药学专业生物化学虚拟实验教学系统的开发与应用

目的:为提高生物化学教学质量提供参考。方法:研发生物化学虚拟实验教学系统,并分析该系统对学生学习兴趣和教学水平的影响。结果:笔者利用网络平台开发了生物化学虚拟实验教学系统,并在教师和学生中进行了试用。教师管理模块实现了教师对实验项目的宏观管理和监控;学生实验模块可实现实验演示及网上实验等功能。结论:教学反馈表明,生物化学虚拟实验教学系统增强并扩展了传统教学模式,以全新的方式将学生、实验仪器及实验项目紧密联系起来,拓宽了学生的知识面,加强了学生之间的相互合作交流,激发了学生的学习热情,提高了学习兴趣,具有助教、助学双重功能。     

中药制药专业虚拟仿真教学平台建设的探索

虚拟仿真实验技术的发展给传统实验教学方式带来了挑战。为提升中药制药专业综合实验教学效果,基于“基础型-专业型-科研教学交互型”的虚拟仿真实验平台建设思路,围绕虚拟仿真实验教学体系、资源建设、教学与研究队伍建设、实验平台管理等方面进行探讨,通过“以虚促实、虚实结合”的虚拟教学平台建设,优化实验教学资源配置,提升实验教学质量,推动实验教学改革和创新。     

适应医药电子商务发展的虚拟电子市场的建设

在电子商务条件下,市场环境发生了变化,出现了虚拟电子市场。笔者结合我国医药电子商务的发展,研究了医药虚拟电子市场的解决方案及其应用,认为我国医药虚拟电子市场已从药品集中招标采购中实现突破,医药虚拟电子市场可分为电子销售场、电子采购场和独立电子市场。     

虚拟仿真考核系统在影像断层解剖学考核中的应用研究

目的 探讨基于医学影像虚拟仿真平台的考核系统(以下简称虚拟仿真考核系统)在影像断层解剖学考核中的应用效果.方法 选取锦州医科大学2015、2016级五年制医学影像专业学生177人作为研究对象,其中2016级作为观察组,2015级作为对照组.观察组采用虚拟仿真考核系统进行考核,对照组采用传统考核方法,比较2组考核成绩,并...     

虚拟实验软件在分析化学实验教学中的优势

分析化学实验是药学及相关专业本科生的必修基础课程之一,实践性很强.该文对分析化学实验教学现状进行了分析,介绍了在实验教学改革中引入虚拟实验软件的优势.该软件可以使学生加深对分析化学实验和理论相关内容的理解,较好地掌握实验操作的技能.     

虚拟数字人赋能网络药店应用框架初探

目的：探讨解决传统网络药店药学服务提供不到位、药店难以掌握消费者真实需求、平台品牌传播力有限等诸多问题的措施。方法：建立基于虚拟数字人的网络药店应用系统框架,对传统网络药店进行升级改造。结果：示范应用表明,虚拟数字人明显提升了消费者对网络药店平台的忠诚度,使消费者懂得更多合理科学用药的知识,平台好评和销售额显著增长。结论：虚拟数字人是元宇宙时代网络药店发展的必然方向,虚拟数字人为网络药店服务的高质量发展提供了一种新的方案。     

Simcyp推出“虚拟实验大鼠”模拟装置

Simcyp公司是英国一家虚拟体药代动力学模型及模拟装置平台的开发商。它已将用于insilico预测药物动力学的“虚拟实验大鼠”投放市场。Simcyp大鼠2008将模拟化合物是如何在体内吸收、分布、代谢和排泄的，以了解在实际情况下药物的动态。     

计算机虚拟急性毒性实验的制作

急性毒性实验是毒理学和医学机能学实验教学内容之一。该实验的内容丰富、要求严格、时间较长、需要动物较多，计算复杂。为此，设计出内容丰富、能提高学生学习兴趣，达到学习与娱乐于一体的教学辅助工具，必将为教学改革增添新的生机和活力。另外，通过该系统的使用，可在实验教材、实验动物与师资力量等方面得到大量的节省，降低教学成本。     

虚拟仿真技术用于中药调剂学实训教学实践

目的 提高中药调剂学专业实训教学水平.方法 分析传统实训教学中的问题与虚拟仿真实训的优势与特点,以教师自主开发的中药调剂虚拟仿真实训平台为例,介绍其在中药调剂学专业实训教学中的实践应用,采用问卷调查法分析学生对该平台和教学模式的总体评价和建议.结果 虚拟仿真实训成绩,506名学生中仅28名低于80分;2019级各药学专...     

虚拟实验教学系统在中等职业医学教育的应用

医学实验是中等职业医学教育的一个重要组成部分,是培养实用型、复合型人才的重要方法之一。传统的实验教学已经不能满足新形势下的教学要求,从而面临各种各样的问题。虚拟实验教学系统是运用虚拟现实技术模拟真实实验情境软件。用仿真软件组建的虚拟实验室,在实践教学中有益于开拓实验路径,增加学生动手实践机会,是传统实验教学的重要变革。     

TS‐Chemscore,a Target‐Specific Scoring Function,Significantly Improves the Performance of Scoring in Virtual Screening

Most of the scoring functions currently used in structure‐based drug design belong to ‘universal’ scoring functions, which often give a poor correlation between the calculated scores and experimental binding affinities. In this investigation, we proposed a simple strategy to construct target‐specific scoring functions based on known ‘universal’ scoring functions. This strategy was applied to Chemscore, a widely used empirical scoring function, which led to a new scoring function, termed TS‐Chemscore. TS‐Chemscore was validated on 14 protein targets, which cover a wide range of biological target categories. The results showed that TS‐Chemscore significantly improved the correlation between the calculated scores and experimental binding affinities compared with the original Chemscore. TS‐Chemscore was then applied in virtual screening to retrieve novel JAK3 and YopH inhibitors. Top 30 compounds for each target were selected for experimental validation. Six active compounds for JAK3 and four for YopH were obtained. These compounds were out of the lists of top 30 compounds sorted by Chemscore. Collectively, TS‐Chemscore established in this study showed a better performance in virtual screening than its counterpart Chemscore.     

Incorporation of stochastic engineering models as prior information in Bayesian medical device trials

Evaluation of medical devices via clinical trial is often a necessary step in the process of bringing a new product to market. In recent years, device manufacturers are increasingly using stochastic engineering models during the product development process. These models have the capability to simulate virtual patient outcomes. This article presents a novel method based on the power prior for augmenting a clinical trial using virtual patient data. To properly inform clinical evaluation, the virtual patient model must simulate the clinical outcome of interest, incorporating patient variability, as well as the uncertainty in the engineering model and in its input parameters. The number of virtual patients is controlled by a discount function which uses the similarity between modeled and observed data. This method is illustrated by a case study of cardiac lead fracture. Different discount functions are used to cover a wide range of scenarios in which the type I error rates and power vary for the same number of enrolled patients. Incorporation of engineering models as prior knowledge in a Bayesian clinical trial design can provide benefits of decreased sample size and trial length while still controlling type I error rate and power.     

虚拟网络环境的医学应用

简要论述了因特网上虚拟环境的医学应用及其意义。其中包括虚拟医院、虚拟图书馆、虚拟医学会议、虚拟生命、网上实验室和网络论坛等。     

电子药历的开发及在合理用药中的作用

目的开发普及型电子药历软件,促进合理用药。方法以编程软件开发,通过实际病例操作观察其在合理用药中的作用结果成功开发了符合要求的电子药历,在通用性、弥补临床药师知识面的不足、作为系统性研究平台、协助医师合理用药、信息共享方面有较大优势。结论作为一种能被广泛普及的电子药历,将促进已开展临床药学工作而信息化水平不高的医院临床药学的发展,值得推广应用。     

Use of Virtual Patients in an Advanced Therapeutics Pharmacy Course to Promote Active,Patient-Centered Learning

Objective. To assess student satisfaction and learning of course objectives following the integration of virtual patient cases designed to promote active, patient-centered learning in an advanced therapeutics pharmacy course.Design. A dynamic virtual patient platform that incorporated a branched-narrative, decision-making teaching model was used in an advanced therapeutics course to supplement lecture content.Assessment. Presimulation and postsimulation tests were used to assess student learning. The use of virtual patients significantly enhanced student learning for both higher- and lower-level test questions (p<0.001 and p=0.01, respectively). Students agreed or strongly agreed that the virtual patient cases provided an effective way to learn (72%), were enjoyable (69%), and were appropriate in content (80%), and that more should be incorporated (59%).Conclusion. The use of virtual patients in an advanced therapeutics practicum effectively promoted active, patient-centered learning; engaged students in an interactive and dynamic educational technology; encouraged teamwork; enhanced higher-level student learning; and improved student satisfaction in the course.     

Virtual screening of the estrogen receptor

Background: For > 30 years, the estrogen receptor (ER) has been the most important biomarker in breast cancer, principally because of its role in indicating the potential of patients to benefit from endocrine therapy. The search for modulators of ER (selective estrogen receptor modulators) through the use of computational methods such as virtual screening (VS) has redefined the area. Objective: We demonstrate how this receptor has become a key target in the computational (docking and scoring, pharmacophore) arena for algorithm development and validation. The use of quantitative structure–activity relationship for estimation of binding affinity to ER is also discussed, and finally all examples of lead identification through VS are exemplified using several VS campaigns carried out to identify environmental endocrine disruptors. Method: This review comprehensively details all current applications of virtual screening to the estrogen receptor and demonstrates how its use has pushed the boundaries of VS in general. Conclusion: The widespread application of the estrogen receptor to VS has allowed identification of numerous pitfalls within the process flow of VS such as library generation, correct validation procedures for docking/scoring functions, and inclusion of receptor flexibility.     

Evaluating the Predictivity of Virtual Screening for Abl Kinase Inhibitors to Hinder Drug Resistance

Virtual screening methods are now widely used in early stages of drug discovery, aiming to rank potential inhibitors. However, any practical ligand set (of active or inactive compounds) chosen for deriving new virtual screening approaches cannot fully represent all relevant chemical space for potential new compounds. In this study, we have taken a retrospective approach to evaluate virtual screening methods for the leukemia target kinase ABL1 and its drug‐resistant mutant ABL1‐T315I. ‘Dual active’ inhibitors against both targets were grouped together with inactive ligands chosen from different decoy sets and tested with virtual screening approaches with and without explicit use of target structures (docking). We show how various scoring functions and choice of inactive ligand sets influence overall and early enrichment of the libraries. Although ligand‐based methods, for example principal component analyses of chemical properties, can distinguish some decoy sets from active compounds, the addition of target structural information via docking improves enrichment, and explicit consideration of multiple target conformations (i.e. types I and II) achieves best enrichment of active versus inactive ligands, even without assuming knowledge of the binding mode. We believe that this study can be extended to other therapeutically important kinases in prospective virtual screening studies.