Plasma phospho-tau181 increases with Alzheimer's disease clinical severity and is associated with tau- and amyloid-positron emission tomography

Introduction

We examined and compared plasma phospho-tau181 (pTau181) and total tau: (1) across the Alzheimer's disease (AD) clinical spectrum; (2) in relation to brain amyloid β (Aβ) positron emission tomography (PET), tau PET, and cortical thickness; and (3) as a screening tool for elevated brain Aβ.

老年2型糖尿病患者糖化血红蛋白预测模型的建立与评分表的开发

背景　糖化血红蛋白（HbA1c）控制达标可降低老年2型糖尿病患者的并发症发生风险，维持良好的HbA1c是管理老年2型糖尿病的重要手段，但近年来有越来越多的研究表明老年2型糖尿病患者HbA1c达标率普遍较低，而HbA1c预测模型可以辅助降低老年糖尿病患者HbA1c控制不达标的风险。目的　寻找影响老年2型糖尿病患者HbA1c控制达标的因素，建立HbA1c达标预测模型及评分表，为老年2型糖尿病患者的血糖管理提供一种可靠工具。方法　采用面对面的问卷调查方式收集2018年3月-2019年12月在四川省人民医院内分泌科就诊的老年2型糖尿病患者的性别、年龄、在岗情况、民族、婚姻状况、文化程度、体型、中心性肥胖、糖尿病家族史、前次HbA1c、糖尿病病程、空腹血糖监测情况、本次HbA1c检查结果、空腹血糖、现阶段治疗方案维持时间、服药依从性情况、使用口服药物种类、是否使用胰岛素、降糖药物日花费、是否高血压、是否高脂血症、是否合并糖尿病并发症、每日运动时间、睡眠情况、是否合理控制饮食、抑郁状态等信息，并根据患者本次HbA1c检查结果分为HbA1c达标组（224例）及HbA1c未达标组（259例）。使用单因素Logistic分析及Lasso-Logistic回归分析筛选变量，以HbA1c为结局指标建立Logistic回归模型与评分表。采用Bootstrap方法对模型进行内部验证，使用受试者工作特征曲线（ROC曲线）和校准图评价模型的区分度和校准度、使用评分-发生概率图验证评分表性能，并找出评分表的最佳切点。结果　两组老年2型糖尿病患者年龄、中心性肥胖、前次HbA1c、糖尿病病程、空腹血糖、服药依从性、使用口服药物种类、使用胰岛素情况、糖尿病并发症情况、每日运动时间、合理控制饮食情况、抑郁状态比较，差异均有统计学意义（P<0.05）。单因素Logistic回归分析结果显示，年龄、前次HbA1c、糖尿病病程、空腹血糖、服药依从性、使用胰岛素情况、糖尿病并发症情况、每日运动时间、合理控制饮食情况、中心性肥胖情况是HbA1c达标的影响因素（P<0.05）；Lasso-Logistic回归分析结果显示，前次HbA1c、糖尿病病程、空腹血糖、服药依从性、使用胰岛素情况、每日运动时间、合理控制饮食情况是老年2型糖尿病患者HbA1c达标的影响因素（P<0.05）。根据Lasso-Logistic分析结果构建的模型回归方程为Logit（P）=-3.89+1.72×〔前次HbA1c（>7%）〕 +0.97×〔糖尿病病程（2~10年）〕 +1.41×〔糖尿病病程（>10年）〕+1.51×〔空腹血糖（≥7 mmol）〕+1.02×〔服药依从性（一般或差）〕+0.85×〔是否使用胰岛素（是）〕+0.58×〔每日运动时间（>0~0.5 h）〕 +1.21×〔每日运动时间（无运动）〕+1.09×〔是否合理控制饮食（否）〕（满足〔〕中的条件为1，不满足为0）。模型进行内部验证结果显示，模型的校准曲线与标准线接近，模型的ROC曲线下面积（AUC）为0.86〔95%CI（0.83，0.89）〕。将Lasso-Logistic回归方程中自变量的回归系数四舍五入、取整为评分表赋值，形成总分为9分的HbA1c预测评分表，0~9分对应的HbA1c控制不达标发生概率为3.7%~100.0%，最大约登指数为0.56时，评分表切点为5分，该分值下评分表的灵敏度为79.54%、特异度为76.79%、准确率为78.26%。结论　联合患者前次HbA1c、糖尿病病程、是否使用胰岛素、是否定量合理进食、服药依从性、每日运动时间与空腹血糖构建的预测评分表可对老年2型糖尿病患者未来的HbA1c达标情况做出有效预测，有一定临床应用和推广价值。     

Predicting factors of postoperative relapse in T2-4N0M0 colorectal cancer patients via harvesting a minimum of 12 lymph nodes

Background and aim  The aim of this retrospective study was to determine which clinicopathological factors influenced the incidence of postoperative relapse and overall survival rates after radical resection of T_2-4N₀M₀ colorectal cancer (CRC) patients via harvesting a minimum of 12 lymph nodes. Materials and methods  Between January 2001 and June 2006, a total of 342 T_2-4N₀M₀ CRC patients who underwent radical resection were retrospectively analyzed in Kaohsiung Medical University Hospital. Of these 342 patients, 155 were observed by harvesting a minimum of 12 lymph nodes. These 155 patients were followed up intensively, and their outcomes were investigated retrospectively. Results  Of 155 patients, 83 were men (53.5%) and 72 (46.5%) were women. The mean age was 65.5 ± 11.1 years (range, 24–89 years). The median follow-up period was 49 months (range, 19–80 months). The present data showed invasive depth (P = 0.012), vascular invasion (P < 0.001), and perineural invasion (P = 0.009) as significantly prognostic factors for postoperative 5-year relapse rate by Kaplan–Meier analysis. Likewise, invasive depth (P = 0.013), vascular invasion (P < 0.001), and perineural invasion (P = 0.008) were significant factors for postoperative 5-year survival rate. Meanwhile, using a Cox proportional hazards analysis, depth of tumor invasion (P = 0.026) and vascular invasion (P = 0.001) were the independent predictors for postoperative relapse. Furthermore, the presence of vascular invasion was considerably correlated to the higher postoperative relapse rate and the poorer overall survival rates by survival analyses (P < 0.0001). Conclusions  Besides the conventional depth of tumor invasion, this study highlights the potential for using vascular invasion as a means of identifying a subgroup of T_2-4N₀M₀ CRC patients with adequate lymph node harvest at higher risk who would potential benefit from adjuvant therapy after surgery.     

Quantifying the determinants of outbreak detection performance through simulation and machine learning

ObjectiveTo develop a probabilistic model for discovering and quantifying determinants of outbreak detection and to use the model to predict detection performance for new outbreaks.Materials and methodsWe used an existing software platform to simulate waterborne disease outbreaks of varying duration and magnitude. The simulated data were overlaid on real data from visits to emergency department in Montreal for gastroenteritis. We analyzed the combined data using biosurveillance algorithms, varying their parameters over a wide range. We then applied structure and parameter learning algorithms to the resulting data set to build a Bayesian network model for predicting detection performance as a function of outbreak characteristics and surveillance system parameters. We evaluated the predictions of this model through 5-fold cross-validation.ResultsThe model predicted performance metrics of commonly used outbreak detection methods with an accuracy greater than 0.80. The model also quantified the influence of different outbreak characteristics and parameters of biosurveillance algorithms on detection performance in practically relevant surveillance scenarios. In addition to identifying characteristics expected a priori to have a strong influence on detection performance, such as the alerting threshold and the peak size of the outbreak, the model suggested an important role for other algorithm features, such as adjustment for weekly patterns.ConclusionWe developed a model that accurately predicts how characteristics of disease outbreaks and detection methods will influence on detection. This model can be used to compare the performance of detection methods under different surveillance scenarios, to gain insight into which characteristics of outbreaks and biosurveillance algorithms drive detection performance, and to guide the configuration of surveillance systems.     

血清肿瘤标志物CA15-3对Her-2阳性乳腺癌术后化疗疗效的评估价值

目的探讨糖类抗原CA15—3在Her-2过表达乳腺癌患者术后化疗疗效监测的评估价值。方法回顾性分析66例Her-2阳性乳腺癌患者术后以蒽环类及紫杉类（44例采用Herceptin生物化疗）为基础化疗过程中血清CA15-3的变化。根据2000年RECIST标准分有效组（OR组）及无效组（NR组）,通过分析两组治疗前后血清CA15-3变化及治疗前后CA15-3下降比率绘制ROC曲线特征评估其对化疗疗效的价值。结果与治疗前相比,所有患者治疗后CA15-3血清水平显著下降（P〈0．05）,治疗前有效组及无效组间CA15-3血清水平无明显差异（P〉0．05）,有效组治疗前后CA15-3血清水平与化疗相关,治疗前后有显著差异（P〈0．05）,而无效组治疗前后与化疗无相关,治疗前后无显著差异（P〉0．05）,治疗前后CA15—3下降比率绘制ROC曲线,其曲线下面积为O．527。采用Herceptin的生物化疗患者,与治疗前相比治疗后CA15—3血清水平亦显著下降（P〈0．05）,治疗前有效组及无效组间CA15-3血清水平亦无明显差异（P〉0．05）,有效组治疗前后CA15—3血清水平与化疗相关,治疗前后有显著性差异（P〈0．05）,而无效组治疗前后血清CA15-3水平与化疗相关（P〈0．05）,但无显著性差异（P〉0．05）。采用治疗前后CA15-3下降比率绘制ROC曲线,其曲线下面积为O．618。结论CAl5-3在Her-2阳性乳腺癌患者术后化疗疗效有一定预测作用,在Herceptin为基础的生物化疗中有一定的敏感性及特异性。     

改良早期预警评分系统在预测急诊抢救室患者病情中的应用研究

目的探讨改良早期预警评分系统（MEWS）在急诊抢救室的应用价值。方法对2012年4月-5月在急诊抢救室就诊的213例患者进行MEWS评分,分析不同分数段患者的分布特点,追踪患者人院后的去向、病情转归及收住专科病房和重症医学科（ICU/CCU）的时间。结果与MEWS得分≥5分的患者相比,MEWS得分〈5分者好转出院、转入专科病房的比例较高,转入ICU/CCU比例低,差异有统计学意义（P〈0．05）;不同MEWS评分段患者转入ICU／CCU的时间最短,其次是出院回家,转入专科病房的时间最长,但MEWS得分〈5分者与≥5分者出院、转入专科病房和ICU／CCU时间之间的差异无统计学意义（P〉0．05）。结论MEWS可以预测患者病情变化及严重程度,对医护人员及时采取救护措施、合理安排住院有一定的指导作用,值得推广应用。     

脑电图预测氯氮平治疗精神分裂症的疗效

目的探究氯氮平治疗精神分裂症过程中脑电图（EEG）演变规律与疗效的关系,以便用EEG预测疗效,指导治疗.方法对128例精神分裂症患者行氯氮平单药治疗,在100～150mg/d、175～300mg/d、325～550mg/d时分别行EEG检查,减量或停药后再次复查EEG.用BPRS对每一入组患者治疗前、后各进行一次评定,减分率＞40%为疗效好,减分率≤40%为无效.结果氯氮平治疗过程中84.96%（322/379）的EEG有异常改变,其中94.72%（305/322）为广泛性异常或在广泛性异常背景上局限性显著.EEG异常程度与服用氯氮平剂量呈正相关.21.09%（27/128）的患者治疗早期EEG无改变（A组）,其有效率为7.41%（2/27）.78.91%（101/128）的患者氯氮平治疗早期EEG即有异常改变（B组）,其有效率为94.06%（95/101）.两组有效率相比有非常显著性差异（χ^2=87.15,P〈0.001）.结论 EEG可以预测氯氮平治疗精神分裂症的疗效,氯氮平治疗早期EEG即有异常改变者,预示治疗有效,EEG无变化者预示疗效欠佳.     

心室晚电位预测早期冠心病的价值

本文应用12导联同步数字心电图机,进行了心室晚电位的检测,共检测10例,早期冠心病,心室晚电位检测阳性为6例,占60％,对阳性患者进行了病机病理分析。阐明了心室晚电位检测对早期冠心病的诊断治疗提供准确依据。并提示冠心病患者的阳性率高,应引起同道们的关注。