生物标志物的联合检测对矽肺早期辅助诊断的价值

目的：探讨联合检测生物标志物应用于矽肺早期辅助诊断的灵敏度和特异性。方法：采用立意抽样法，选取65例男性矽肺患者为病例组，以70例接触游离二氧化硅的在岗作业人员为对照组。清晨空腹采集外周血并分离血清，采用比色法测定对照人群和矽肺患者血清中超氧化物歧化酶（SOD）和丙二醛（MDA）水平；酶联免疫吸附法测定白细胞介素-6（IL-6）及肿瘤坏死因子-α（TNF-α）水平，以矽肺I期患者血清中有显著性变化的生物标志物（MDA、IL-6和TNF-α）检测数据为数据集，绘制受试者工作特征曲线（ROC）并建立Logistic回归模型，评估联合检测上述生物标志物对矽肺早期辅助诊断的价值。结果：与对照组相比，矽肺患者血清中SOD表达水平无显著变化（P > 0.05）；矽肺I期及Ⅱ期患者血清中MDA及IL-6表达水平显著升高（P < 0.05）；矽肺I期患者血清中TNF-α表达水平显著升高（P < 0.05）。回归模型结果显示，ROC曲线下面积排序为：3个指标联合检测（0.89） > IL-6（0.86） > MDA（0.81） > TNF-α（0.65）。结论：IL-6、TNF-α和MDA的联合检测可以提高矽肺早期辅助诊断的灵敏度和特异性。     

术前脑状态指数预测老年患者神经认知功能的有效性

目的观察老年患者术前认知功能损伤的发生率,并探讨脑状态指数(cerebral state index, CSI)评估老年患者术前认知功能损伤的有效性。方法择期肺切除手术的老年患者105例,男54例,女51例,年龄65～80岁,BMI 18～30 kg/m~2,ASAⅠ—Ⅲ级。术前行CSI监测和中国改良版蒙特利尔认知功能测试(MoCA)。根据MoCA测试结果将患者分为术前认知功能正常组(MoCA≥26分,N组)和认知功能异常组(MoCA26分,AN组)。以MoCA认知功能测试结果为金标准,基于患者术前CSI建立回归方程,计算认知指数;采用ROC曲线检验认知指数预测术前认知功能损伤的效能。结果有50例(47.6%)患者存在术前认知功能损伤。基于认知指数构建的预测模型诊断老年患者术前认知功能损伤的阈值为0.5,敏感性66.7%,特异性69.2%,曲线下面积(AUC)为0.699(95%CI 0.563～0.835,P=0.007)。结论老年患者术前认知功能损伤的发生率较高,术前脑状态指数能够预测其认知功能损伤,但效能一般。     

Circulating CCL20: A potential biomarker for active vitiligo together with the number of Th1/17 cells

Background

Vitiligo is an autoimmune disease with varying pathological features. Activation of the CCL20-CCR6 axis plays an important role in chronic inflammatory diseases. However, whether CCL20-CCR6 and Th1/17 cells are indicative of active vitiligo is unclear.

Brain CT perfusion improves intracranial vessel occlusion detection on CT angiography

Background and purpose

To evaluate whether brain CT perfusion (CTP) aids in the detection of intracranial vessel occlusion on CT angiography (CTA) in acute ischemic stroke.

5种病原学检测方法在肺结核病诊断中的应用评价

目的 评价涂片法(AFS)、罗氏培养法(L-J)、环介导等温扩增法(LAMP)、多色半巢式荧光定量PCR(Xpert MTB/RIF)、线性探针技术(LPA)对肺结核病的诊断价值。方法 选取2016年1月— 2018年3月陕西省结核病防治研究所和汉中市第二人民医院就诊的258例肺结核可疑症状者作为研究对象,同时应用AFS、L-J、LAMP、Xpert MTB/RIF、LPA对所有研究对象的同一份痰样本进行检测,以临床诊断为标准,分组计算5种病原学检测方法的阳性率;以培养和鉴定为金标准,计算4种方法的敏感度和特异度,并结合受试者工作特征 (ROC)曲线进行分析比较。结果 在肺结核病组中LAMP(73.30%)、Xpert MTB/RIF(77.27%)、LPA(70.45%)的阳性率均显著高于AFS(46.02%)和L-J(58.52%),差异均有统计学意义;以培养和鉴定为金标准,AFS、LAMP、Xpert MTB/RIF、LPA的敏感度分别为68.93%、93.20%、97.09%、92.23%;特异度分别为93.55%、74.84%、75.48%、83.23%;ROC曲线下的面积分别为0.812、0.840、0.863、0.877,P均< 0.05。结论 5种病原学检测方法对肺结核病的诊断均具有较好的辅助作用;与AFS和L-J相比,LAMP、Xpert MTB/RIF、LPA具有较高的阳性检出率,有助于传染源的早期发现。     

Logistic模型和ROC曲线对磷酸钠盐致电解质紊乱的预测分析

目的：应用Logistic模型和ROC曲线探讨磷酸钠盐口服溶液致电解质紊乱的相关危险因素，并进行风险预测。方法：采用回顾性、观察性的研究方法收集使用磷酸钠盐口服溶液患者的病历资料，分析致电解质紊乱的危险因素，建立Logistic模型，绘制风险因素的ROC曲线。结果：共纳入294例（男183例，女111例）患者，78例（占26.5%）患者出现电解质紊乱，其中低钾血症有34例（占11.6%），高氯血症有29例（占9.9%），低钾高氯血症有7例（占2.4%），低钠血症有4例（占1.4%），低钠低氯血症有3例（占1.0%），低钠低氯低钾血症有1例（占0.34%）。单因素方差分析显示年龄、SCr、Ccr 3项指标的差异具有显著性，进一步多因素Logistic模型分析显示CCr分级是发生电解质紊乱的独立危险因素。根据Logistic模型分析结果，用CCr分级构建ROC曲线，曲线下面积0.816。CCr< 60 mL·min^-1时，患者更易发生电解质紊乱，提示CCr是发生电解质紊乱的危险因素。结论：临床医师可根据患者CCr情况选择合适的肠道清洁药物。     

The predictive value of procalcitonin for early detection of infection in elderly type 2 diabetes mellitus

ObjectiveAimed to investigate the predictive value of procalcitonin (PCT) in early detection of infections in elderly patients with type 2 diabetes, and to discover the optimum cut-off points of PCT.MethodsA retrospective study was conducted with type 2 diabetic patients (≥65 years) with lung infection (LI), urinary tract infection (UTI) or skin and soft tissue infection (SSTI). The receiver operating characteristic (ROC) curves of the 3 markers (PCT, WBC count, and CRP) were constructed and compared to assess their accuracies in diagnosing.ResultsAmong the three different groups with LI, UTI or SSTI, the area under the ROC curve (AUC) of PCT was 0.98 (95% confidence interval (CI): 0.96–0.99, p < 0.05) for the LI group, 0.98 (95% CI: 0.96–0.99, p < 0.05) for the UTI group, and 0.97 (95% CI: 0.94–1.00, p < 0.05) for the SSTI group. The optimum cut-off point of PCT level was 0.73 ng/mL (Sn 89.7%, Sp 97.7%) for the LI group, 1.48 ng/mL (Sn 88.9%, Sp 100%) for the UTI group, and 0.73 ng/mL (Sn 85.7%, Sp 97.7%) for the SSTI group.ConclusionPCT demonstrated the strongest correlation with each of the infection types, indicating significant diagnostic value. Optimum cut-off points of PCT levels in elderly diabetes were higher.     

血浆长链非编码RNA转移相关肺腺癌转录因子1对肺癌临床诊断价值

目的探讨血浆中长链非编码RNA转移相关肺腺癌转录因子1(MALAT1)对肺癌临床诊断意义。方法分别检测84例肺癌患者(34例腺癌、26例鳞癌、24例小细胞性肺癌)和60例体检健康者血浆中MALAT1、癌胚抗原(CEA)、CK19片段(CYFRA21-1)、鳞状细胞癌抗原(SCC-Ag)和神经元特异性烯醇化酶(NSE)的表达水平,通过logistic回归分析和受试者工作曲线(ROC曲线)分析其灵敏度、特异度等指标。结果MALAT-1在肺癌患者血浆中比健康对照组的表达水平高(P0.01);当临界点(cutoff值)为0.03时,MALAT-1在腺癌中阳性率为70.59%,其诊断肺癌时AUC、灵敏度和特异度分别为0.70、58.33%、81.67%。MALAT-1、NSE和CYFRA21-1三者联合检测肺癌及小细胞性肺癌的AUC分别为0.91、0.98,灵敏度分别为82.14%、95.83%,特异度分别为85.00%、93.33%。MALAT-1在腺癌中AUC、灵敏度和特异度分别为0.81、76.47%、83.33%。结论 MALAT-1可作为肺癌,特别是肺腺癌血清学诊断独立标志物,MALAT-1、NSE和CYFRA21-1三指标联合可显著提高肺癌及不同肺癌病理类型的诊断效能。     

EEG functional connectivity contributes to outcome prediction of postanoxic coma

ObjectiveTo investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest.MethodsProspective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as “good” (Cerebral Performance Category [CPC] 1–2) or “poor” (CPC 3–5).ResultsWe included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34–56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0–54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50–77%) at 100% specificity.ConclusionFunctional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma.SignificanceFunctional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.