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%.     

Insight towards Nucleation Mechanism and Change in Morphology for Nanostructured Platinum Thin Film Directly Grown on Carbon Substrate via Electrochemical Deposition

Nanocrystalline platinum with different morphologies is synthesized via electrochemical deposition technique. The nucleation mechanism and its structural effect over the electrodeposited Pt on carbon electrodes have been systematically studied. Powder X-ray diffraction, X-ray photoelectron spectroscopy, and field-emission scanning electron microscopy are employed to study nucleation, oxidation states, and Pt structure growth on a carbon electrode. This study reports significant development of Pt metal nanoparticles with different morphologies such as a sphere, flower, core-flower, and rod-like structure directly fabricated on carbon electrode while tuning the deposition parameters such as current density, time, temperature, pH during the deposition process. The proposed electrochemical route represents a superior fabrication procedure for large-scale electrode fabrication compared to a conventional method for preparing membrane electrode assemblies for fuel cell stacks.     

环境水样中痕量铜的浊点萃取-火焰原子吸收光谱测定法

目的建立环境水样中痕量铜的浊点萃取(cloud point extraction,CPE)-火焰原子吸收光谱(flame atomic absorption spectrometry,FAAS)测定法。方法样品在p H 9.5的条件下,加入0.4 ml的1 mmol/L 2-(5-溴-2-吡啶偶氮)-5-二乙氨基酚(5-Br-PADAP)溶液,0.1%氯化钙溶液0.1 ml,5%(W/V)Triton X-114溶液0.8 ml,40℃加热15 min后离心,采用火焰原子吸收光谱法进行检测。结果在2~240μg/L的线性范围内,所得回归方程为A=0.002 7c+0.024 6,r=0.995 8。以3倍信噪比计算,方法的检出限为0.62μg/L,富集倍数为36.58倍,平均加标回收率为96.28%~98.08%,RSD为1.67%~3.13%。结论该方法简单、灵敏,具有良好的重现性,适用于环境水样中痕量铜的测定。     

经自然腔道取标本手术在结直肠外科手术中的应用进展

随着腔镜技术的进一步发展以及微创理念应用于结直肠外科疾病的诊治中,结直肠相关疾病的诊治发生了翻天覆地的变化。由传统的经腹手术到腹腔镜手术、经自然腔道手术,再到经自然腔道取标本手术(NOSES),结直肠疾病的外科诊治在微创领域取得了巨大成果。NOSES技术是目前结直肠外科在微创领域前沿的手术方式之一,它通过经直肠、阴道取标本来避免了腹壁的辅助取标本切口,从而将结直肠外科手术进一步微创化。NOSES技术集传统腹腔镜手术的优势与现代微创外科的理念于一体,它在确保手术效果的基础上集中体现了微创、加速康复外科、功能外科、"无疤"等理念的特点。本文主要就国内外各中心开展NOSES技术在结直肠外科诊治开展中的相关经验、心得和体会进行综述。     

不同手术方法治疗复杂胫骨平台骨折临床疗效观察

目的探讨不同手术方法治疗复杂胫骨平台骨折临床疗效。方法选择2016年1月—2018年12月84例复杂胫骨平台骨折患者,随机分组。单侧锁定钢板内固定组选择单侧锁定钢板内固定手术,双侧切口双侧解剖钢板内固定组选择双侧切口双侧解剖钢板内固定。分析手术操作时间、手术失血、平均住院天数以及复杂胫骨平台骨折愈合时间;治疗前后患者视觉模拟评分和Rasmussen膝关节功能评分;膝关节僵硬发生率。结果双侧切口双侧解剖钢板内固定组视觉模拟评分和Rasmussen膝关节功能评分、手术操作时间、手术失血、平均住院天数以及复杂胫骨平台骨折愈合时间、膝关节僵硬发生率和单侧锁定钢板内固定组比较有优势,P<0.05。结论复杂胫骨平台骨折患者实施双侧切口双侧解剖钢板内固定可获得较好效果。     

子宫内膜功能亢进性疾病的“异病同治”诊治研究思路＊

笔者根据现代临床子宫内膜的病理演变过程结合中医“异病同治”理念，提出“子宫内膜功能亢进性疾病”概念，涵盖5种常见的子宫内膜疾病：无排卵性异常子宫出血（Abnormal Uterine Bleeding Ovulatory dysfunction，AUB-O），子宫内膜息肉（Endometrial Polyp，EP），子宫内膜异位症（Endometriosis，EMs），子宫腺肌病（Adenomyosis，AM），子宫内膜癌（Endometrial Carcinoma，EC）。基于文献的汇总，分析得到此类疾病的子宫内膜病理演变与中医病因病机的关联性，为寻找关键通路、创新性治疗此类疾病提供了重要的参考方向，对深化中医妇科学常见疾病的共性病因病机提供了理论依据和探索思路。     

两例 SMILE 术中特殊情况更改 SBK 手术的疗效观察

目的：通过对同一患者双眼不同手术方式的自身对比，了解飞秒扫描后改SBK手术的疗效差异，为不同患者屈光手术方式的选择提供临床指导依据。方法回顾分析2例右眼行飞秒激光小切口透镜取出术（ SMILE），左眼SMILE术中负压环脱失和激光扫描不良透镜分离困难而改行机械刀制瓣准分子激光手术（ SBK）的患者，比较术后视力、等效球镜（ SE）、对比敏感度（ CSF）、波前像差（ HAOS）以及泪膜破裂时间（ BUT）、泪液分泌功能试验（ Schimer I）和SPEED、OSDI干眼症状问卷调查分析差异。结果（1）术后个月，均能达到最佳矫正视力（BSCVA），等效球镜均在±0．75 D以内，双眼比较无显著性差异。（2）CSF的比较，左右眼无显著性差异，略低于正常水平。（3）术后3个月，BUT和Schimer检查中，双眼无显著性差异。（4）HAOS比较中，总像差RMS和高阶像差这两项， SMILE眼低于SBK眼；球差（S）的比较，SMILE眼高于SBK眼。（5）SPEED和OSDI问卷评分双眼无显著差异。结论 SMILE失败眼改SBK后视觉质量与SMILE眼无显著差异，其视觉质量、疗效不受影响。     

In Vitro-In Vivo Extrapolation and Hepatic Clearance-Dependent Underprediction

Accurately predicting the hepatic clearance of compounds using in vitro to in vivo extrapolation (IVIVE) is crucial within the pharmaceutical industry. However, several groups have recently highlighted the serious error in the process. Although empirical or regression-based scaling factors may be used to mitigate the common underprediction, they provide unsatisfying solutions because the reasoning behind the underlying error has yet to be determined. One previously noted trend was intrinsic clearance-dependent underprediction, highlighting the limitations of current in vitro systems. When applying these generated in vitro intrinsic clearance values during drug development and making first-in-human dose predictions for new chemical entities though, hepatic clearance is the parameter that must be estimated using a model of hepatic disposition, such as the well-stirred model. Here, we examine error across hepatic clearance ranges and find a similar hepatic clearance-dependent trend, with high clearance compounds not predicted to be so, demonstrating another gap in the field.