Pharmacokinetic study of the oral fluorouracil antitumor agent S‐1 in patients with impaired renal function

Although dose reduction of S‐1 is recommended for patients with impaired renal function, dose modification for such patients has not been prospectively evaluated. The aim of the present study was to investigate the pharmacokinetic parameters of 5‐fluorouracil, 5‐chloro‐2,4 dihydroxypyridine and oteracil potassium, and to review the recommended dose modification of S‐1 in patients with renal impairment. We classified patients receiving S‐1 into 4 groups according to their renal function, as measured using the Japanese estimated glomerular filtration rate (eGFR) equation. The daily S‐1 dose was adjusted based on the patient's eGFR and body surface area. Blood samples were collected for pharmacokinetic analysis. A total of 33 patients were enrolled and classified into 4 groups as follows: 10 patients in cohort 1 (eGFR ≥ 80 mL/min/1.73 m²), 10 patients in cohort 2 (eGFR = 50‐79 mL/min/1.73 m²), 10 patients in cohort 3 (eGFR = 30‐49 mL/min/1.73 m²), and 3 patients in cohort 4 (eGFR < 30 mL/min/1.73 m²). Those in cohorts 3 and 4 treated with an adjusted dose of S‐1 showed a similar area under the curve for 5‐fluorouracil (941.9 ± 275.6 and 1043.5 ± 224.8 ng/mL, respectively) compared with cohort 2 (1034.9 ± 414.3 ng/mL). Notably, while there was a statistically significant difference between cohort 1 (689.6 ± 208.8 ng/mL) and 2 (P = 0.0474) treated with an equal dose of S‐1, there was no significant difference observed in the toxicity profiles of the cohorts. In conclusion, dose adjustment of S‐1 in patients with impaired renal function using eGFR is appropriate and safe.     

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。     

D6S1043、D12S391基因座与主动攻击行为的关联研究

目的:探讨D6S1043、D12S391基因座的基因多态性对主动攻击行为的影响。方法:应用聚合酶链反应技术结合毛细管电泳法对114例男性主动攻击行为者(研究组)及120名健康男性(对照组)进行D6S1043、D12S391基因座的基因型及等位基因检测,分析D6S1043、D12S391基因座多态性与主动攻击行为的相关性。结果:研究组D6S1043基因座中12~19基因型频率(13.16%)明显高于对照组(1.67%)(P0.05);两组等位基因频率差异无统计学意义;两组D12S391基因座的基因型及等位基因频率差异无统计学意义。结论:D6S1043基因座中12~19基因型可能主动攻击行为有关。     

First Japanese autopsy case showing LRRK2 mutation G2019S and TDP-43 proteinopathy

This is the first Japanese autopsy case of Leucine-rich repeat kinase 2 (LRRK2) G2019S mutation with atypical TDP43 proteinopathy. Our case is important that presented clinically dysphagia and pathologically TDP-43 proteinopathy. TDP43 may play an important role of clinical presentation with LRRK2 G2019S mutation carriers.     

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%.