中医舌诊术语英译国际标准探究＊

本文通过对《中医基本名词术语中英对照国际标准》和《WHO西太平洋地区传统医学名词术语国际标准》舌诊术语进行比较，分析两部标准中舌诊术语英译的优缺点，提出更适宜优先选择作为中医舌诊术语英译标准的方案，以期为中医名词术语标准化工作提供参考。     

中文SCI科技期刊现状与影响力提升建议

【目的】 研究我国中文SCI科技期刊的现状,以期为其影响力提升和未来发展提供思考。【方法】 利用网站调研(期刊官网和Web of Science数据库等)、微信公众号运营情况查询及统计分析等方法对中文SCI科技期刊进行较全面的数据采集与分析。【结果】 18种中文SCI科技期刊有深厚的历史积淀和良好的发展基础,但整体上仍存在一些问题,如国内影响力不如优秀的纯中文期刊(国内影响力排名不够靠前),国际影响力不高(JCR分区仅为Q3或Q4区),国际化发展程度不够(国际编委占比为0的期刊高达11种,缺乏国际新媒体宣传),不够重视网络首发(仅9种期刊有网络首发)等。【结论】 在目前国内良好政策环境下,我国中文SCI科技期刊需要坚守初心,可根据各自期刊定位制定个性化发展策略,从而发展成为旗舰期刊甚至是世界一流期刊。     

Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy

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Bench to bedside: recent advances in lung cancer pathological research with implications for diagnostic clinical practice

After decades of relative stagnation lung cancer is emerging as a disease type where rapid progress is being made in diagnosis and therapy, as well as in our understanding of disease biology. Much of this progress is of immediate impact to diagnosticians, and more is likely to affect diagnostic practice in the near future. In this review we seek to briefly summarize several key areas of active research of immediate or probable imminent value to trainee and consultant pulmonary pathologists alike. We cover some major changes in tumour classification, grading, and patient stratification, as well as considering the state of the art in machine-assisted interpretation of lung cancer histology, and the use of genetically modified lung cancer models.     

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study

IntroductionPredicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.Materials and methodsWe retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.ResultsThe WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).ConclusionOur study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.     

Coupling online control and inhibitory systems in children with Developmental Coordination Disorder: Goal-directed reaching

For children with Developmental Coordination Disorder (DCD), the real-time coupling between frontal executive function and online motor control has not been explored despite reported deficits in each domain. The aim of the present study was to investigate how children with DCD enlist online control under task constraints that compel the need for inhibitory control. A total of 129 school children were sampled from mainstream primary schools. Forty-two children who met research criteria for DCD were compared with 87 typically developing controls on a modified double-jump reaching task. Children within each skill group were divided into three age bands: younger (6–7 years), mid-aged (8–9), and older (10–12). Online control was compared between groups as a function of trial type (non-jump, jump, anti-jump). Overall, results showed that while movement times were similar between skill groups under simple task constraints (non-jump), on perturbation (or jump) trials the DCD group were significantly slower than controls and corrected trajectories later. Critically, the DCD group was further disadvantaged by anti-jump trials where inhibitory control was required; however, this effect reduced with age. While coupling online control and executive systems is not well developed in younger and mid-aged children, there is evidence of age-appropriate coupling in older children. Longitudinal data are needed to clarify this intriguing finding. The theoretical and applied implications of these results are discussed.     

Training in Informatics: Teaching Informatics in Surgical Pathology

This article presents an overview of the curriculum deemed essential for trainees in pathology, with mapping to the Milestones competency statements. The means by which these competencies desired for pathology graduates, and ultimately practitioners, can best be achieved is discussed. The value of case (problem)-based learning in this realm, in particular the kind of integrative experience associated with hands-on projects, to both cement knowledge gained in the lecture hall or online and to expand competency is emphasized.