Thrombosis and thromboembolism: Brighton collaboration case definition and guidelines for data collection,analysis, and presentation of immunization safety data

This is a Brighton Collaboration case definition of thrombosis and thromboembolism to be used in the evaluation of adverse events following immunization, and for epidemiologic studies for the assessment of background incidence or hypothesis testing. The case definition was developed by a group of experts convened by the Coalition for Epidemic Preparedness Innovations (CEPI) in the context of active development of SARS-CoV-2 vaccines. The case definition format of the Brighton Collaboration was followed to develop a consensus definition and defined levels of certainty, after an exhaustive review of the literature and expert consultation. The document underwent peer review by the Brighton Collaboration Network and by selected expert reviewers prior to submission.     

Modulators of platelet function in aging

Abstract

Platelets are small, anucleated effector cells that play an important role in linking the hemostatic and inflammatory processes in the body. Platelet function is known to be altered under various inflammatory conditions including aging. A gain in platelet function during aging can increase the risk of thrombotic events, such as stroke and acute myocardial infarction. Anti-platelet therapy is designed to reduce risk of serious cerebrovascular and cardiovascular events, but the adverse consequences of therapy, such as risk for bleeding increases with aging as well. Age-associated comorbidities such as obesity, diabetes, and hyperlipidemia also contribute to increased platelet activity and thus can enhance the risk of thrombosis. Therefore, identification of unique mechanisms of platelet dysfunction in aging and in age-associated comorbidities is warranted to design novel antiplatelet drugs. This review outlines some of the current areas of research on aging-related mechanisms of platelet hyperactivity and addresses the clinical urgency for designing anti-platelet therapies toward novel molecular targets in the aging population.     

奥扎格雷联合低分子肝素钙治疗脑血栓的临床研究

目的：对应用低分子肝素钙与奥扎格雷联合对患有脑血栓疾病的患者实施治疗的临床效果进行研究。方法整群选择在该院2012年12月—2014年12月就诊的患有脑血栓疾病的患者86例，随机分为对照组和治疗组，每组43例。采用奥扎格雷对对照组患者实施治疗；采用低分子肝素钙与奥扎格雷联合对治疗组患者实施治疗。对比神经功能缺损评分在药物治疗前后的变化幅度、脑神经功能恢复正常时间和脑血栓药物治疗计划实施总时间、脑血栓疾病药物治疗效果、用药期间的不良反应人数。结果治疗组患者神经功能缺损评分在药物治疗前后的变化幅度明显大于对照组；脑神经功能恢复正常时间（9.66±2.41）d和脑血栓药物治疗计划实施总时间（13.28±2.14）d明显短于对照组（13.62±3.47）、（17.39±3.20）d；脑血栓疾病药物治疗效果（总有效率90.6%）明显优于对照组（总有效率69.8%）；用药期间的不良反应人数（1例）明显少于对照组（8例）。结论应用低分子肝素钙与奥扎格雷联合对患有脑血栓疾病的患者实施治疗的临床效果非常明显。     

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.     

Development of a core outcome set for venous leg ulceration (CoreVen) research evaluations (protocol)

BackgroundA venous leg ulcer is a chronic leg wound caused by poor venous blood circulation in the lower limbs. It is a recurring condition causing pain, malodour, reduced mobility, and depression. Randomised controlled trials evaluating treatments for venous leg ulcers provide important evidence to inform clinical decision-making. However, for findings to be useful, outcomes need to be clinically meaningful, consistently reported across trials, and fully reported. Research has identified the large number of outcomes reported in venous leg ulcer trials, impacting both synthesis of results, and clinical decision-making. To address this, a core outcome set will be developed. A core outcome set is an agreed standardised set of outcomes which should be, as a minimum, measured and reported in all trials which evaluate treatment effectiveness for a given indication. A core outcome set has the potential to reduce research waste, improve the utility of RCTs, reduce reporting bias, facilitate treatment comparisons across different sources of evidence and expedite the production of systematic reviews, meta-analyses and evidence-based clinical guidelines.AimThe aim of this project is to develop a core outcome set for research evaluating the effectiveness of interventions for treating venous leg ulceration.MethodsThrough a scoping review of the literature on venous leg ulceration, we will firstly identify a list of candidate outcome domains (broad categories in relation to what is being measured) from randomised controlled trials and qualitative research, and outcomes (specific methods in relation to what is being measured). In two further stages, we will use the resulting lists of outcome domains and outcomes to design two online surveys. A range of stakeholders will be invited to participate in the surveys and they will be asked to indicate which outcome domains and outcomes are most important and should be considered as core in future research reports.