切接触史者15例,1例来自武汉;18例中发热15例,以中、低热为主;中医辨证分型以湿邪郁肺证和邪热壅肺证为主,分别为8例和7例;16例为双肺受累,以多个肺段受累为主要表现,18例患者共计91个肺段受累,主要位于肺段外带或内、外带同时受累,无单独肺段内带受累患者;CT上以磨玻璃影为主要表现,共67个肺段(73.63%),辅以间质改变和实变。结论重庆市中医医疗机构收治的新型冠状病毒肺炎患者具有起病隐匿、临床症状轻等特点,胸部CT表现具有一定的特征,可以作为中医辨证施治的重要参考。     

老化与人类β细胞量和功能

2型糖尿病(T2DM)的病理生理机制复杂,最核心的发病机制是胰岛素抵抗以及伴随的β细胞量(BCM)和功能的下降。老化会导致葡萄糖稳态的精确调节逐渐下降,使T2DM的患病率随着年龄的增长而增加。已有研究表明老化会导致外周胰岛素敏感性下降。然而,现目前对老化过程中BCM和功能的改变尚无充分的了解。对老化过程中β细胞生物学的研究,将有助于我们深入了解其对年龄相关的T2DM的贡献。本文综述了β细胞在分子、形态和功能水平上与年龄相关的改变,为T2DM的防治提供更多的角度和治疗思路。     

胃动康治疗糖尿病胃轻瘫临床研究

目的观察胃动康治疗糖尿病胃轻瘫(DGP)的临床疗效。方法60例DGP患者随机分为两组,治疗组30例口服胃动康;对照组30例服用吗丁啉。两组疗程均为4周。观察患者消化不良症状积分、胃排空及生活质量改变情况。结果治疗组临床疗效优于对照组,其治疗前后症状积分、症状缓解程度优于对照组,且不良反应轻微。结论胃动康治疗DGP具有良好的临床疗效和安全性。     

银屑病代谢组学研究进展

银屑病是临床常见的复发难治性皮肤病,其精准诊疗是皮肤科领域研究的热点。代谢组学广泛运用于疾病机制、药效评价等研究领域。本文综述近年来国内外基于代谢组学技术开展银屑病代谢标志物筛选、代谢通路分析与网络构建、药物代谢效应靶标等进展,并就当前存在的问题进行归纳和展望,以期为研究其发病机制,进而实现精准治疗提供参考。     

济生乌梅胶囊治疗慢性咽炎临床研究

目的观察济生乌梅胶囊治疗慢性咽炎（痰瘀互结证）的临床疗效。方法将患者187例随机分为两组。治疗组95例服用济生乌梅胶囊,对照组92例服用利咽灵片。结果治疗组总有效率81．05％,高于对照组的65．22％;治疗组主要症状体征的改善亦优于对照组（P〈0．05）。结论济生乌梅胶囊是治疗慢性咽炎（痰瘀互结证）的有效制剂。     

醒脑开窍针法对急性缺血性脑卒中患者的临床疗效及预后预警模型建立（英文）

目的:探究醒脑开窍针法对急性缺血性脑卒中患者的临床疗效,通过logistic回归分析,建立预后预警模型以便针对性预见性治疗。方法:将96例急性缺血性脑卒中患者随机分为臭氧组和臭氧+醒脑开窍针组,每组各48例。臭氧组采用臭氧疗法进行治疗,臭氧+醒脑开窍针组在臭氧组的基础上针刺内关,水沟,三阴交、下极泉、委中、尺泽,每日一次,7天一个疗程,连续治疗3个疗程。于治疗前及治疗后48 h检测两组患者血清学指标白细胞介素-6(IL-6)、肿瘤坏死因子-α (TNF-α)、超敏C-反应蛋白(hs-CRP),采用美国国立卫生研究院卒中量表(NIHSS)评分、蒙特利尔认知评估量表(MoCA)评分评估患者的神经及认知功能,采用改良rankin量表评估临床疗效,统计两组不良反应发生率,利用多因素Logistic回归分析筛选出影响患者预后的危险因素并建立相应的列线图预警模型。结果:(1)临床疗效:治疗后臭氧+醒脑开窍针组IL-6、TNF-α、hs-CRP水平低于臭氧组,差异有统计学意义(all P<0.05)。治疗后,与臭氧组比较,臭氧+醒脑开窍针组NIHSS评分较低,BI、MoCA评分较高,差异均有统...     

A hybrid approach for risk stratification and predictive modelling of 30-days unplanned readmission of comorbid patients with diabetes

ObjectivesWhen comorbid patients with diabetes have 30-days Unplanned Readmission (URA), they attract more burdens to the healthcare system due to increased cost of treatment, insurance penalties to hospitals, and unavailable bed spaces for new patients. This paper, therefore, aims to develop a risk stratification and a predictive model for identifying patients at various risk severities of 30-days URA.MethodsPatients records of comorbid patients with diabetes treated with different medications were collected from different hospitals and analysed with Principal Component Analysis (PCA) and Multivariate Logistic Regression (MLR) to determine the probability of 30-days URA, which is classified into very low, low, moderate, high, and very high. The risk classes are later modelled using ANOVA feature selection to identify the optimal predictors and the best random forest (RF) hyperparameters for 30-days URA risk stratification. Synthetic Minority Oversampling Technique (SMOTE) was used to balance the risk classes while employing a10-fold cross-validation.ResultsAfter analysing 17,933 episodes of comorbid diabetes patients' treatment, 10.71% are identified to have 30-days URA with 61.95% of patients at moderate risk, 35.5% at low risk, 2.25% at very low risk, 0.37% at high risk, and 0.08% at very high risk. The predictive accuracy of RF is: - recall: 0.947 ± 0.035, precision: 0.951 ± 0.033, F1-score: 0.947 ± 0.035, AUC: 0.994 ± 0.007 and Average Precision (AP) of 0.99. The predictive accuracies of the risk classes measured with F1-score are: - very low: 0.985 ± 0.019, low risk: 0.871 ± 0.079, moderate: 0.881 ± 0.093, high: 0.999 ± 0.003, and very high: 1.000 ± 0.00.ConclusionThis study identified the risk severity of comorbid patients with diabetes treated with different medications, making it easier to identify those that will be prioritized on hospitalization to minimize 30-days URA. By relying on the technique developed, vulnerable patients to 30-days URA can be given better post-discharge monitoring to build critical self-management skills that will minimize the cost of diabetes care and improve the quality of life.