A case of purulent pneumococcal pericarditis

Background

Purulent bacterial pericarditis is a rare and potentially fatal disease. The course may be fulminant, and the presentation may pose a diagnostic challenge.

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%.     

基于医院信息系统的乳腺癌临床特征及中西医用药特征分析

目的 解析真实世界中乳腺恶性肿瘤患者的人群特征、诊断特征、中西医用药特征，为乳腺癌的临床防治提供参考。方法 采集2002年2月至2015年5月全国60家三级甲等医 院信息系统（Hospital Information System，HIS）中，出院诊断为“乳腺癌”的患者用药信息，采用SAS9.3统计软件，对人口学信息、诊断信息、医嘱用药信息等进行描述性分析。结果 39798例乳腺癌患者，平均年龄（50.93者，平均年龄）岁；多以门诊入院，入院病情以“一般”为主;合并疾病主要为高血压，骨肿瘤，联用西药以抑制肿瘤细胞增殖、治疗并发症、缓解放化疗不良反应为主；中医辨证以痰瘀互结证，气阴两虚证，肝气淤滞证，脾气亏虚证型最为常见，临床清热解毒剂、益气扶正剂，活血化瘀剂应用较多。结论 乳腺癌中西医结合治疗，联用药物广泛，临床治疗基本符合临床指南。     

Instrument-based Tests for Measuring Anterior Chamber Cells in Uveitis: A Systematic Review

ABSTRACT

Purpose

New instrument-based techniques for anterior chamber (AC) cell counting can offer automation and objectivity above clinician assessment. This review aims to identify such instruments and its correlation with clinician estimates.     

2014—2018年黄山市淋病疫情流行特征分析

目的分析2014-2018年黄山市淋病的流行特征,为全市的淋病防控工作提供科学依据。方法通过中国疾病预防控制信息系统收集黄山市2014—2018年的淋病数据、人口数据,进行统计分析。结果2014—2018年黄山市累计报告淋病病例761例,年均发病率为11.09/10万,年均增长率为29.14%,发病率呈逐年上升趋势(χ^2趋势=112.29,P<0.05)。淋病的人群分布中,男女报告病例数之比为13.63:1,男女发病率有统计学差异(χ^2=113.18,P<0.05);15~49岁年龄组病例数最多,占85.94%,职业以农民最多,占44.81%;以屯溪区、歙县报告病例数较多,分别占37.45%、23.26%。结论2014—2018年黄山市淋病发病率呈上升趋势,淋病防控形势严峻,需进一步完善各项防控措施。     

Lung Ultrasound for the Emergency Diagnosis of Pneumonia,Acute Heart Failure,and Exacerbations of Chronic Obstructive Pulmonary Disease/Asthma in Adults: A Systematic Review and Meta-analysis

Background

Lung ultrasound can accelerate the diagnosis of life-threatening diseases in adults with respiratory symptoms.

Diagnostic accuracy of the Retinal Thickness Analyser: differentiation between normal eyes and eyes with glaucoma or macular pathologies

Background The Retinal Thickness Analyser (RTA) is intended to detect glaucomatous changes as well as macular pathologies at the posterior pole. We determined the diagnostic accuracy for eyes with manifest glaucoma or macular diseases.Methods We examined 71 eyes with long-term, established eye conditions. Included were 28 eyes with glaucoma, 21 with different macular diseases and 22 normal eyes. All examinations were evaluated in a blind-test by RTA experts without any clinical information on the patients. After comparison of the RTA interpretation with the clinical diagnosis, we determined sensitivity, specificity, positive and negative predictive values.Results Of 71 examinations, 15 (21%) were not interpretable. If these results are excluded, the following diagnostic accuracy values were calculated for glaucoma and macular disorders respectively: sensitivity 75 and 59%, specificity 55 and 97%, positive predictive value 48 and 90% and negative predictive value 80 and 84%. These values were not significantly different when both eyes of each patient were included in the final analysis (n=133).Conclusion The diagnostic values of the RTA determined in this case control study were not satisfactory. However, no clinical information was used in the assessment. The extent to which additional clinical information increases the diagnostic value remains to be determined.     

Prenatal screening and diagnosis of handicaps: Current state of the art

Current medical procedures used in prenatal screening and diagnosis of handicapping conditions are reviewed. These strategies include ultrasound, amniocentesis, chorionic villus biopsy, restriction enzyme analysis, maternal serum analysis, fetoscopy, and fetal serum analysis. Along with an explanation of each method, advantages, disadvantages, and risks involved with each are provided. An understanding of these procedures by medical and educational personnel is encouraged, and the potential benefits of prenatal identification of handicapping conditions are emphasized.