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。     

Incidence of pressure ulcers in intensive care units and direct costs of treatment: Evidence from Iran

Background and objective

Pressure ulcer (PU) is one of the important and frequent complications of hospitalization, associated with high treatment costs. The present study was conducted to determine the incidence of PU and its direct treatment costs for patients in intensive care unit (ICU) in Iran.

Utility of Tissue Similarity Maps Based on Relative Cerebral Blood Volume for Grading Gliomas as Validated by Histological Results

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糖尿病护理门诊在糖尿病健康教育中的作用探究

目的:研究糖尿病护理门诊在糖尿病健康教育中作用.方法:取78例糖尿病患者,其中39例患者进行常规护理为对照组,39例患者进行糖尿病门诊护理为观察组,对比2组患者护理后自我管理能力和血糖指标.结果:观察组自我管理能力均高于对照组(P<0.05).观察组血糖指标均低于对照组(P<0.05).结论:向糖尿病患者进行健康教育时,糖尿病门诊护理的应用,可以科学创建护理档案,明确随访策略,提升糖尿病的管理水平,使患者具有较强自我管理能力,同时降低其血糖指标.     

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%.