基于文献的老年人群常见证候分布特征研究＊

目的 了解我国老年人中医证候分布特征，为中医药辨治老年人提供参考依据。方法 系统检索中国知识基础设施（CNKI）、中文科技期刊数据库（CCD）、万方数据资源系统数据库（CSPD），纳入研究对象为老年人的临床流行病学调查研究文献，对其调查目的、调查疾病及调查结果等进行描述统计。结果 ①最终纳入168篇文献，其中2010至2020年之间发表138篇（82.14%）；调查地区涵盖全国28个省市自治区，共纳入43948例老年人，累计覆盖340个研究中心；②主要研究结果显示，老年人中医证候阴虚阳亢证（10.05%）>血瘀证（9.5%）>痰浊（湿）证（8.91%）>阴阳两虚证（4.98%）>痰瘀互结证（4.96%）；单元证分布阴虚证（14.70%）>痰证（11.22%）>气虚证（7.15%）>肾虚证（4.72%）>血瘀证（4.18%）；涉及病变脏腑肾>肝>脾>肺>心。结论 根据统计结果，可以看出老年人证候分布虚证多于实证，虚证中又以阴虚证、气虚证最为多见，实证常见血瘀证、痰浊（湿）证等，且多见痰瘀互结证；老年人五脏皆损，其中又以肾、肝、脾功能失调最为突出。     

蒙药绿松石的质量标准研究＊

目的 建立蒙药绿松石的质量标准。方法 收集不同产地绿松石，共10批。观察绿松石样品和粉末的性状并进行理化鉴别；按2020年版《中国药典》（四部）通则方法测定绿松石样品中水分、浸出物含量；采用原子吸收光谱法测定绿松石样品铜元素含量。结果 绿松石为不规则、周围带有黑石的块状物，表面蓝绿色，体重，质硬脆，难砸碎，断面呈贝壳状，蜡样光泽，粉末呈灰绿色，无臭，味淡；理化鉴别结果显示，呈铜盐反应；10批次样品中水分含量为0.41%-3.94%（SD=1.37%），浸出物含量为0.21%-0.81%（SD=0.21%），铜元素含量为3.03%-4.63%（SD=0.63%）。结论 初步拟定绿松石中水分含量不得超多5.0%、浸出物含量不得低于0.10%，铜元素含量应为2.60%-4.84%，制定的标准可用于蒙药材绿松石的质量控制。     

胎儿小脑蚓部不同超声纹理特征与胎儿畸形关系的初探郭永华 肖宝军

目的：总结后颅窝正常和异常的胎儿小脑及小脑蚓部的矢状面的声像学特征,了解小脑细微结构的改变与胎儿畸形的关系。     

Early posterior vitreous detachment is associated with LAMA5 dominant mutation

Background: Extracellular matrix molecular components, previously linked to multisystem syndromes include collagens, fibrillins and laminins. Recently, we described a novel multisystem syndrome caused by the c.9418G>A p.(V3140M) mutation in the laminin alpha-5 (LAMA5) gene, which affects connective tissues of all organs and apparatus in a three generation family. In the same family, we have also reported a myopic trait, which, however, was linked to the Prolyl 4-hydroxylase subunit alpha-2 (P4HA2) gene. Results of investigation on vitreous changes and their pathogenesis are reported in the present study.Materials and Methods: Nineteen family individuals underwent complete ophthalmic examination including best-corrected visual acuity (BCVA), fundus examination, fundus photography, intraocular pressure measurement, axial length measurement using ocular biometry, Goldmann visual field examination, standard electroretinogram, SD-OCT. Segregation analysis of LAMA5 and P4HA2 mutations was performed in enrolled members.Results: The vitreous alterations fully segregated with LAMA5 mutation in both young and adult family members. Slight reduction of retinal thickness and peripheral retinal degeneration in only two patients were reported.Conclusions: In this work we showed that PVD is a common trait of LAMA5 multisystem syndrome, therefore occurring as an age-unrelated trait. We hypothesize that the p.(V3140M) mutation results in a reduction of retinal inner limiting membrane (ILM) stability, leading to a derangement in the macromolecular structure of the vitreous gel, and PVD. Further investigations will be necessary to elucidate the role of wild type and mutated LAMA5 in the pathogenesis of PVD.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.