2010—2016年重庆市中毒病例流行病学特征及影响因素分析

对2010年1月—2016年7月重庆市中毒救治指定医疗机构收治的2 497例中毒患者进行回顾性分析。结果显示，中毒患者中男性1 230例（49.3%）、女性1 267例（50.7%）；分布最多的3个年龄段依次是41~50岁（18.5%）、≤10岁（16.4%）和21~30岁（13.0%）；死亡16例，病死率0.6%。中毒类型构成排前三位的是细菌（33.8%）、化学品（32.2%）、植物（14.8%）。中毒预后多因素Logistic回归分析结果显示重度中毒患者结局死亡的可能性是轻度患者的1 102倍。提示重庆市应重点关注农村地区41~50岁人群的细菌、化学品、药物中毒，同时应该加强儿童中毒防控工作。     

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%.     

对药代动力学实验的两点改进

通过具体的实验数据，讨论在药代动力学实验中如何加入曲线下面积（AUC）的计算，以帮助学生理解并掌握这一概念；在实验室设置不同剂量组，给药后比较剂量与浓度是否呈等比例关系，以此加深学生对于一级动力学消除及其临床意义的理解。     

Bayesian estimation of a random effects heteroscedastic probit model

Summary Bayesian analysis is given of a random effects binary probit model that allows for heteroscedasticity. Real and simulated examples illustrate the approach and show that ignoring heteroscedasticity when it exists may lead to biased estimates and poor prediction. The computation is carried out by an efficient Markov chain Monte Carlo sampling scheme that generates the parameters in blocks. We use the Bayes factor, cross‐validation of the predictive density, the deviance information criterion and Receiver Operating Characteristic (ROC) curves for model comparison.     

Greater curve approach for laparoscopic gastric bypass: total stapler gastrojejunostomy

BACKGROUND: Gastric bypass is an established bariatric procedure that has undergone multiple modifications to improve its effectiveness. The side-to-side stapled technique is well recognized, but closure of the gastrotomy/enterotomy by the stapler can potentially narrow the Roux limb. Because of this, many surgeons will hand suture the closure of the gastrotomy/enterotomy. To obviate this difficulty, we inserted the linear stapler from the stomach's greater curvature, using a double-stapled anastomosis that minimized the need for hand suturing. METHODS: We performed a retrospective analysis of 307 patients undergoing this technique for laparoscopic gastric bypass. The weight loss and 30-day morbidity and mortality were tabulated and compared with those in other published series. RESULTS: Of the 307 patients, none died postoperatively. The overall 30-day morbidity rate was 15%. Two leaks from the gastrojejunostomy and 2 from the jejunojejunostomy (1.2%) developed. The mean percentage of excess weight loss was 34% at 3 months, 52% at 6 months, 73% at 1 year, 71% at 2 years, and 69% at 3 years. CONCLUSION: The greater curve approach avoids Roux limb obstruction, minimizes the need for hand suturing, and uses standard trocar incisions. Our short-term follow-up results are similar to those of series of other techniques.     

人工神经网络在避孕研究中的应用

人工神经网络作为一门新兴的边缘学科,已开始在许多领域应用.本文将神经网络的方法应用于避孕方法选择方面,并通过ROC曲线比较了神经网络方法与传统的多元LO-gistic回归分析方法对训练样本和检验样本的表现.结果提示,神经网络方法有望在统计学、医学领域得到进一步应用和发展.     

应用ROC曲线评价DR后处理技术对胸部影像的影响

目的:运用ROC曲线,探讨DR胸部摄影最佳的影像后处理技术。方法:使用DR系统设置的正常胸部摄影条件,对胸部体模与专用于X线摄影的ROC曲线评价体模(TRG)进行组合并曝光。分析未使用后处理技术及使用不同后处理技术,对DR胸部摄影胸片影像的影响。结果:使用单纯噪声消除技术、噪声消除加图像反转及噪声消除加全屏放大三种后处理技术所得的ROC曲线下面积Az值依次为0.85、0.825及0.925,均明显大于未使用后处理技术的面积Az值0.6;单纯噪声消除技术获得的ROC曲线下面积略大于噪声消除加图像反转技术的面积;噪声消除加全屏放大技术获得的ROC曲线下面积Az值最大。结论:应对胸部原始影像实施后处理调节。单纯噪声消除技术可明显增加影像的信息量,若在噪声消除基础上实施全屏放大技术,可获得DR胸部摄影的最大影像信息量。