基于灰度共生矩阵和人工神经网络的肺癌CT图像的分类研究

目的研究小细胞肺癌（SCLC）和非小细胞肺癌（NSCLC）的分类问题。方法217例肺癌患者．其中男性165例．殳性52例;年龄35～80岁,平均年龄61．5岁。其中SCLC108例,NSCLC109例。提取患者764幅肺癌CT图像的灰度共生矩阵,选取对比度、熵、能量和逆差矩4个特征值,借助临床确诊结果,利用多层前向（BP）、径向基函数（RBF）人工神经网络对特征进行训练测试。结果BP人工神经网络对10％的78例样本进行测试,SCLC42例预测正确．NSCLC33例预测正确．3例预测失败。RBF神经网络对10％的78例测试样本进行测试,SCLC42例预测正确．NSCLC36例预测正确、类似方法对样本总数的70％进行训练,用30％的230例进行测试;BP人工神经网络有209例预测正确。正确率为90．9％：其中SCLC111例预测正确,正确检出率为88．8％;NSCLC98例预测正确,正确检出率为93.3％。RBF人工神经网络有216例预测正确．正确率为93．9％,其中SCLC117例预测正确,正确率为93．6％;NSCLC99例预测正确,止确检出率为94.3％。可见BP、RBF人1二神经网络对SCLC和NSCLC均具有90％以上的正确率,高于人工诊断结果。结论基于灰度共生矩阵的对比度、熵、能量和逆差矩4个特征值能反映SCLC和NSCLC的有效特征参量．通过人工神经网络能达到分类目的,辅助临床治疗。     

“金课”理念下的线上线下混合式教学对科研护士的培养

目的 探讨在“金课”理念下的线上线下混合式教学对科研护士培养的探索和实践效果。方法 选取长沙市某三级综合医院36名有意向申请2021年科研护士岗位的临床护士为研究对象,在“金课”理念下基于线上线下混合式教学的形式,围绕科研能力及批判性思维能力培养,更加侧重学员的“分析、评价、创造”3个高级认知阶段,分别展开护理科研培训与实践。结果 培训后临床护士科研能力、批判性思维能力得分均高于培训前,差异均有统计学意义（P<0.05）。结论 “金课”理念下的线上线下混合式教学能有效提高临床护士科研能力及批判性思维能力。     

Breaking Down Silos: Mapping Growth of Cross‐Disciplinary Collaboration in a Translational Science Initiative

The importance of transdisciplinary collaboration is growing, though not much is known about how to measure collaboration patterns. The purpose of this paper is to present multiple ways of mapping and evaluating the growth of cross‐disciplinary partnerships over time. Social network analysis was used to examine the impact of a Clinical and Translational Science Award (CTSA) on collaboration patterns. Grant submissions from 2007 through 2010 and publications from 2007 through 2011 of Institute of Clinical and Translational Sciences (ICTS) members were examined. A Cohort Model examining the first‐year ICTS members demonstrated an overall increase in collaborations on grants and publications, as well as an increase in cross‐discipline collaboration as compared to within‐discipline. A Growth Model that included additional members over time demonstrated the same pattern for grant submissions, but a decrease in cross‐discipline collaboration as compared to within‐discipline collaboration for publications. ICTS members generally became more cross‐disciplinary in their collaborations during the CTSA. The exception of publications for the Growth Model may be due to the time lag between funding and publication, as well as pressure for younger scientists to publish in their own fields. Network analysis serves as a valuable tool for evaluating changes in scientific collaboration.     

三阳三阴辨证的理论基础

三阳三阴辨证是《伤寒论》辨证论治的方法与体系,是辨证的纲领与论治的准则。历代医家认为,三阳三阴辨证理论基础源于《周易》《内经》相关理论、中国传统文化,理论基础研究是正确认识事物规律的基石,避开理论基础研究就难以推动事物的发展。因此,正确认识和掌握三阳三阴辨证(后世俗称"六经辨证")的理论基础,具有深远而重大的意义。该文从哲学基础之阴阳五行及医学基础之《内经》《难经》两方面来进行阐述。     

基于nano LC-MS/MS法的黄明胶胶原蛋白鉴定研究

目的 基于shotgun混合蛋白鉴定技术,系统研究黄明胶中胶原类物质组成,探讨其胶原蛋白的修饰。方法 采用胰蛋白酶酶切黄明胶样品,纳升液相串联质谱（nano-LC Q Exactive Orbitrap MS）技术,对黄明胶酶解液进行分析获得MS/MS图谱,利用PEAKS软件对获得的MS/MS图谱搜索牛科蛋白质库,以鉴定其中蛋白质组成。结果 从2个批次的黄明胶酶解液样品中鉴定了1 378个肽段,共鉴定了30蛋白质,胶原I蛋白α1链、α2链,胶原Ⅲ蛋白α1链为黄明胶的主要物质基础,研究发现黄明胶中蛋白主要有4种修饰形式:羟基化（Hydroxylation）、脱酰胺化（Deamidation）、N-端酰胺化（Acetylation）及氧化（Oxidation）修饰。结论 黄明胶的主要物质基础源于胶原Ⅰ蛋白和胶原Ⅲ蛋白,胶原蛋白经熬制后,变性、溶出、修饰获得的产物共同构成了黄明胶的物质基础。      

中医药高校图书馆开展科研支持服务现状分析

通过网络调查法和文献调研法对国内24所中医药高校图书馆科研支持服务开展情况进行调查,分析中医药高校图书馆科研支持服务现状及以及存在的问题,提出具有学科领域特色的科研支持服务建议,以期为我国中医药高校图书馆科研服务发展提供参考。     

肾主生殖的生物学基础

从形态、位置和毗邻的角度来看,中医的肾藏就是生物学之肾脏;从功能上看,肾藏不完全等同于肾脏,但是中医学肾主生殖功能与生物学生殖-内分泌系统相关。因此,可以通过对肾主生殖功能异常症状的生理病理解释,获得肾主生殖的生物学基础。肾藏的生物学基础一是主男性生殖之肾藏:①睾丸、附睾、输精管、射精管、尿道、阴茎、阴囊;②分泌器官:睾丸、附睾、前列腺、精囊、尿道球腺。二是主女性生殖之肾藏:①卵巢、输卵管、子宫、阴道、外阴;②分泌器官:腺垂体、卵巢、黄体、肾上腺、胎盘、乳房。肾精的生物学基础一是主男性生殖之肾精:①具有遗传基因的生殖细胞(精子)及其母细胞;②促性腺激素、雄激素、胰岛素样因子3、降钙素基因相关肽、雄激素结合蛋白;③附属性腺产生的分泌液。二是主女性生殖之肾精:①具有遗传基因的生殖细胞(卵子)及其母细胞;②促性腺激素(促卵泡素和黄体生成素)、雌激素、孕激素、雄激素、催产素、催乳素、前列腺素、人绒毛膜促性腺激素;③卵泡壁溶解酶。     

基于“三早理念”的卓越医生人才培养模式的构建

适应现阶段医学教育培养目标的以器官系统为中心基础医学课程整合教学模式有助于学生早期树立医学整体概念,以培养学生独立分析问题和解决问题的综合能力;基于导师制的早期接触科研,将有助于学生科研能力和创新思维的培养;基于案例教学的早期接触临床,有利于基础与临床的有机结合。因此,基于"三早理念(早期树立医学整体概念、早期接触科研、早期接触临床)"的培养模式对高等医学院校和职业院校卓越医生培养计划的实施产生实践意义。     

Collective credit allocation in science

Collaboration among researchers is an essential component of the modern scientific enterprise, playing a particularly important role in multidisciplinary research. However, we continue to wrestle with allocating credit to the coauthors of publications with multiple authors, because the relative contribution of each author is difficult to determine. At the same time, the scientific community runs an informal field-dependent credit allocation process that assigns credit in a collective fashion to each work. Here we develop a credit allocation algorithm that captures the coauthors’ contribution to a publication as perceived by the scientific community, reproducing the informal collective credit allocation of science. We validate the method by identifying the authors of Nobel-winning papers that are credited for the discovery, independent of their positions in the author list. The method can also compare the relative impact of researchers working in the same field, even if they did not publish together. The ability to accurately measure the relative credit of researchers could affect many aspects of credit allocation in science, potentially impacting hiring, funding, and promotion decisions.Reflecting the increasing complexity of modern research, in the last decades, collaboration among researchers became a standard path to discovery (1). Collaboration plays a particularly important role in multidisciplinary research that requires expertise from different scientific fields (2). As the number of coauthors of each publication increases, science’s credit system is under pressure to evolve (3–5). For single-author papers, which were the norm decades ago, credit allocation is simple: the sole author gets all of the credit. This rule, accepted since the birth of science, fails for multiauthor papers (6). The lack of a robust credit allocation system that can account for the discrepancy between researchers’ contribution to a particular body of work and the credit they obtain, has prompted some to state that “multiple authorship endangers the author credit system” (7). This situation is particularly acute in multidisciplinary research (8, 9), when communities with different credit allocation traditions collaborate (10). Furthermore, a detailed understanding of the rules underlying credit allocation is crucial for an accurate assessment of each researcher’s scientific impact, affecting hiring, funding, and promotion decisions.Current approaches to allocating scientific credit fall in three main categories. The first views each author of a multiauthor publication as the sole author (11, 12), resulting in inflated scientific impact for publications with multiple authors. This system is biased toward researchers with multiple collaborations or large teams, customary in experimental particle physics or genomics. The second assumes that all coauthors contribute equally to a publication, allocating fractional credit evenly among them (13, 14). This approach ignores the fact that authors’ contributions are never equal and hence dilutes the credit of the intellectual leader. The third allocates scientific credit according to the order or the role of coauthors, interpreting a message agreed on within the respective discipline (15–17). For example, in biology, typically the first and the last author(s) get the lion’s share of the credit, and in some areas of physical sciences, the author list reflects a decreasing degree of contribution. An extreme case is offered by experimental particle physics, where the author list is alphabetic, making it impossible to interpret the author contributions without exogenous information. Finally, there is an increasing trend to allocate credit based on the specific contribution of each author (18, 19), specified in the contribution declaration required by some journals (20, 21). However, each of these approaches ignores the most important aspect of credit allocation: notwithstanding the agreed on order, credit allocation is a collective process (22–24), which is determined by the scientific community rather than the coauthors or the order of the authors in a paper. This phenomena is clearly illustrated by the 2012 Nobel prize in physics that was awarded based on discoveries reported in publications whose last authors were the laureates (25, 26), whereas the 2007 Nobel prize in physics was awarded to the third author of a nine-author paper (27) and the first author of a five-author publication (28). Clearly the scientific community operates an informal credit allocation system that may not be obvious to those outside of the particular discipline.The leading hypothesis of this work is that the information about the informal credit allocation within science is encoded in the detailed citation pattern of the respective paper and other papers published by the same authors on the same subject. Indeed, each citing paper expresses its perception of the scientific impact of a paper’s coauthors by citing other contributions by them, conveying implicit information about the perceived contribution of each author. Our goal is to design an algorithm that can capture in a discipline-independent fashion the way this informal collective credit allocation mechanism develops.