Longistylin A,a natural stilbene isolated from the leaves of Cajanus cajan,exhibits significant anti-MRSA activity

Longistylin A (LLA) is an abundant stilbene isolated from the leaves of Cajanus cajan (L.) Millsp. However, the antibacterial effect of LLA is not yet understood. Therefore, in this study, a detailed investigation of the antibacterial effect of LLA, particularly against methicillin-resistant Staphylococcus aureus (MRSA), was conducted. In vitro, LLA exhibited strong antibacterial activity against MRSA with a minimum inhibitory concentration (MIC) of 1.56 µg/mL and displayed much more rapid bactericidal activity (3-log decrease in MRSA survival within 8 h) than vancomycin. A membrane-targeting experiment suggested that the antibacterial activity of LLA is associated with perturbation of the bacterial membrane potential and increased membrane permeability. Notably, LLA had relatively weak cytotoxicity to murine macrophages [50% cytotoxic concentration (CC₅₀) = 8.61 ± 0.57 µg/mL]. In vivo, topical treatment of a skin injury with LLA improved wound healing and closure in an MRSA-infected wound healing mouse model. After 3 days treatment, LLA decreased MRSA bacterial counts in the wounded region, reduced the accumulation of immune cells at the injury site, and alleviated induction of the inflammatory cytokines tumour necrosis factor-alpha (180.74 ± 10.78 pg/mL vs. 606.57 ± 68.99 pg/mL) and interleukin-6 (87.25 ± 10.19 pg/mL vs. 280.58 ± 42.27 pg/mL) in serum.     

SeqSeg: A sequential method to achieve nasopharyngeal carcinoma segmentation free from background dominance

Reliable nasopharyngeal carcinoma (NPC) segmentation plays an important role in radiotherapy planning. However, recent deep learning methods fail to achieve satisfactory NPC segmentation in magnetic resonance (MR) images, since NPC is infiltrative and typically has a small or even tiny volume with indistinguishable border, making it indiscernible from tightly connected surrounding tissues from immense and complex backgrounds. To address such background dominance problems, this paper proposes a sequential method (SeqSeg) to achieve accurate NPC segmentation. Specifically, the proposed SeqSeg is devoted to solving the problem at two scales: the instance level and feature level. At the instance level, SeqSeg is forced to focus attention on the tumor and its surrounding tissue through the deep Q-learning (DQL)-based NPC detection model by prelocating the tumor and reducing the scale of the segmentation background. Next, at the feature level, SeqSeg uses high-level semantic features in deeper layers to guide feature learning in shallower layers, thus directing the channel-wise and region-wise attention to mine tumor-related features to perform accurate segmentation. The performance of our proposed method is evaluated by extensive experiments on the large NPC dataset containing 1101 patients. The experimental results demonstrated that the proposed SeqSeg not only outperforms several state-of-the-art methods but also achieves better performance in multi-device and multi-center datasets.     

REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (https://refuge.grand-challenge.org), held in conjunction with MICCAI 2018. The challenge consisted of two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.     

师承教育在医教协同创新中医人才培养中的作用

中医师承教育是当今社会中医传承的主要方式,其培养了大量的中医优秀人才,促进中医药文化和中医学术思想传播,是现代中医药发展不可缺少的组成部分。但近代以来,人们脑海中逐渐形成了以西方医学为体系的思维模式时,中医学教育该如何的发展与传承。针对这一现象,我们分析了现代院校教育与师承教育的特点,阐述师承教育在现代中医人才培养中的作用,以期探讨高等中医药院校教育改革的模式。     

肾虚证动物模型研究进展

肾虚证动物模型一直是中医证候研究的重点,提供与人类疾病类似的肾虚证动物模型,有助于深入研究肾虚证本质及其治疗。近年来建立了不少肾虚证动物模型,然而不同的肾虚证模型特点也不尽相同。对临床常见肾虚证型,从肾阳虚、肾阴虚、肾精不足、肾气虚方面进行概述,并对其存在的不足进行思考,以期为肾虚证动物模型的构建及其证候生物学机制研究提供新思路。     

阳春砂小花假合蕊柱的形成过程

目的:研究阳春砂小花假合蕊柱的形成过程。方法:将阳春砂小花自0. 5 cm长度至开花后一天划分为8个生长时期,对小花鲜样解剖并制作石蜡切片,测定花药腔的高度,花粉囊裂口夹角,花粉囊缝隙宽度,花柱直径,花丝与唇瓣的夹角(α),花丝与花药的夹角(β)。结果:花粉囊夹角在开花前无明显变化,开花时从32°缩小为17°。两对花粉囊间的缝隙宽度在第5时期增大至约0. 29 mm,同时期的花柱直径约0. 32 mm,两者比例达92%。相比开花前一天,开花时α角从83°减小为42°,β角从186°减小为147°。花丝的远轴侧比近轴侧多1至5层细胞,花柱的近轴侧比远轴侧多1至6层细胞。结论:远、近轴侧细胞结构的不对称性是花丝、花柱运动的基础。在第5时期,花粉囊间的缝隙增大至几乎与花柱等径,花柱运动嵌入花粉囊间。开花时花粉囊夹角缩小,将花粉囊间隙出口封闭,花柱留在花粉囊间,同时角α,β剧烈减小,雄蕊将雌蕊夹在其中向唇瓣弯曲,最终形成半包于唇瓣内的假合蕊柱结构。     

体医融合专门人才培养现状与优化路径

目前我国的体医融合已探索出以医院为平台的医院健康指导中心模式、以健身场所为平台的体育俱乐部健康指导模式、以社区为平台的健康监测中心模式和以体医融合健康产业为平台的产学研合作模式等4种运行模式。但总体来说我国体医融合尚处于起步阶段，优化体医融合专门人才培养模式成为体医深度融合发展的关键。研究表明，我国体医融合人才现行培养模式主要以在职培训为主，存在专门人才缺失严重、认证体系有待完善和人才培养模式单一等现实困境。同时提出加强顶层设计夯实体医融合人才培养体系，精心布局优化体医融合人才培养课程体系，强化引导完善体医融合舆论宣传体系，标准引领健全体医融合职业标准等优化路径，以期实现体医融合专门人才队伍的科学化和可持续化发展。     

Accuracy of prehospital triage systems for mass casualty incidents in trauma register studies - A systematic review and meta-analysis of diagnostic test accuracy studies

BackgroundPrioritising patients in mass casualty incidents (MCI) can be extremely difficult. Therefore, triage systems are important in every emergency medical service. This study reviews the accuracy of primary triage systems for MCI in trauma register studies.MethodsWe registered a protocol at PROSPERO ID: CRD42018115438. We searched MEDLINE, EMBASE, Central, Web of Science, Scopus, Clinical Trials, Google Scholar, and reference lists for eligible studies. We included studies that both examined a primary triage system for MCI in trauma registers and provided sensitivity and specificity for critically injured vs non-critically injured as results. We excluded studies that used paediatric, chemical, biological, radiological or nuclear MCIs populations or triage systems. Finally, we calculated intra-study relative sensitivity, specificity and diagnostic odds ratio for each triage system.ResultsTriage Sieve (TS) significantly underperformed in relative diagnostic odds ratio (DOR) when compared to START and CareFlight (CF) (START vs TS: 19.85 vs 13.23 (p<0.0001)│CF vs TS: 23.72 vs 12.83 (p<0.0001)). There was no significant difference in DOR between TS and Military Sieve (MS) (p<0.710). Compared to START, MS and CF TS had significantly higher relative specificity (START vs TS: 93.6% vs 96.1% (p=0.047)│CF vs TS: 96% vs 95.3% (p=0.0006)│MS vs TS: 94% vs 88.3% (p=0.0002)) and lower relative sensitivity (START vs TS: 57.8% vs 34.8% (p<0.0001)│CF vs TS: 53.9% vs 34.7% (p<0.0001)│MS vs TS: 51.9% vs 35.2% p<0.0001)).CF had significantly better relative DOR than START (CF vs START: 23.56 vs 27.79 (p=0.043)). MS had significantly better relative sensitivity than CF and START (MS vs CF: 49.5% vs 38.7% (p<0.0001)│MS vs START: 49.4% vs 43.9% (p=0.01)). In contrast, CF had significantly better relative specificity than MS (MS vs CF: 91.3% vs 93.3% (p<0.0001)). The remaining comparisons did not yield any significant differences.ConclusionAs the included studies were at risk of bias and had heterogenic characteristics, our results should be interpreted with caution. Nonetheless, our results point towards inferior accuracy of Triage Sieve compared to START and CareFlight, and less firmly point towards superior accuracy of Military Sieve compared to START, CareFlight and Triage Sieve     

Convergence and attractivity of memristor-based cellular neural networks with time delays

This paper presents theoretical results on the convergence and attractivity of memristor-based cellular neural networks (MCNNs) with time delays. Based on a realistic memristor model, an MCNN is modeled using a differential inclusion. The essential boundedness of its global solutions is proven. The state of MCNNs is further proven to be convergent to a critical-point set located in saturated region of the activation function, when the initial state locates in a saturated region. It is shown that the state convergence time period is finite and can be quantitatively estimated using given parameters. Furthermore, the positive invariance and attractivity of state in non-saturated regions are also proven. The simulation results of several numerical examples are provided to substantiate the results.