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.

Organic and Inorganic Mercury in Biological Samples of Flouresecent Lamp Industries Workers and Health Risks

Objective The present study aims to investigate the concentrations of Hg and its aspects methyl mercury(Me-Hg) and inorganic mercury(I-Hg) in the biological samples(BSs) of fluorescent lamp industries workers(FLIWs).Methodology Different BSs including red blood cells(RBCs),plasma,urine,hair and nails were collected from the workers exposed to Hg and unexposed persons were selected as control group to measure both the T-Hg concentration as well as its species in different biological samples through quantitative analysis.Health data was collected through questionnaire survey.Results The mean concentrations of T-Hg(31.9 μg/L),Me-Hg(27.7 μg/L),and I-Hg(5.36 μg/L) in RBCs were found significantly(P 0.001) higher among the workers(n = 40) as compared to the control group(n = 40).Similarly the mean Hg concentrations in plasma,urine,hair and nails were also significantly higher among the workers than the control group.The statistical relation between Hg concentration and demographic characteristics observed that workers experience and fish consumption has increased the Hg concentration while age,weight and smoking found no significant effect on Hg concentration in the BSs.Conclusion The study observed that the workers were highly exposed to high concentration of Hg and they are at a high health risk.     

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%.     

胸腔镜下心包厚壁囊肿切除术1例报告

面对来势汹汹的新型冠状病毒肺炎(COVID-19),如何能够正确、快速诊断和筛检出确诊病例显得尤为重要,国内诸多科技工作者都在积极开展相关的诊断试验研究。本文回顾了目前正在开展的十余项诊断试验的注册信息,针对其中存在的共性问题进行了讨论,重点阐述了如何使用PICOS原则构造COVID-19诊断试验的研究问题,还对金标准的设置、受试者的代表性、样本量计算和同步、独立、盲法的测定等诊断试验的设计要点进行了详细说明。旨在为广大研究者提供开展COVID-19诊断试验的设计建议,帮助研究者在顶层设计阶段减少、避免偏倚,完成高质量的临床研究,为临床诊疗提供循证医学证据。