一个以前列腺特异抗原密度和穿刺评分为基础的中国人群中前列腺癌根治术后病理升级预测模型的建立

目的：分析前列腺癌根治术后病理得分较穿刺得分增加的原因，并建立一个可以预测中国人群中前列腺癌根治术后病理升 级的模型。方法：以2008 年8 月至2013 年12 月在我院泌尿科行前列腺癌根治性切除术的264例患者的临床资料为基础，根据 术前和术后患者病理得分的变化将其分为升级组和未升级组。运用单因素和多因素logistic 回归分析病理升级的原因，并通过多 因素回归系数建立预测病理升级的诺模图。结果：264 例患者中，共238 例最终纳入统计分析，多因素logistic 回归分析显示前列 腺特异抗原密度(0R=3.854，P=0.001 )和穿刺Gleason(≤ 6)评分是中国人群中前列腺癌根治术后病理升级的独立危险因素。前列腺 特异抗原密度和穿刺得分的ROC 最佳截断取值为0.37 ng/ml 2和8 分。运用上述两个变量建立了一个可用于预测病理升级的诺 模图。结论：前列腺特异抗原密度和穿刺Gleason 评分是预测中国人群中前列腺癌根治术后病理升级的独立危险因素，本研究所 得的诺模图可以很好地预测前列腺癌根治术后的病理升级。     

Sirtuin 5 regulates the proliferation,invasion and migration of prostate cancer cells through acetyl‐CoA acetyltransferase 1

Sirtuin 5 (SIRT5) is a NAD⁺‐dependent class III protein deacetylase, and its role in prostate cancer has not yet been reported. Therefore, to explore the diagnosis and treatment of prostate cancer, we investigated the effect of SIRT5 on prostate cancer. Sirtuin 5 was assessed by immunohistochemistry in 57 normal and cancerous prostate tissues. We found that the tissue expression levels of SIRT5 in patients with Gleason scores ≥7 were significantly different from those in patients with Gleason scores <7 (P < .05, R > 0). Further, mass spectrometry and pathway screening experiments showed that SIRT5 regulated the activity of the mitogen‐activated protein kinase (MAPK) pathway, which in turn modulated the expression of MMP9 and cyclin D1. Being a substrate of SIRT5, acetyl‐CoA acetyltransferase 1 (ACAT1) was regulated by SIRT5. SIRT5 also regulated MAPK pathway activity through ACAT1. These results revealed that SIRT5 promoted the activity of the MAPK pathway through ACAT1, increasing the ability of prostate cancer cells to proliferate, migrate and invade. Overall, these results indicate that SIRT5 expression is closely associated with prostate cancer progression. Understanding the underlying mechanism may provide new targets and methods for the diagnosis and treatment of the disease.     

Circulating exosome‐derived bona fide long non‐coding RNAs predicting the occurrence and metastasis of hepatocellular carcinoma

Although the diagnosis and therapy approach developed, techniques for the early diagnosis of HCC remain insufficient which results in poor prognosis of patients. The traditional biomarker AFP, however, has been proved with low specificity. Circulating exosomal ncRNAs revealed different profiles reflecting the characteristics of tumour. In this study, we mainly focused on circulating exosomal ncRNAs which might be the fingerprint for HCC, especially for the diagnosis or metastasis prediction. A high throughput lncRNA microarray in exosomes extracted from cell‐free plasma was applied. The risk score analysis was employed to screen the potential exosome‐derived lncRNAs in two independent sets based on different clinical parameters in 200 paired HCC patients. After a multi‐stage validation, we finally revealed three lncRNAs, ENSG00000248932.1, ENST00000440688.1 and ENST00000457302.2, increased in HCC comparing with the both chronic hepatitis (CH) patients and cancer‐free controls. ROC curve revealed a higher sensitivity and specificity in predicting the occurrence of HCC from cancer‐free controls and CH patients with the area under curve (AUC) of 0.905 and 0.879 by combining AFP. The three lncRNA panel combined with AFP also indicted a fingerprint function in predicting the metastasis of HCC with the AUC of 0.870. In conclusion, ENSG00000248932.1, ENST00000440688.1 and ENST00000457302.2 might be the potential biomarker for the tumorigenesis prediction from CH patients or healthy controls and may also be applied for dynamic monitoring the metastasis of HCC.     

房颤射频消融术后复发风险预测评分系统的建立及评价

目的:探讨心房颤动(房颤)患者射频消融术后复发的风险因素,并依此构建个性化的风险评分系统。方法:选取2017年1~8月行射频消融术的房颤患者154例作为研究对象,依据术后3个月的随访结果将患者分为复发组及未复发组,采用单因素分析和Logistic回归分析对各风险因素进行分析,构建其评分系统,采用Hosmer-Lemeshow拟合优度检验和ROC曲线下面积评价评分系统的准确度及区分度。结果:术后随访3个月的结果显示共37例(24.03%)房颤患者出现复发,房颤类型、病程、体质量指数(BMI)、左房前后径(LAD)、左房容积(LAV)及超敏C反应蛋白(hs-CRP)水平均是房颤复发的独立风险因素(P<0.05)。构建的风险评分系统得分为0~26分,Hosmer-Lemeshow拟合优度检验:x^2=7.520,P=0.482;ROC曲线下面积为0.864(95%CI:0.837~0.891),预测评分值为15分时,约登指数最大(0.605),此时的敏感度和特异度分别为77.3%和83.2%。结论:房颤患者射频消融术后的复发率较高,依据风险因素构建的风险评分系统具有较高的预测效率和区分能力,可作为房颤患者射频消融术后复发风险评估的参考工具。     

持续镇痛分娩对产妇分娩结局和新生儿评分的影响

目的:研究持续镇痛分娩对产妇分娩结局和新生儿评分的影响。方法:选择2018年7月～2019年7月中国医科大学航空总医院(本院)采取硬膜外分娩镇痛的101例产妇,将其随机分为两组。当产生确切的镇痛效果,进入第二产程后,观察组的51例产妇采用0.4μg/m L舒芬太尼以及0.08%罗哌卡因进行持续镇痛分娩;对照组的50例产妇则在宫口开全后,使用生理盐水替代泵内的局麻药物,直到分娩结束。比较两组产妇催产素的使用率,宫口扩张度和第一、第二产程按压硬膜外自控镇痛泵的次数,分娩方式,新生儿的体质量,脐动脉血pH值,出生后1 min和5 min Apgar评分,产妇修复会阴部时的视觉模拟评分(visual analogue scale, VAS)评分及产妇对于第二产程镇痛的满意度评分。结果:两组产妇催产素的使用率、宫口扩张度和第一、第二产程按压硬膜外自控镇痛泵的次数、分娩方式(剖宫产率、器械助产率、自然分娩率)、第一产程镇痛时间、第一以及第二产程时间相比均无显著差异(P0.05);两组新生儿的体质量,脐动脉血pH值,出生后1 min和5 min Apgar评分小于8分的新生儿所占的比例相比没有明显的差异(P0.05);观察组产妇修复会阴部时的VAS评分明显低于对照组(P0.05),产妇对于第二产程镇痛的满意度评分明显高于对照组(P0.05)。结论:持续镇痛分娩对产妇分娩结局和新生儿评分无明显的影响,但可显著提高产妇对第二产程镇痛和修复会阴部时镇痛的满意度。     

冠心病患者血浆CD69、DKK-1与血脂、炎性因子的关系及其诊断价值分析

摘要 目的：分析冠心病（CHD）患者血浆CD69、Wnt拮抗剂蛋白-1（DKK-1）与血脂、炎性因子的关系,并分析CD69和DKK1对于CHD患者的诊断价值。方法：选取2016年6月~2018年12月在我院诊治的CHD患者136例,根据Gensini评分分为轻度亚组、中度亚组、重度亚组,同时纳入与之年龄、性别匹配的冠状动脉造影正常者136例作为对照组。检测受试者血清炎性因子、血脂、及血浆CD69、DKK-1水平,分析CHD患者CD69、DKK-1水平与血脂、炎性因子、Gensini评分的相关性,采用受试者工作特征（ROC）曲线分析CD69和DKK1对于CHD患者的诊断价值。结果：CHD患者CD69、DKK-1、白细胞介素-10（IL-10)、白细胞介素-6（IL-6）、三酰甘油（TG）、总胆固醇（TC）、低密度脂蛋白（LDL）水平高于对照组,高密度脂蛋白（HDL）水平低于对照组（P<0.05）;随着冠脉狭窄病变程度的加重,CD69、DKK-1、IL-10、IL-6、TC、TG、LDL水平呈升高趋势,HDL水平呈降低趋势（P<0.05）。CHD患者CD69、DKK-1水平与IL-10、IL-6、TC、TG、LDL、Gensini评分均呈正相关,与HDL呈负相关（P<0.05）。ROC曲线分析结果显示,CD69、DKK-1联合检测诊断CHD的灵敏度、特异度分别为0.816、0.846。结论：CHD患者血浆DKK-1、CD69水平升高,与血脂、炎性因子和Gensini评分密切相关,两者联合检测可提高CHD诊断效能。     

云南松形态和叶片碳氮磷化学计量及其海拔变化特征

云南松(Pinus yunnanensis Franch.)是我国西南地区特有的物种,在当地的经济、生态和水土保持功能方面占有重要地位。本研究以云南东北部不同海拔的云南松天然次生林为对象,测定了成年植株的胸径、株高、叶片长度、干重、比叶长及叶片碳、氮、磷含量,分析了不同海拔下叶片碳氮磷化学计量特征。结果表明:随海拔升高,胸径、株高、叶P含量和叶C∶N呈现"中间高,两头低"的趋势,而叶C∶P和N∶P呈现的趋势与叶P和叶C∶N的相反;云南松叶片单束松针长、比叶长、叶C、N随海拔升高呈明显降低的趋势,而单束松针重呈增加的趋势;各海拔的N∶P值(平均值为5.82±0.10)均小于14,且与植株胸径、株高呈显著负相关;该地区云南松对海拔具有较强的适应性,植株生长主要受N元素的营养限制,该结果支持生长速率假说。本研究揭示了云南松化学计量特征的空间格局变化规律,为云南松林的经营管理提供科学依据。     

Efficient screening of predictive biomarkers for individual treatment selection

The development of molecular diagnostic tools to achieve individualized medicine requires identifying predictive biomarkers associated with subgroups of individuals who might receive beneficial or harmful effects from different available treatments. However, due to the large number of candidate biomarkers in the large‐scale genetic and molecular studies, and complex relationships among clinical outcome, biomarkers, and treatments, the ordinary statistical tests for the interactions between treatments and covariates have difficulties from their limited statistical powers. In this paper, we propose an efficient method for detecting predictive biomarkers. We employ weighted loss functions of Chen et al. to directly estimate individual treatment scores and propose synthetic posterior inference for effect sizes of biomarkers. We develop an empirical Bayes approach, namely, we estimate unknown hyperparameters in the prior distribution based on data. We then provide efficient screening methods for the candidate biomarkers via optimal discovery procedure with adequate control of false discovery rate. The proposed method is demonstrated in simulation studies and an application to a breast cancer clinical study in which the proposed method was shown to detect the much larger numbers of significant biomarkers than existing standard methods.