Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma

PurposeTo compare morphological imaging features and CT texture histogram parameters between grade 3 pancreatic neuroendocrine tumors (G3-NET) and neuroendocrine carcinomas (NEC).Materials and methodsPatients with pathologically proven G3-NET and NEC, according to the 2017 World Health Organization classification who had CT and MRI examinations between 2006-2017 were retrospectively included. CT and MRI examinations were reviewed by two radiologists in consensus and analyzed with respect to tumor size, enhancement patterns, hemorrhagic content, liver metastases and lymphadenopathies. Texture histogram analysis of tumors was performed on arterial and portal phase CT images. images. Morphological imaging features and CT texture histogram parameters of G3-NETs and NECs were compared.ResultsThirty-seven patients (21 men, 16 women; mean age, 56 ± 13 [SD] years [range: 28-82 years]) with 37 tumors (mean diameter, 60 ± 46 [SD] mm) were included (CT available for all, MRI for 16/37, 43%). Twenty-three patients (23/37; 62%) had NEC and 14 patients (14/37; 38%) had G3-NET. NECs were larger than G3-NETs (mean, 70 ± 51 [SD] mm [range: 18 - 196 mm] vs. 42 ± 24 [SD] mm [range: 8 - 94 mm], respectively; P = 0.039), with more tumor necrosis (75% vs. 33%, respectively; P = 0.030) and lower attenuation on precontrast (30 ± 4 [SD] HU [range: 25-39 HU] vs. 37 ± 6 [SD] [range: 25-45 HU], respectively; P = 0.002) and on portal venous phase CT images (75 ± 18 [SD] HU [range: 43 - 108 HU] vs. 92 ± 19 [SD] HU [range: 46 - 117 HU], respectively; P = 0.014). Hemorrhagic content on MRI was only observed in NEC (P = 0.007). The mean ADC value was lower in NEC ([1.1 ± 0.1 (SD)] × 10⁻³ mm²/s [range: (0.91 - 1.3) × 10⁻³ mm²/s] vs. [1.4 ± 0.2 (SD)] × 10⁻³ mm²/s [range: (1.1 - 1.6) × 10⁻³ mm²/s]; P = 0.005). CT histogram analysis showed that NEC were more heterogeneous on portal venous phase images (Entropy-0: 4.7 ± 0.2 [SD] [range: 4.2-5.1] vs. 4.5 ± 0.4 [SD] [range: 3.7-4.9]; P = 0.023).ConclusionPancreatic NECs are larger, more frequently hypoattenuating and more heterogeneous with hemorrhagic content than G3-NET on CT and MRI.     

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%.     

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。     

薄层色谱法在当前中药质量标准中的应用探讨

薄层色谱鉴别在历版《中国药典》中的应用经历了从无到有、从少到多的过程；而薄层扫描含量测定在最近几版《中国药典》中的应用比例逐渐降低。随着对中药质量标准体系要求的进一步提高，薄层色谱法的不足之处陆续显现，如仪器普及率低、设备并不简单、结果重复性和稳定性较差、鉴别速度及准确性不及高效液相色谱法、展开剂毒性大等，逐渐不合时宜。在制定中药质量标准时，研究者不应该墨守成规，薄层色谱鉴别也不应该是雷打不动的定性鉴别必备选项。高效液相色谱法具备完全取代薄层色谱法的可行性，薄层色谱法可作为高效液相色谱法的补充。为充分降低检测成本、缩短检测周期、提高鉴别效率，笔者建议中药质量标准体系应该大幅减少薄层色谱鉴别方法的应用，增加高效液相特征图谱鉴定，尽量做到“一个条件，一张图谱”；除非确有必要，《中国药典》等国家质量标准体系应将薄层色谱鉴别作为推荐方法，而非强制标准。     

浙江省2007－2017年罕见病住院病例特征分析

目的 分析2007-2017年浙江省24 388例罕见病住院病例特征，为制定罕见病防控策略提供依据。方法 收集2007-2017年浙江省10家三级甲等（三甲）医院罕见病住院病例资料和各年度住院数，进行描述性统计分析。结果 罕见病病例共24 388例，占住院总例数的2.69‰（24 388/9 054 201），病例数居前3位的疾病类型依次为"血液和造血器官疾病以及某些涉及免疫机能的异常"（占32.81%，8 001/24 388）、"先天性畸形、变形和染色体异常"（占24.87%，6 065/24 388）和"神经系统疾病"（占19.01%，4 635/24 388）；2007-2017年罕见病病例数呈逐年增长趋势，年均增幅19.69%，而罕见病例数占同期住院总例数比例仅在2016-2017年明显上升，各类型罕见病时间分布呈不同特征；罕见病的病例数男女性别比为1.35：1（13 990/10 398），男女性别比最高的3类疾病依次为"消化系统疾病"（4.45：1，1 180/265）、"损伤、中毒和外因的某些其他后果"（3.51：1，281/80）和"神经系统疾病"（2.26：1，3 213/1 422）；各年龄段罕见病类型、各类型罕见病年龄分布均呈不同特征；病例数居前10位的疾病占全部罕见病例数的53.55%（13 060/24 388），其中前3位疾病分别为成人粒细胞缺乏症（14.41%，3 515/24 388）、皮质基底核退化症（7.60%，1 854/24 388）和亨诺克-舍恩莱因紫癜（6.01%，1 466/24 388）。结论 本研究分析的浙江省2007-2017年24 388例罕见病住院病例的特征资料，是推动我国罕见病的研究、监测或登记数据库构建、制定防控策略的参考依据。     

Biopharmaceutics classification and intestinal absorption of chikusetsusaponin IVa

The objective of this study was to investigate the absorption behavior of chikusetsusaponin IVa (CHS‐IVa) in the rat intestine using single‐pass intestinal perfusion (SPIP) and to classify CHS‐IVa into the biopharmaceutics classification system (BCS). The equilibrium solubility of CHS‐IVa was determined by the shaker method. The absorption mechanism of CHS‐IVa in the intestine was studied by comparing the P_eff of different concentrations of CHS‐IVa. The intestinal site dependence of CHS‐IVa absorption was studied by comparing the P_eff of the same concentration of CHS‐IVa in different intestinal segments. The relationship between CHS‐IVa and intestinal efflux protein was studied by perfusion with an efflux protein inhibitor. The permeability of CHS‐IVa was investigated by comparing the P_eff of CHS‐IVa and the reported value. The solubility of CHS‐IVa over the pH range 1.0–7.5 was 14.4 ± 0.29 to 16.9 ± 0.34 mg/ml. The P_eff of CHS‐IVa in the duodenum was 1.76 × 10^?3 to 2.00 × 10^?3 cm/min. The P_eff of CHS‐IVa in the jejunum was 1.26 × 10^?3 to 1.39 × 10^?3 cm/min. The P_eff of CHS‐IVa in the ileum was 1.25 × 10^?3 to 1.31 × 10^?3 cm/min. The P_eff of CHS‐IVa in the colon was 1.02 × 10^?3 to 1.08 × 10^?3 cm/min. There was no statistical difference of the P_eff in the four segments at different CHS‐IVa concentrations. The P_eff of CHS‐IVa (0.07, 0.7 and 7.0 mg/ml) were all notably smaller than the reported P_eff (3.00 × 10^?3 cm/min) in the jejunum. The P_eff of CHS‐IVa was not influenced by verapamil (P‐gp inhibitor), indomethacin (MRP inhibitor) and pantoprazole (BCRP inhibitor). CHS‐IVa was classified as high solubility, low permeability and BCS III. The main absorptive tracts were the upper intestinal tracts and the rank order of intestinal permeability was duodenum > jejunum ≈ ileum > colon. The transport mechanism of CHS‐IVa in all intestinal segments might be primarily passive transport. CHS‐IVa was not a substrate of P‐gp, MRP and BCRP.