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%.     

TLR9在不同胎龄新生儿脐血中的表达变化

目的 通过观察早产儿不同胎龄Toll样受体9（TLR9）的表达，探讨早产儿免疫功能低下的机制。方法 采集2010年7月至2014年6月在上海市嘉定区妇幼保健院产科出生的活产新生儿的脐血229份，按胎龄分为4组，28~31周组，31~34周组，34~37周组，≥37周组，采用流式细胞术和实时荧光定量PCR方法，分别检测其TLR9的蛋白和mRNA表达情况，了解其与胎龄之间的关系，并分析mRNA和蛋白表达间的相关性。结果 TLR9阳性细胞率在28~31周组，31~34周组，34~37周组，≥37周组分别为（15.93±6.23）%，（11.63±6.70）%，（13.66±6.88）%，（20.51±12.06）%；其在胎龄28~31周较高，至31~34周逐渐下降至最低，两组差异有统计学意义（P<0.05）；34~37周后TLR9阳性细胞率表达逐渐升高，至≥37周达最高，两胎龄组比较，差异具有统计学意义（P<0.05）。31~37周间新生儿脐血TLR9阳性细胞率与胎龄呈正相关（r=0.273，P=0.006）。TLR9 mRNA表达在28~31周组，31~34周组，34~37周组，≥37周组分别为（4.95±3.44）%，（8.89±8.49）%，（13.91±10.92）%，（7.19±7.11）%；其在28~36周逐渐升高，与胎龄呈正相关（r=0.355，P< 0.001）。≥37周TLR9 mRNA表达量下降，该值虽高于28~31周，但差异无统计学意义（P>0.05）。相关性分析表明，同胎龄时期同样本新生儿的TLR9 mRNA和TLR9阳性细胞率之间存在负相关（r=-0.227，P=0.011）。结论 TLR9阳性细胞率和TLR9 mRNA表达在不同胎龄组新生儿间有差异，TLR9阳性细胞率表达在31~37周间随着胎龄的增加而增加，TLR9 mRNA在28~36周间随着胎龄的增加而增加。     

苯扎贝特治疗儿童ApoE Tokyo(Arg25Cys)突变型脂蛋白肾病1例

脂蛋白肾病(Lipoprotein glomerulopathy,LPG),1989年首次由日本学者Saito报道,LPG主要累及肾脏,且以肾小球病变为主[1]。几乎所有患者均有不同程度的蛋白尿,多数表现为肾病综合征,少数表现为轻微蛋白尿和镜下血尿,部分患者伴有不同程度的贫血及高血压,血脂异常易被忽略为肾病综合征的低蛋白血症所致。载脂蛋白E(apolipoprotein E,ApoE)增高是LPG血脂改变的主要特点[2-3]。LPG为一种与脂质代谢紊乱密切相关的肾脏疾病,目前世界范围内有报道的病例不足200例,儿童报道仅10余例[2]。本病进展缓慢,临床常误诊为原发性肾病综合征[4]。因此,为增强对LPG的认识,提高诊治水平,现分析1例确诊的儿童LPG临床资料,总结LPG的临床特点、诊断、治疗及预后。     

人精子体外氧化损伤对线粒体tRNA~(LeuUUR)基因的影响

目的:探讨活性氧(ROS)对人类精子线粒体tRNALeuUUR基因的氧化损伤。方法:采用Percoll梯度离心法筛选具有正常生理功能的精子,作为正常精子模型,并分为损伤组20例和对照组20例,分别加入次黄嘌呤-黄嘌呤氧化酶体系或不予处理,37℃有氧环境中孵育60min。分别提取精子DNA,以Fpg酶切损伤碱基并采用接头介导PCR(LM-PCR)检测线粒体tRNALeuUUR基因的氧化损伤。采用Rhodamine(Rh123)荧光探针标记精子,通过流式细胞仪检测线粒体膜电位,观察精子的功能。结果:与对照组相比,损伤组精子孵育后线粒体膜电位明显降低[(116.27±11.72)%vs(64.00±4.88)%,P<0.05]。Fpg酶切和LM-PCR显示精子线粒体tRNALeuUUR基因损伤。结论:ROS可能通过对精子线粒体tRNALeuUUR基因氧化损伤而影响精子功能(线粒体膜电位明显降低),从而引起不育。