预测加氢裂化过程的产率

以缓和加氢裂化数据为基础,对于两种典型的加氢裂化动力学模型－－Stangeland模型和改进MHC模型,使用Shor最优化法进行了参数的拟合,比较了这两种动力学模型的结果、算法、复杂度以及预测能力。结果表明,改进MHC模型是一种更为合理的动力学模型,该模型也可用于实际加氢过程。     

Sperm binding to hyaluronan is an excellent predictor of Nili-Ravi buffalo bull fertility

This study reports the first evaluation of sperm hyaluronan binding assay (HBA) for predicting the fertility of Nili-Ravi buffalo bulls in relation to standard parameters of sperm quality. Cryopreserved semen doses of low (n = 6), medium (n = 3) and high fertility (n = 8) bulls based on their respective return rates were used. Significantly, more spermatozoa bound to hyaluronan from the most fertile bulls (57.15% ± 1.44) compared with medium (42.46% ± 1.08) and low fertility bulls (29.70% ± 0.78). A strongly positive correlation (r = .824, p < .01) was found between HBA and fertility that predicts a 67.9% variability (r² = .679, p < .01) in fertility. HBA was also strongly positively correlated with sperm viability (r = .679, p < .01) followed by their live/dead ratio (r = .637, p < .01), uncapacitated spermatozoa (r = .631, p < .01), normal apical ridge (r = .459, p < .01), motility (r = .434, p < .01), mature spermatozoa with low residual histones (r = .364, p < .01), high plasma membrane integrity (r = .316, p < .01) and nonfragmented DNA levels (r = .236, p < .05). It was negatively correlated with spermatozoa having reacted acrosome (r = −.654, p < .01). A fertility model built using a combination of sperm HBA and either sperm livability or viability predicts, respectively, 86.1% (r² = .861, p < .01) and 85.9% (r² = .859, p < .01) variability in buffalo bull fertility. In conclusion, sperm HBA may prove to be a single robust predictor of Nili-Ravi buffalo bull fertility.     

Can Machine Learning Methods Produce Accurate and Easy-to-Use Preoperative Prediction Models of One-Year Improvements in Pain and Functioning After Knee Arthroplasty?

BackgroundApproximately 15%-20% of total knee arthroplasty (TKA) patients do not experience clinically meaningful improvements. We sought to compare the accuracy and parsimony of several machine learning strategies for developing predictive models of failing to experience minimal clinically important differences in patient-reported outcome measures (PROMs) 1 year after TKA.MethodsPatients (N = 587) in 3 large Veteran Health Administration facilities completed PROMs before and 1 year after TKA (92% follow-up). Preoperative PROMs and electronic health record data were used to develop and validate models to predict failing to experience at least a minimal clinically important difference in Knee Injury and Osteoarthritis Outcome Score (KOOS) Total, KOOS JR, and KOOS subscales (Pain, Symptoms, Activities of Daily Living, Quality of Life, and recreation). Several machine learning strategies were used for model development. Ten-fold cross-validation and bootstrapping were used to produce measures of overall accuracy (C-statistic, Brier Score). The sensitivity and specificity of various predicted probability cut-points were examined.ResultsThe most accurate models produced were for the Activities of Daily Living, Pain, Symptoms, and Quality of Life subscales of the KOOS (C-statistics 0.76, 0.72, 0.72, and 0.71, respectively). Strategies varied substantially in terms of the numbers of inputs required to achieve similar accuracy, with none being superior for all outcomes.ConclusionModels produced in this project provide estimates of patient-specific improvements in major outcomes 1 year after TKA. Integrating these models into clinical decision support, informed consent and shared decision making could improve patient selection, education, and satisfaction.Level of EvidenceLevel III, diagnostic study.     

Is Patient Satisfaction After Total Knee Arthroplasty Predictable Using Patient Characteristics and Preoperative Patient-Reported Outcomes?

BackgroundDissatisfaction after total knee arthroplasty (TKA) remains a difficult problem. Patient characteristics and preoperative patient-reported outcomes (PROs) are potential predictors of satisfaction one year after TKA. Being able to predict the outcome preoperatively might reduce the number of less satisfied patients.MethodsA retrospective cohort study on prospectively collected data of 1239 primary TKA patients (ASA I-II, BMI <35) was performed. Primary outcome was degree of patient satisfaction one year after TKA (Numeric Rating Scale (NRS) 0-10). Secondary outcomes were degree of patient satisfaction six months and two years after TKA and being dissatisfied (NRS 0-6) or satisfied (NRS 7-10) at all three time points. Multivariate linear and binary logistic regression analyses were executed with patient characteristics and preoperative PROs as potential predictors.ResultsOne year after TKA, median NRS satisfaction score was 9.0 (8.0-10.0) and 1117 (90.2%) patients were satisfied. BMI, degree of medial cartilage damage, previous knee surgery, Knee injury and Osteoarthritis Outcome Score-Physical Function Short Form score, EQ VAS score, and anxiety were identified as predictors of the degree of patient satisfaction (P = .000, R² = 0.027). Models on secondary outcomes reported R² of 1.7%-7.1% (P < .05). All models showed bad agreement between observed and predicted values for lower NRS satisfaction scores and being dissatisfied.ConclusionThe degree of patient satisfaction and the chance of being dissatisfied or satisfied six months, one, and two years after TKA are predictable by patient characteristics and preoperative PROs but not at a reliability level that is clinically useful.     

基于生物信息学探讨SARS-CoV-2介导免疫炎症反应诱发动脉粥样硬化进展的潜在机制与防治中药筛选

目的 应用生物信息学技术,从免疫炎症角度探索严重急性呼吸综合征冠状病毒-2(severe acute respiratory syndrome coronavirus-2,SARS-CoV-2)感染诱导动脉粥样硬化（atherosclerosis,AS）进展的核心靶点及重要通路,进而预测潜在防治中药。方法 从基因表达数据库（Gene Expression Omnibus,GEO）中获取新型冠状病毒肺炎患者和动脉粥样硬化患者芯片数据,利用“limmar”包及“Venn”包筛选2种疾病的共同差异表达基因（differentially expressed genes,DEGs）,对共同DEGs进行基因本体论（gene ontology,GO）和京都基因与基因组百科全书（Kyoto encyclopedia of genes and genomes,KEGG）分析,注释其功能及重要通路。对2组基因集进行免疫细胞和免疫功能打分,评估免疫细胞浸润水平。利用STRING数据库,构建蛋白-蛋白互作（protein-protein interaction,PPI）网络;通过Cytoscape的CytoH...     

基于网络药理学的三仙汤治疗骨质疏松症作用机制研究

目的通过网络药理学方法探讨三仙汤干预骨质疏松症(OP)的有效成分、作用靶点及信号通路等,从而明确其作用机制。方法应用中药系统药理学数据库与分析平台(TCMSP)检索获得三仙汤中3味药物的化学成分,通过Uniprot数据库,将已筛选出的靶点名称进行标准化。通过Genecards、OMIM数据库检索骨质疏松症的相关靶基因。将药物靶点与疾病靶点取交集,筛选得到疾病-药物成分共同靶蛋白在String网站构建蛋白-蛋白相互作用(PPI)网络模型。在Cytoscape软件中绘制药物、疾病、靶基因网络图,并进行基因GO功能和KEGG通路富集分析,探讨三仙汤治疗骨质疏松症的作用机制。结果通过数据库筛选得到三仙汤的28种有效成分以及与骨质疏松症相关的靶点127个,其中蛋白激酶、白介素6、血管内皮生长因子等基因为PPI网络中的核心基因; GO富集分析显示靶点主要影响核受体活性、转录因子活性、类固醇激素受体活性、细胞因子受体结合、蛋白质异二聚活性、血红素结合等生物过程;以及影响AGE-RAGE信号通路、流体剪切应力与动脉粥样硬化通路、IL-17信号通路、前列腺癌通路、肿瘤坏死因子信号通路、乙型肝炎等信号通路。结论三仙汤中的有效成分通过关键信号通路作用于核心基因,影响细胞分化、凋亡、炎症、氧化应激等多种生物学过程发挥抗骨质疏松的作用,为其后续研究提供了科学依据。     

基于Bevilacqua模型的乳腺癌保乳术患者术后淋巴水肿风险预测的临床研究

目的验证Bevilacqua乳腺癌术后淋巴水肿风险预测模型的临床适用性及可行性。方法回顾性分析2010年1月至2015年12月203例乳腺癌患者临床资料,临床数据分析使用统计学软件SPSS 24.0。Cox回归模型分析乳腺癌患者术后发生上肢淋巴水肿的危险因素,以P<0.05为有统计学意义;绘制ROC曲线,以曲线下面积检验模型预测效果;应用Hosmere-Lemeshow检验评估预测值与实际值的校准程度,以P>0.05为预测模型校准能力较好,预测与实际没有区别。结果所有患者随访共计62~86个月,中位随访时间70个月。术后5年内共发生上肢淋巴水肿患者45例(22.2%)。Cox回归模型分析结果显示,高身体质量指数(BMI)、接受过新辅助化疗、全腋窝淋巴结清扫、接受过放疗是上肢淋巴水肿的独立危险因素。Becilacqua上肢淋巴水肿风险预测模型ROC曲线分析结果显示,模型AUC值为0.711,95%CI(0.651~0.760),有较好的的预测效果。Hosmer-Lemeshow检验结果显示,风险预测模型预测风险与实际无明显差异(P=0.262),校准能力较好,与实际差别不大。结论Bevilacqua术后6个月淋巴水肿风险预测模型的准确性及适用性较高,可用于临床对乳腺癌保乳术后淋巴水肿的预测,可为预防淋巴水肿的发生制定干预决策提供参考。     

Derivation and validation of a novel comorbidity‐based delirium risk index to predict postoperative delirium using national administrative healthcare database

ObjectiveTo derive and validate a comorbidity‐based delirium risk index (DRI) to predict postoperative delirium.Data Source/Study SettingData of 506 438 hip fracture repair surgeries from 2006 to 2016 were collected to derive DRI and perform internal validation from the Premier Healthcare Database, which provided billing information on 20‐25 percent of hospitalizations in the USA. Additionally, data of 1 130 569 knee arthroplasty surgeries were retrieved for external validation.Study DesignThirty‐six commonly seen comorbidities were evaluated by logistic regression with the outcome of postoperative delirium. The hip fracture repair surgery cohort was separated into a training dataset (60 percent) and an internal validation (40 percent) dataset. The least absolute shrinkage and selection operator (LASSO) procedure was applied for variable selection, and weights were assigned to selected comorbidities to quantify corresponding risks. The newly developed DRI was then compared to the Charlson‐Deyo Index for goodness‐of‐fit and predictive ability, using the Akaike information criterion (AIC), Bayesian information criterion (BIC), area under the ROC curve (AUC) for goodness‐of‐fit, and odds ratios for predictive performance. Additional internal validation was performed by splitting the data by four regions and in 4 randomly selected hospitals. External validation was conducted in patients with knee arthroplasty surgeries.Data CollectionHip fracture repair surgeries, knee arthroplasty surgeries, and comorbidities were identified by using ICD‐9 codes. Postoperative delirium was defined by using ICD‐9 codes and analyzing billing information for antipsychotics (specifically haloperidol, olanzapine, and quetiapine) typically recommended to treat delirium.Principal FindingsThe derived DRI includes 14 comorbidities and assigns comorbidities weights ranging from 1 to 6. The DRI outperformed the Charlson‐Deyo Comorbidity Index with better goodness‐of‐fit and predictive performance.ConclusionsDelirium risk index is a valid comorbidity index for covariate adjustment and risk prediction in the context of postoperative delirium. Future work is needed to test its performance in different patient populations and varying definitions of delirium.     

Analysis of time‐to‐event for observational studies: Guidance to the use of intensity models

This paper provides guidance for researchers with some mathematical background on the conduct of time‐to‐event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time‐dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.