Selecting those to refer for joint replacement: Who will likely benefit and who will not?

Osteoarthritis (OA) is one of the 10 most disabling diseases in developed countries and worldwide estimates are that 10% of men and 18% of women aged over 60 years have symptomatic OA, including moderate and severe forms. Total joint replacement (TJR) is considered the most effective treatment for end-stage OA in those who have exhausted available conservative interventions. The demand for TJR is continually rising due to the ageing population; in the United States, more than 1 million TJRs were performed in 2010 and the number of procedures is projected to exceed 4 million in the US by 2030. It has been estimated that of all hip and knee replacements performed, approximately one quarter of the patients may be considered inappropriate candidates. Predicting who will benefit from TJR and who will not would seem critical in terms of containing the current and projected expenditure as well as improving satisfaction in TJR recipients. Few formal predictive tools are available to aid referring clinicians to determine those likely to be good or poor responders to surgery and current available tools tend to focus on disease severity alone with little consideration of risk factors that may predict a poor outcome or impede an effective response to surgery. This review examines the tools available to assist with assessing appropriateness for TJR; investigates the modifiable risk factors associated with poor outcome; and identifies areas for future research in selecting those appropriate for joint replacement.     

Predicting the Printability in Selective Laser Melting with a Supervised Machine Learning Method

Though selective laser melting (SLM) has a rapidly increasing market these years, the quality of the SLM-fabricated part is extremely dependent on the process parameters. However, the current metallographic examination method to find the parameter window is time-consuming and involves subjective assessments of the experimenters. Here, we proposed a supervised machine learning (ML) method to detect the track defect and predict the printability of material in SLM intelligently. The printed tracks were classified into five types based on the measured surface morphologies and characteristics. The classification results were used as the target output of the ML model. Four indicators had been calculated to evaluate the quality of the tracks quantitatively, serving as input variables of the model. The data-driven model can determine the defect-free process parameter combination, which significantly improves the efficiency in searching the process parameter window and has great potential for the application in the unmanned factory in the future.     

Majority Vote and Other Problems when using Computational Tools

Computational tools are essential for most of our research. To use these tools, one needs to know how they work. Problems in application of computational methods to variation analysis can appear at several stages and affect, for example, the interpretation of results. Such cases are discussed along with suggestions how to avoid them. The applications include incomplete reporting of methods, especially about the use of prediction tools; method selection on unscientific grounds and without consulting independent method performance assessments; extending application area of methods outside their intended purpose; use of the same data several times for obtaining majority vote; and filtering of datasets so that variants of interest are excluded. All these issues can be avoided by discontinuing the use software tools as black boxes.     

Homology modeling in drug discovery: Overview,current applications,and future perspectives

Homology modeling is one of the computational structure prediction methods that are used to determine protein 3D structure from its amino acid sequence. It is considered to be the most accurate of the computational structure prediction methods. It consists of multiple steps that are straightforward and easy to apply. There are many tools and servers that are used for homology modeling. There is no single modeling program or server which is superior in every aspect to others. Since the functionality of the model depends on the quality of the generated protein 3D structure, maximizing the quality of homology modeling is crucial. Homology modeling has many applications in the drug discovery process. Since drugs interact with receptors that consist mainly of proteins, protein 3D structure determination, and thus homology modeling is important in drug discovery. Accordingly, there has been the clarification of protein interactions using 3D structures of proteins that are built with homology modeling. This contributes to the identification of novel drug candidates. Homology modeling plays an important role in making drug discovery faster, easier, cheaper, and more practical. As new modeling methods and combinations are introduced, the scope of its applications widens.     

Coronary Artery Calcification and Family History of Myocardial Infarction in the Dallas Heart Study

ObjectivesThis study aimed to investigate the independent and joint associations between family history of myocardial infarction (FH) and coronary artery calcification (CAC) with incident coronary heart disease (CHD).BackgroundFH and CAC are associated with each other and with incident CHD. It is not known whether FH retains its predictive value after CAC results are accounted for.MethodsAmong 2,390 participants without cardiovascular disease enrolled in the Dallas Heart Study, we assessed FH (myocardial infarction in a first-degree relative) and prevalent CAC by electron-beam computed tomography. The primary outcome, a composite of CHD-related death, myocardial infarction, and percutaneous or surgical coronary revascularization, was assessed over a mean follow-up of 8.0 ± 1.2 years. The individual and joint associations with the CHD composite outcome were determined for FH and CAC.ResultsThe mean age of the population was 44 ± 9 years; 32% had FH and 47% had a CAC score of 0. In multivariate models adjusted for traditional risk factors, FH was independently associated with CHD (adjusted hazard ratio: 2.6; 95% confidence interval: 1.6 to 4.2; p < 0.001). Further adjustment for prevalent CAC did not diminish this association (adjusted hazard ratio: 2.6; 95% confidence interval: 1.6 to 4.2; p < 0.001). FH and CAC were additive: CHD event rates in those with both FH and CAC were 8.8% vs. 3.3% in those with prevalent CAC alone (p < 0.001). CHD rates were 1.9% in those with FH alone compared with 0.4% in those with neither FH nor CAC (p < 0.017). Among subjects without CAC, FH characterized a group with a more unfavorable cardiometabolic profile.ConclusionsFH provided prognostic information that was independent of and additive to CAC. Among those with CAC, FH identified subjects at particularly high short-term risk, and, among those without it, selected a group with an adverse risk-factor profile.     

慢性乙型病毒性肝炎患者合并抑郁的COX风险预测模型

背景　慢性乙型病毒性肝炎是我国流行最广、危害最重的传染病,抑郁是其常见并发症,轻者情绪低落,重者不仅可加重固有疾病、甚至可出现轻生行为,目前常用治疗措施疗效不理想,积极预防护理成为医护人员关注的重点。目的　构建慢性乙型病毒性肝炎患者合并抑郁的COX风险预测模型。方法　选择2017年6月-2018年5月川北医学院附属医院收治的慢性乙型病毒性肝炎患者245例为研究对象,随访慢性乙型病毒性肝炎患者合并抑郁的情况。完成数据预处理后,所有因素均进入单因素及多元COX风险因素分析,并构建风险预测模型,采用列线图展示预测模型,受试者工作特征（ROC）曲线评价模型区分度,采用calibration plot曲线评价模型准确度,采用临床决策曲线（DCA）评价模型的有效性。结果　单因素分析结果显示不同年龄、职业、学历、乙肝分度、感染时间、确诊时间、复发次数、家庭地位、婚姻满意度、担心疾病难以根治、担心住院环境、疾病分期、有无并发症、对治疗是否有信心慢性乙型病毒性肝炎患者的抑郁发生率比较,差异有统计学意义（P<0.05）。多元COX风险因素分析结果显示：RR=-1.446 1×（职业为知识分子）-0.688 7×（学历高中或中专）-2.043 0×（经常饮酒）-0.783 5×（偶尔吸烟）-1.068 2×（经常吸烟）-0.894 0×（确诊时间0.5~5年）-1.092 4×（确诊时间<0.5年）+1.335 2×（家庭地位不满意）+1.345 1×（婚姻不满意）-0.574 3×（不担心住院环境不适宜）。本研究构建的COX风险预测模型的ROC曲线下面积（AUC）为0.979 8,模型预测特异度0.972 5,灵敏度0.940 7,准确度为0.954 9,阳性似然比为34.180 2,阴性似然比为0.060 9,诊断价值比为560.916 7,阳性预测值为0.976 9,阴性预测值为0.929 8,模型的区分度较高。在模型准确度评价上：当事件发生率在16%以下时,模型高估风险;当事件发生率在16%~40%时,模型低估风险;当事件发生率在40%~80%时,模型高估风险;当事件发生率在80%~100%时,模型低估风险;而在16%、40%、80%时候,模型预测和观察值完全一致,整体上看本模型构建的准确度较好。临床决策曲线显示模型的净获益（NB）值较高,提示基于本模型预测结果开展临床决策产生的效果能给患者病情带来较好的获益值。结论　本次构建的慢性乙型病毒性肝炎合并抑郁的风险预测模型可用于预测新诊断慢性乙型病毒性肝炎合并抑郁的风险,从而指导临床医护人员进行针对性的干预措施,最终避免或降低患者合并抑郁的可能性,值得临床推广应用。