二补助育汤对胚胎着床障碍小鼠子宫血管生成相关因子表达的影响

目的：探讨二补助育汤对胚胎着床障碍模型小鼠子宫内膜形态及血管生成素-1（Ang-1）mRNA、血管内皮生长因子（VEGF）mRNA的表达和定位的影响。方法：24只ICR雌性小鼠随机分为空白组、模型组、戊酸雌二醇组、二补助育汤组，每组6只，用米非司酮建立胚胎着床障碍动物模型，各组给予相应药物灌胃，妊娠第5天处死小鼠后，检测各组妊娠率、平均着床位点数、子宫内膜Ang-1和VEGF mRNA表达量及其蛋白定位。结果：模型组小鼠平均胚胎着床位点数、Ang-1 mRNA、VEGF mRNA表达量明显低于空白组（均P<0.05）；与模型组比较，二补助育汤组平均胚胎着床位点数、Ang-1 mRNA、VEGF mRNA表达量显著提高（均P<0.05）。结论：二补助育汤可提高子宫内膜Ang-1和VEGF蛋白表达量，促进子宫内膜血管生成，从而提高子宫内膜容受性。     

Can muscle weakness and disability influence the relationship between pain catastrophizing and pain worsening in patients with knee osteoarthritis? A cross-sectional study

ObjectiveTo verify if the relationship between pain catastrophizing and pain worsening would be mediated by muscle weakness and disability in patients with symptomatic knee osteoarthritis.MethodsThis was a cross-sectional study in a hospital out-patient setting. Convenience sampling was used with a total of 50 participants with symptomatic knee osteoarthritis. Pain and the activities of daily livings (ADL) were assessed using the Knee Injury and Osteoarthritis Outcome Score (KOOS) subscale. Pain catastrophizing was assessed using the Coping Strategy Questionnaire (CSQ) subscale. Muscle strength of knee extension and 30-s chair stand test (30CST) were also assessed. Path analysis was performed to test the hypothetical model. Goodness of fit of models were assessed by using statistical parameters such as the chi-square value, goodness of fit index (GFI), adjusted goodness of fit index (AGFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA).ResultsThe chi-square values were not significant (chi-square = 0.283, p = 0.594), and the indices of goodness of fit were high, implying a valid model (GFI = 1.000; AGFI = 0.997; CFI = 1.000; RMSEA = 0.000). Pain was influenced significantly by muscle strength and ADL; muscle strength was influenced significantly by ADL via 30CST; ADL was influenced by pain catastrophizing.ConclusionThe relationship between pain catastrophizing with pain worsening are mediated by muscle weakness and disability.     

Aminotransferases During Treatment Predict Long-Term Survival in Patients With Autoimmune Hepatitis Type 1: A Landmark Analysis

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Validation of PATHFx 2.0: An open-source tool for estimating survival in patients undergoing pathologic fracture fixation

Treatment decisions in patients with metastatic bone disease rely on accurate survival estimation. We developed the original PATHFx models using expensive, proprietary software and now seek to provide a more cost-effective solution. Using open-source machine learning software to create PATHFx version 2.0, we asked whether PATHFx 2.0 could be created using open-source methods and externally validated in two unique patient populations. The training set of a well-characterized, database records of 189 patients and the bnlearn package within R Version 3.5.1 (R Foundation for Statistical Computing), was used to establish a series of Bayesian belief network models designed to predict survival at 1, 3, 6, 12, 18, and 24 months. Each was externally validated in both a Scandinavian (n = 815 patients) and a Japanese (n = 261 patients) data set. Brier scores and receiver operating characteristic curves to assessed discriminatory ability. Decision curve analysis (DCA) evaluated whether models should be used clinically. DCA showed that the model should be used clinically at all time points in the Scandinavian data set. For the 1-month time point, DCA of the Japanese data set suggested to expect better outcomes assuming all patients will survive greater than 1 month. Brier scores for each curve demonstrate that the models are accurate at each time point. Statement of Clinical Significance: we successfully transitioned to PATHFx 2.0 using open-source software and externally validated it in two unique patient populations, which can be used as a cost-effective option to guide surgical decisions in patients with metastatic bone disease.     

替考拉宁在中国成人患者中的群体药动学研究

目的建立中国成年患者的替考拉宁(teicoplanin,TEC)群体药动学(population pharmacokinetics,PPK)模型,考察TEC药动学参数的影响因素。方法前瞻性收集139例革兰阳性球菌感染患者静脉注射TEC后的222份常规监测血药浓度和相关信息,采用一级消除的一室模型进行数据拟合,并应用非线性混合效应模型(nonlinear mixed effect model,NONMEM)程序建立PPK模型。采用Bootstrap、正态预测分布误差法(normalized predictive distribution error,NPDE)进行最终模型评价。利用蒙特卡洛模拟法对给药方案进行优化。结果确定了肌酐清除率(creatinine clearance,CLcr)、白蛋白(albumin,ALB)为影响TEC清除率的主要因素。最终模型为:CL(L·h^-1)=1.24×(CLcr/77)0.564×31/ALB;V(L)=69.2。验证表明,模型稳定、有效,且有较好的预测效能。对于不同ALB和CLcr的多数患者起始负荷剂量400 mg/q12h,iv,3次,维持剂量400~800 mg·d^-1的给药方案可达有效治疗谷浓度。严重感染者需调整负荷剂量至800 mg/q12h,iv,3次,维持剂量400~800 mg·d^-1的给药方案来确保血药浓度达到15 mg·L^-1以上。结论本实验报道了CLcr、ALB对TEC清除率有显著影响,所建模型对TEC在中国成人患者中实现个体化给药具有重要应用价值。