涎腺导管内癌的病理诊断及新进展

涎腺导管内癌(intraductal carcinoma,IDC)是一种主要发生在腮腺,在导管内或囊内生长为主的罕见肿瘤,但也可伴浸润性生长,生物学行为相对惰性。具有独特的形态学、免疫表型及分子遗传学特征。主要包括4种亚型:闰管型、顶浆分泌型、闰管-顶浆分泌混合型和嗜酸型。闰管型多数存在NCOA4-RET融合,个别存在STRN-ALK融合;混合型存在TRIM27-RET融合,顶浆分泌型具有PIK3CA和HRAS突变或TP53缺失;嗜酸型具有TRIM33-RET融合或BRAF V600E突变。目前多数观点认为与涎腺导管癌不同,即使存在浸润,仍具有良好的预后,罕见复发和淋巴结转移,无远处转移。因此精准诊断对于临床治疗的选择极其重要。     

The potential mechanism of Bupleurum against anxiety was predicted by network pharmacology study and molecular docking

Metabolic Brain Disease - Bupleurum chinense DC. (Chaihu) is a traditional Chinese medicine (TCM) used in the treatment of anxiety. But the anxiolytic mechanisms of bupleurum are still unclear....     

MRI图像融合技术在肛瘘评估中的初步应用

目的采用图像融合技术获得T2WI与T2WI-FS的融合图像，评估其在肛瘘及肛周结构显示中的优势。 方法2016年6月至2018年6月，前瞻性选择中山大学附属第一医院29例肛瘘患者进行肛管磁共振（MR）检查，采用图像融合技术获取T2WI与T2WI-FS的融合图像T2WI-Fusion，利用Fisher score算法计算瘘管及肛门括约肌的组织间分辨力Fisher值、脂肪与肛门括约肌间的Fisher值，评估融合图像中瘘管及肛周结构的显示情况。采用改进的双刺激连续质量量表（DSCQS）对T2WI-FS、T2WI、增强3D-VIBE和T2WI-Fusion序列图像进行主观图像质量评价。 结果29例患者均成功获得T2WI与T2WI-FS的融合图像T2WI-Fusion。T2WI-Fusion、T2WI瘘管与括约肌间Fisher均值分别为6.46、3.31，T2WI-Fusion图像对瘘管的显示优于T2WI序列图像（P<0.001）。T2WI-Fusion、T2WI-FS脂肪与括约肌间Fisher均值分别为10.61、2.45，T2WI-Fusion图像对括约肌的显示优于T2WI-FS序列图像（P<0.001）。T2WI-Fusion对瘘管与括约肌的图像质量评价总评分均高于T2WI-FS、T2WI、增强3D-VIBE序列（P<0.001）。 结论MRI图像融合技术同时具备T2WI及T2WI-FS的优势，无需增加扫描序列及扫描时间，且操作简单，花费时间短，显著提高病变及肛周解剖结构的对比度和图像质量。     

Antiplatelet Therapy and Coronary Artery Bypass Grafting: Analysis of Current Evidence With a Focus on Acute Coronary Syndrome

This review was undertaken to summarize and discuss the current evidence around antiplatelet therapy and coronary artery bypass grafting (CABG). Aspirin (ASA) monotherapy remains the standard of care among patients before and after CABG. The role of more intense antiplatelet therapy—specifically, P2Y12 inhibitors—in improving clinical outcomes and graft patency is becoming increasingly apparent. As such, we provide an overview of a variety of antiplatelet regimens. The review discusses the evidence around preoperative management of antiplatelet therapies, with a particular focus on timing of cessation. It also evaluates the current literature to elucidate the best antiplatelet therapy regimen after CABG, focusing on acute coronary syndrome (ACS). Whenever possible, data are presented from randomized controlled trials (RCTs) and meta-analyses. Although guidelines recommend use of dual antiplatelet therapy (DAPT) after CABG for patients with ACS, available evidence is limited to small RCTs, and meta-analyses are of substudies of larger RCTs. There is also considerable heterogeneity in patient population of these studies; a significant number of patients underwent off-pump CABG (OPCAB) in trials that demonstrate graft-patency benefit with DAPT. With this limited evidence, DAPT remains underused in the CABG population, even among patients presenting after ACS.     

F形空心钉与传统3枚平行钉治疗Pauwels Ⅲ型股骨颈骨折的疗效比较

目的:分析比较"F"形空心钉与传统倒三角3枚平行螺钉内固定治疗青壮年Pauwels Ⅲ型股骨颈骨折的临床疗效。方法：2017年1月至2020年1月收治Pauwels Ⅲ型股骨颈骨折患者38例，根据置入钉方式的不同将其分为两组，其中A组18例，采用"F"形空心钉固定，男12例，女6例，年龄37~55岁，受伤至手术时间1~3 d。B组20例，采用传统倒三角3枚平行拉力螺钉固定，男12例，女8例，年龄35~55岁，受伤至手术时间为1~3 d。比较两组患者骨折不愈合，股骨头坏死，股骨颈短缩，空心螺钉退出情况，髋关节功能Harris评分，疼痛视觉模拟评分（visual analogue scale，VAS）。结果：所有患者获得随访，时间为15~31个月。两组患者在骨折不愈合，股骨颈短缩，股骨头坏死方面差异无统计学意义（P>0.05）；两组患者在螺钉退出方面差异有统计学意义（P<0.05）。两组患者术后12个月时髋关节Harris评分及VAS评分差异均无统计学差异（P>0.05）。结论："F"形与传统倒三角3枚平行空心钉内固定治疗青壮年Pauwels Ⅲ型股骨颈骨折中短期疗效相似，但"F"形空心钉退钉率较低。     

Deep Phenotyping and Prediction of Long-term Cardiovascular Disease: Optimized by Machine Learning

BackgroundPrediction of cardiovascular disease (CVD) is important in clinical practice. Machine learning (ML) may offer an improved alternative to current CVD risk stratification in individual patients. We aim to identify important predictors and compare ML models with traditional models according to their prediction performance in a large long-term follow-up cohort.MethodsThe Atherosclerosis Risk in Communities (ARIC) study was designed to study the progression of subclinical disease to cardiovascular events over a 25-year follow-up period. All phenotypic variables at visit 1 were obtained. All-cause death, CVD, and coronary heart disease were the outcomes for analysis. The ML framework involved variable selection using the random survival forest (RSF) method, model building, and 5-fold cross-validation. Model performance was evaluated by discrimination using the Harrell concordance index (C-index), accuracy using the Brier score (BS), and interpretability using the number of variables in the model.ResultsOf the 14,842 participants in ARIC, the average age was 54.2 years, with 45.2% male and 26.2% Black participants. Thirty-eight unique variables were selected in the RSF top 20 importance ranking of all 6 outcomes. Aging, hypertension, glucose metabolism, renal function, coagulation, adiposity, and sodium retention dominated the predictions of all outcomes. The ML models outperformed the regression models and established risk scores with a higher C-index, lower BS, and varied interpretability.ConclusionsThe ML framework is useful for identifying important predictors of CVD and for developing models with robust performance compared with existing risk models.