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.     

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.     

当前高校设备维修管理服务SWOT分析与战略探讨

随着高校对教学科研仪器设备投入的不断增加，仪器设备维修工作的重要性日益突出。通过SWOT分析法，对高校设备维修服务的内外因素进行分析，找出当前高校设备维修工作的内在优势和弱势、外在机会和挑战，并提出在高校设备维修服务战略方面的进攻调整防御和生存四种相应策略的思考。     

GPC-II-3液间歇低温灌流保存兔肺的形态学观察

目的探讨对移植供肺具有长期保护作用的新技术。方法用自制的GPC-Ⅱ-3液,以间歇低温灌流的方法保存兔肺24～192h,观察其组织结构的变化。结果保存120h以内的肺,其组织结构与对照组无明显区别,肺泡、各级支气管、血管的结构清楚、支气管上皮结构完整清楚,上皮细胞胞核清晰,染色质分布均匀、肺泡完整,肺泡上皮和毛细血管内皮清楚。部分肺泡腔中,可见结构清楚的尘细胞及其吞噬的粉尘颗粒;保存144h的肺,细胞成分的染色似乎有所加深,其余结构无明显变化。保存168h的肺,肺泡、各级支气管、血管的结构依然清楚,但细胞成分的染色有所加深,部分支气管上皮细胞有脱落现象。保存192h的肺,细胞成分的染色明显加深,有固缩迹象,支气管上皮有脱落现象加重。结论用GPC-Ⅱ-3液以低温冷藏的方法能够保存兔肺的组织结构120h。