中药复方配伍的研究方法及其进展

中药复方是由2味或2味以上中药遵循中医理论组合而成的方剂。多味中药在合适的剂量配比之下,协同发挥作用,实现中医的整体调节治疗。研究中药复方的配伍对推动中药现代化发展、新药开发以及临床应用有着重要意义。近年来,研究者们在传统的"七情和合"与"君臣佐使"的基础上,运用新技术和新方法对中药复方的成分、药效活性和药代动力学性质等进行了研究,从不同角度探讨了中药复方配伍的科学内涵。同时,多种数理方法和模型的建立、网络药理学和数据挖掘方法的发展与应用,也对中药复方配伍研究提供了很大帮助。研究方法的发展虽促进了中药复方配伍的科学研究,但还需进一步建立适合中药复方配伍复杂关系的研究方法,以阐明中药复方及其成分/组分配伍的内在规律,进而构建新的现代中药复方,这也是目前中药复方配伍研究的重点任务。     

辨体-辨病-辨证诊疗模式在慢性病防治中的应用策略

依据中国防治慢性病中长期规划（2017-2025年），按照国医大师王琦教授倡导的"辨体-辨病-辨证诊疗模式"，提出慢性病"3+4+3"防治路向：面向三类人群（一般、高危、患病人群），朝向四种状态（无病、病前、病中、病后状态），指向三辨模式（辨体-辨病-辨证诊疗模式）。进而探讨其应用策略：对于一般人群无病状态——辨体养生，固本防病；对于高危人群病前状态——辨体干预，治本救萌；针对患病人群病中状态——"三辨"施治，标本兼顾；针对患病人群病后状态——辨体调理，固本防复。文中结合案例加以佐证，具有临床指导价值。     

数据挖掘法探讨周珉教授治疗原发性肝癌用药规律及经验总结

[目的]数据挖掘法分析周珉教授治疗原发性肝癌的中医用药规律，探讨相关病机并进行经验总结。[方法]收集2016年2月—2018年5月周珉教授门诊期间治疗原发性肝癌的方剂，运用“中医传承辅助系统（V2.50）”进行数据挖掘，并结合周珉教授临床经验，进行原发性肝癌病机探讨及用药规律分析。[结果]共收集治疗原发性肝癌方剂176首，涉及中药235种，列出方剂中的高频药物及组合规律。[结论]总结原发性肝癌以“湿热痰毒、气阴两伤”为基础病机，“清热化湿、健脾养阴”治则贯穿原发性肝癌治疗始末，同时，根据疾病不同发展阶段及治疗措施，权衡“扶正”与“祛邪”的主次分配，斟酌应用“攻毒散结”“行气化瘀”等治法，以达到改善患者生活质量、延缓疾病进展的目的。     

Emergency department patient payer status after implementation of the Affordable Care Act: A nationwide analysis using NHAMCS data

ObjectiveTo evaluate changes in insurance status among emergency department (ED) patients presenting in the two years immediately before and after full implementation of the Affordable Care Act (ACA).MethodsWe evaluated National Hospital Ambulatory Medical Care Survey (NHAMCS) Emergency Department public use data for 2012–2015, categorizing patients as having any insurance (private; Medicare; Medicaid; workers' compensation) or no insurance. We compared the pre- and post-ACA frequency of insurance coverage—overall and within the older (≥65), working-age (18–64) and pediatric (<18) subpopulations—using unadjusted odds ratios with 95% confidence intervals. We also conducted a difference-in-differences analysis comparing the change in insurance coverage among working-age patients with that observed for older Medicare-eligible patients, while controlling for sex, race and underlying temporal trends.ResultsOverall, the proportion of ED patients with any insurance did not significantly change from 2012 to 2013 to 2014–2015 (74.2% vs 77.7%) but the proportion of working-age adult patients with at least one form of insurance increased significantly, from 66.0% to 71.8% (OR 1.31, CI: 1.13–1.52). The difference-in-differences analysis confirmed the change in insurance coverage among working-age adults was greater than that seen in the reference population of Medicare-eligible adults (AOR 1.70, CI: 1.29–2.23). The increase was almost entirely attributable to increased Medicaid coverage.ConclusionIn the first two years following full implementation of the ACA, there was a significant increase in the proportion of working-age adult ED patients who had at least one form of health insurance. The increase appeared primarily associated with expansion of Medicaid.     

An Introduction to the Residency Application Process

Emergency medicine (EM) is a swiftly developing yet still relatively young discipline. We are going to present in the Medical Student Forum section of the Journal of Emergency Medicine several article series covering the key topics that medical students interested in emergency medicine will find helpful. This article introduces the topics that will be tackled in the first compilation of articles dealing with the residency application process.     

Composite interaction tree for simultaneous learning of optimal individualized treatment rules and subgroups

Treatment response heterogeneity has long been observed in patients affected by chronic diseases. Administering an individualized treatment rule (ITR) offers an opportunity to tailor treatment strategies according to patient-specific characteristics. Overly complex machine learning methods for estimating ITRs may produce treatment rules that have higher benefit but lack transparency and interpretability. In clinical practices, it is desirable to derive a simple and interpretable ITR while maintaining certain optimality that leads to improved benefit in subgroups of patients, if not on the overall sample. In this work, we propose a tree-based robust learning method to estimate optimal piecewise linear ITRs and identify subgroups of patients with a large benefit. We achieve these goals by simultaneously identifying qualitative and quantitative interactions through a tree model, referred to as the composite interaction tree (CITree). We show that it has improved performance compared to existing methods on both overall sample and subgroups via extensive simulation studies. Lastly, we fit CITree to Research Evaluating the Value of Augmenting Medication with Psychotherapy trial for treating patients with major depressive disorders, where we identified both qualitative and quantitative interactions and subgroups of patients with a large benefit.     

Why We Need Continuous Pharmaceutical Manufacturing and How to Make It Happen

We make the case for why continuous pharmaceutical manufacturing is essential, what the barriers are, and how to overcome them. To overcome them, government action is needed in terms of tax incentives or regulatory incentives that affect time.