Endoscopy-Related Bleeding and Thromboembolic Events in Patients on Direct Oral Anticoagulants or Vitamin K Antagonists

1. Download : Download high-res image (232KB)
2. Download : Download full-size image

     

脑卒中患者恐惧疾病进展的研究现状

脑卒中患者在面对复杂的治疗期、漫长的康复期及难以预测的病情变化时容易产生对疾病或疾病进展的恐惧。恐惧疾病进展会损害脑卒中患者的身心健康和社会功能,最终影响患者的康复和预后。从恐惧疾病进展的定义、测量工具、国内外研究现状及影响因素几个方面进行综述,为临床护理人员深入了解脑卒中患者恐惧疾病进展现状、开展相关护理实践和临床研究提供依据。     

模拟诊疗在留学生心血管内科实习带教中的应用研究

目的　探讨模拟诊疗在留学生心血管内科实习带教中的应用效果。方法　将35名留学生随机编号，分成试验组和对照组。试验组18名学生分成3组（每组6名）进行模拟诊疗教学，对照组17名学生进行常规实习。通过填写评价单和相关的临床能力测试对两组教学效果进行评价。选用SPSS 19.0软件进行t检验。结果　参与模拟诊疗教学的留学生均希望在各科实习中多采用此方法。在临床能力测试方面，试验组学生在病史采集[（22.16±2.17）vs.（20.33±2.03）]、体格检查[（23.05±1.79）vs.（19.78±2.05）]及回答问题[（23.50±1.47）vs.（19.56±1.92）]方面均优于对照组，差异有统计学意义（P<0.05）。但是在病历书写方面，两组差异无统计学意义（P>0.05）。结论　利用高仿真的模拟诊疗教学手段，弥补了留学生在实习期间不能充分接触患者的缺陷，有助于提高其临床操作技能、增强医患沟通能力等医学职业素养。     

Preparing IR for COVID-19: The Singapore Experience

This paper describes country-wide special measures undertaken for interventional radiology staff during the current coronavirus disease 2019 (COVID-19) pandemic. Although each interventional radiology service around the world faces unique challenges, the principles outlined in this article will be useful when designing or strengthening individual practices and integrating them within wider hospital and national measures. Moving beyond the current outbreak, these measures will be useful for any future infectious diseases which are likely to arise.     

Resistin is elevated in cystic fibrosis sputum and correlates negatively with lung function

Background

Resistin is an immunometabolic mediator that is elevated in several inflammatory disorders. A ligand for Toll-like receptor 4, resistin modulates the recruitment and activation of myeloid cells, notably neutrophils. Neutrophils are major drivers of cystic fibrosis (CF) lung disease, in part due to the release of human neutrophil elastase- and myeloperoxidase-rich primary granules, leading to tissue damage. Here we assessed the relationship of resistin to CF lung disease.

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.