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。     

特发性肺纤维化急性加重期证候与血清生物标志物的相关性研究＊

目的 通过分析特发性肺纤维化急性加重期（AE-IPF）患者证候与血清生物标志物的关系，为中医辨证治疗提供参考。方法 采用观察性研究设计，收集2019年3月至2019年11月三个中心的AE-IPF患者76例，其中痰热壅肺证26例、痰浊阻肺证50例，并纳入健康志愿者10例作为对照。采用ELISA测定患者血清CCL18、HMGB1、KL-6、MMP-7、SP-A和SP-D水平，分析与中医证候的相关性。结果 AE-IPF患者血清CCL18、HMGB1、KL-6、MMP-7、SP-A和SP-D水平均显著高于健康对照组。血清CCL18、HMGB1、KL-6、MMP-7和SP-D水平在痰热壅肺证和痰浊阻肺证患者间无显著性差异（P＞0.05），而血清SP-A水平存在显著性差异（P＜0.05）。结论 血清SP-A与AE-IPF证候存在一定的相关性，血清SP-A的浓度升高，与痰热壅肺证关系越密切，反之，血清SP-A浓度降低，则与痰浊阻肺证关系越密切。AE-IPF痰热壅肺证患者的预后可能较痰浊阻肺证患者更差。     

李延应用泽泻汤合温胆汤治疗舒张压高的临床经验

随着社会的发展,人类生活方式的改变,高血压病越来越高发,具有低龄化趋势,代谢综合征常伴随发生,此种情况下的高血压往往是以舒张压升高为主,临床表现常与“亚健康”状态混淆,未能引起患者甚至部分医生的重视,然而事实上越来越多的科学研究证实舒张压高之危害十分明显,不容忽视。尽管现代医学对舒张压高的病因及病理机制有明确的阐述,但是暂时没有特效的药物。李延教授在治疗高血压病方面有着丰富的临床经验,临床中运用泽泻汤合温胆汤加减治疗舒张压高之眩晕,切中病机,加减灵活,屡有良效。文中从中医角度阐述舒张压高的病因病机,介绍李师辨病辨证思路,组方用药特点,附三则典型验案,另加个人心得体会,以期为舒张压高的有效治疗提供思路。     

Total bilirubin independently predicts incident metabolic syndrome among community-dwelling women

BackgroundMetabolic syndrome (MetS) is related to the increased risk of major cardiovascular diseases (CVD). The link between high serum total bilirubin (TBL) is cross-sectionally related to MetS and its components. However, whether serum TBL predicts incidence of MetS and its components remains inconclusive.MethodsThe present study included 893 women aged 70 ± 9 years from a rural village. We examined the relationship between serum TBL and MetS based on the modified criteria of the National Cholesterol Education Program's Adult Treatment Panel (NCEP-ATP) III report in a cross-sectional (N = 893) and cohort (N = 288) data.ResultsIn the cross-sectional study, serum TBL (β = 0.536, p < 0.001) as well as age, alcohol consumption, exercise habits, history of CVD, SUA, GGT, and ALT was significantly and dependently associated with number of MetS components, but in the cohort study serum TBL was not associated with number of MetS components. Compared with the 1st tertile of serum TBL (0.20–0.55 mg/dL), multivariate-adjusted odds ratio (95% confidence interval) for the 2nd -3rd tertiles of serum TBL (0.54–2.00 mg/dL) was 0.70 (0.51–0.95) in the cross-sectional study and 0.41 (0.21–0.81) in the cohort study.ConclusionsOur data demonstrated an independently negative association between serum TBL and MetS in Japanese community-dwelling women.     

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%.