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41.
Major ursodeoxycholic acid (UDCA)-based therapies for primary biliary cirrhosis (PBC) include UDCA only, or combined with either methotrexate (MTX), corticosteroids (COT), colchicine (COC), or bezafibrate (BEF). As the optimum treatment regimen is unclear and warrants exploration, we aimed to compare these therapies in terms of patient mortality or liver transplantation (MOLT) and adverse events (AE).PubMed, the Cochrane Library, and Scopus were searched for randomized controlled trials up to August 31, 2014. We estimated the hazard ratios (HRs) for MOLT and odds ratios (ORs) for AE. A sensitivity analysis based on the dose of UDCA was also executed.Thirty-one eligible articles were included. Compared with COT plus UDCA, UDCA (HR 0.38, 95% confidence interval [CI] 0.09–1.39), BEF plus UDCA (HR 0.29, 95% CI 0.02–4.83), COC plus UDCA (HR 0.39, 95% CI 0.07–2.25), MTX plus UDCA (HR 0.28, 95% CI 0.05–1.63), or OBS (HR 0.49, 95% CI 0.11–2.01) all provided an increased risk of MOLT. With respect to drug AE profile, although not differing appreciably, BEF plus UDCA was associated with more AEs compared with UDCA (OR 3.16, 95% CI 0.59–20.67), COT plus UDCA (OR 2.27, 95% CI 0.15–33.36), COC plus UDCA (OR 1.00, 95% CI 0.09–12.16), MTX plus UDCA (OR 2.03, 95% CI 0.23–17.82), or OBS (OR 3.00, 95% CI 0.53–20.75). The results of sensitivity analyses were highly consistent with previous analyses.COT plus UDCA was the optimal UDCA-based regimen for both MOLT and AEs. BEF plus UDCA was most likely to cause AEs, whereas monotherapy with UDCA and coadministriation of COT plus UDCA appeared to be associated with the fewest AEs for PBC treatment.  相似文献   
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目的:观察鼻腔内窥镜下行鼻腔泪囊造孔术治疗慢性泪囊炎的临床疗效。方法回顾性分析2013年6月至2015年6月在我院收治的慢性泪囊炎患者39例(40只眼),采用鼻腔内窥镜下鼻腔泪囊造孔术进行手术治疗,术后随访3~6个月,观察术眼流脓、溢泪等情况。结果鼻内窥镜下鼻腔泪囊造孔术治疗39例(40只眼)患者,治愈35例(36只眼),好转3例(3只眼),无效1例(1只眼)。治愈率90%,好转率7.5%,无效率2.5%。结论经鼻内窥镜下行鼻腔泪囊造孔术治疗慢性泪囊炎,疗效确切。具有手术简单、创伤小、出血少、无需面部切口、面部不留疤痕等优点。  相似文献   
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Platelet function has been described by many laboratory assays, and PL-11 is a new point-of-care platelet function analyzer based on platelet count drop method, which counts platelet before and after the addition of agonists in the citrated whole blood samples. The present study sought to compare PL-11 with other three major more established assays, light transmission aggregometry (LTA), VerifyNow? aspirin system and thromboelastography (TEG), for monitoring the short-term aspirin responses in healthy individuals. Ten healthy young men took 100?mg/d aspirin for 3-day treatment. Platelet function was measured via PL-11, LTA, VerifyNow and TEG, respectively. The blood samples were collected at baseline, 2 hour, 1 day during the aspirin treatment and 1 day, 5?±?1 days, 8?±?1 days after the aspirin withdrawal. Moreover, 90 additional healthy subjects were recruited to establish a reference range for PL-11. Platelet function of healthy subjects decreased significantly 2 hours after 100?mg/d aspirin intake and began to recover during 4–6 days after the aspirin withdrawal. Correlations between methods were PL-11 vs. LTA (r?=?0.614, p?<?0.01); PL-11 vs. VerifyNow (r?=?0.829, p?<?0.01); PL-11 vs. TEG (r?=?0.697, p?<?0.001). There was no significant bias between PL-11 and LTA at baseline (bias?=?1.94%, p?=?0.804) using Bland-Altman analysis, while the data of PL-11 were significantly higher than LTA (bias?=?24.02%, p?<?0.001) during the aspirin therapy. The reference range for PL-11 in healthy young individuals was from 66.8 to 90.5% (95%CI). When aspirin low-responsiveness was defined as LTA?>?20%, the cut-off values for each method were, respectively: PL-11?>?50%, VerifyNow?>?533 ARU, TEG?>?60.2%. The results of different platelet function assays were uninterchangeable for monitoring aspirin response and correlations among them were also varied. Correlations among PL-11 and other three major assays suggested the ability of PL-11 to assess the treatment effects of aspirin. But a large cohort study is needed to confirm the cut-off value of aspirin response detected by PL-11.  相似文献   
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目的 探讨剂量组学在预测肺癌根治性放疗患者放射性肺炎发生中的应用潜能。方法 回顾性收集行根治性放疗的314例肺癌患者的临床资料、放疗剂量文件、定位及随访CT图像,根据临床资料及影像学随访资料对放射性肺炎进行分级,提取全肺的剂量组学特征,构建机器学习模型。应用1000次自助抽样法(bootstrap)的最小绝对值收敛和选择算子嵌套逻辑回归(LASSO‐LR)及1000次bootstrap的赤池信息量准则(AIC)向后法筛选与放射性肺炎相关的剂量组学特征,随机按照7∶3划分为训练集及验证集,应用逻辑回归建立预测模型,并应用ROC曲线及校正曲线评价模型的性能。结果 共提取120个剂量组学特征,经LASSO‐LR降维筛选得到12个特征进入“特征池”,再经过AIC向后法筛选,最终筛选出6个剂量组学特征进行模型构建,训练集AUC为0.77(95%CI为0.65~0.87),独立验证集AUC为0.72(95%CI为0.64~0.81)。结论 利用剂量组学建立的预测模型具有预测放射性肺炎发生的潜力,但仍需继续纳入多中心数据及前瞻性数据进一步挖掘剂量组学的应用潜能。  相似文献   
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