健脾消癌方通过PI3K/Akt信号通路抑制结肠癌血管生成的研究＊

目的 观察结肠癌HCT116细胞健脾消癌方的条件培养液对HUVEC细胞管腔形成的影响，从PI3K/Akt生物轴调控角度探讨其作用机制。方法 培养HCT116细胞，细胞设3组：对照组，健脾消癌方组（加入15%健脾消癌方含药血清）及人参皂苷Rg3组；制备HCT116细胞健脾消癌方条件培养液（分组及制备方法见实验方法），用条件培养液干预HUVEC（脐静脉内皮细胞，Human Umbilical Vein Endothelial Cells），Matrigel基质胶法检测HCT116细胞健脾消癌方条件培养液对HUVEC小管形成的影响。随后采用蛋白免疫印迹法（Western blot）检测各组HCT116细胞磷脂酰肌醇3-激酶（PI3K）、蛋白激酶B（Akt）、p-Akt、VEGF（血管内皮生长因子，Vascular endothelial growth factor）蛋白表达。最后在结肠癌HCT116荷瘤小鼠中验证健脾消癌方对肿瘤生长速度的影响，并经瘤组织VEGF蛋白表达、CD31免疫组化染色检测肿瘤内血管生成情况。结果 模型组HUVEC细胞管腔形成较空白血清组显著增加（P<0.05）；健脾消癌方组及人参皂苷Rg3组较模型组HUVEC细胞管腔形成显著减少（P<0.01）。p-Akt和VEGF蛋白表达水平模型组高于空白血清组（P<0.05），健脾消癌方组及人参皂苷Rg3组显著低于模型组（P<0.01）；PI3K、Akt蛋白表达量组间差异无统计学意义。与对照组比较，模型组荷瘤小鼠肿瘤体积显著性增大，瘤组织内VEGF表达、CD31阳性面积显著性增加，差异有统计学意义（P<0.05）；与模型组比较，健脾消癌方组及人参皂苷Rg3组荷瘤小鼠肿瘤体积显著减小，瘤组织内VEGF表达、CD31阳性面积降低，差异有统计学意义（P<0.05）。结论 健脾消癌方可抑制肿瘤的血管生成和生长，其作用机制可能与PI3K/Akt生物轴调控VEGF表达有关。     

MSCT在对比剂外渗的急性腹部创伤性动脉损伤诊断中的应用价值

目的探讨MSCT在对比剂外渗的急性腹部创伤性动脉损伤诊断中的应用价值。方法回顾性分析本院2016年1月～2018年12月收治的44例急性创伤性腹部动脉损伤患者及58例腹部非创伤性动脉病变患者影像资料,分析急性腹部创伤性和非创伤性动脉病变所致对比剂外渗的相关性。结果 44例创伤性腹部损伤患者通过MSCT及图像后处理技术共检出59支动脉损伤,其中对比剂外渗34支、非对比剂外渗共25支;58例急性腹部非创伤组共检出58支动脉病变,其中8支存在对比剂外渗。对比剂外渗征象创伤组出现率明显高于非创伤性组(χ~2=24.42,P0.05)。结论与非创伤组相比,急性腹部创伤性动脉损伤对比剂外渗征象较为常见,MSCT可以较好地显示创伤性对比剂外渗的直接及间接征象。     

原发性骶尾部脊索瘤的CT分型及征象

目的对治疗前原发性骶尾部脊索瘤(PSC)CT图像分型,并分析其CT征象,为诊断和个性化治疗提供依据。方法回顾性分析101例PSC患者治疗前的CT图像,包括肿瘤的部位、范围、大小、密度、肿瘤与邻近结构的关系。按照肿瘤的部位由上及下分为Ⅰ~Ⅳ型,并根据肿瘤侵犯的范围从小到大分为a^d亚型。采用Kruskal-Wallis H检验比较PSC各亚型的占比,并对各亚型之间进行两两比较。采用R×C列联表精确概率检验比较分型和亚型肿瘤钙化的发生率。采用单因素方差分析及LSD-t检验对各分型和亚型肿瘤的大小和密度进行分析、比较。结果101例PSC中,Ⅰ~Ⅳ型的发生率分别为17.8%、30.7%、36.6%、14.9%,a^d亚型的占比分别为9.9%、25.7%、58.4%、5.9%。各亚型的占比差异具有统计学意义(P=0.012)。c亚型明显高于a亚型(P=0.039),d亚型明显低于a亚型(P=0.036),其余各型之间无明显差异。各分型肿瘤内钙化的差异无统计学意义(P=0.233);各亚型肿瘤内钙化的差异有统计学意义(P=0.003),a^d亚型肿瘤钙化的比率逐渐增加。Ⅰ型肿瘤的左右径及上下径明显大于Ⅱ~Ⅳ型(P<0.05)。a亚型与b亚型肿瘤之间前后径的差异无统计学意义(P=0.102),b^d亚型之间前后径的差异均有统计学意义(P<0.05);不同亚型肿瘤之间的左右径、上下径之间的差异均有统计学意义(P<0.05),a亚型径线最小,d亚型径线最大。结论101例PSC中,Ⅱ、Ⅲ型最多见,肿瘤较少累及第一骶骨;各亚型中,a型较少见,c亚型最多见,d亚型最少见。肿瘤的密度与分型无关,肿瘤内钙化与亚型有关。Ⅰ型肿瘤侵犯的范围较Ⅱ~Ⅳ型广泛,a^d亚型肿瘤的径线逐渐增大,CT分型有利于判断肿瘤的范围。PSC诊断延迟现象比较明显,但很少发生远处侵犯和转移。CT图像可对治疗前PSC分型,为诊断和个性化治疗提供依据。     

儿科诊断性X线扫描家属知情与接受现状调查

目的:了解建德市基层医院儿科门诊进行X线扫描治疗中,家属对X射线检查的知情同意与接受现状。方法:随机抽取建德市属3家公立综合性医院共60位接受X线扫描的儿童患者家属为调查对象,发放自制调查问卷并统计分析。结果:55位(91.67%)患者表示治疗前主治医生只告知了X射线是诊断性检查,没有具体告知X射线的危害性;18位(30%)的患者家属不清楚辐射会对人体有损害,42位(70%)不清楚或不注意辐射警示标志,更不会主动要求防护措施,学历水平较高者及有从事医务工作背景者接受X射线检查的为12人(20%),明显低于学历水平低者或没有从事医务工作背景者的46人(76.67%),不知可否接受的有2人(3.33%)(P<0.001)。结论:基层医院儿科诊断性X射线扫描前的知情同意告知仍需加强,儿科放射检查偏多,应避免并加强宣传和教育。     

Morphological,Functional, and Tissue Characterization of Silent Myocardial Involvement in Patients With Primary Biliary Cholangitis

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Retrograde Instrumentation of Surgically Resected Roots Using Controlled Memory Files: A Human Cadaver Study

IntroductionThe purpose of this study is to evaluate the amount of residual obturation material of retroinstrumented surgically resected roots using controlled memory files and to evaluate the incidence of adverse treatment outcomes.MethodsThirty maxillary anterior teeth in human cadavers were selected, and nonsurgical root canal treatment was performed on these teeth. A standardized 4-mm osteotomy and a 3-mm root resection with as close to 0° bevel as possible were made on each tooth. A microsurgical diamond tip was used to create a 1- to 2-mm starting point for each retropreparation. A 25/06 and 30/06 VTaper 2H were bent at about 90° angle to mimic the clinical and anatomic restrictions and used to create a retropreparation to a depth of 14 mm. Micro–computed tomography scans were taken and analyzed for volume and percentage of residual obturation material at 5 and 10 mm. In addition, the incidences of instrument separation and crack and ledge formation in the teeth were recorded.ResultsThe median volume of residual obturation at 5 and 10 mm was 0.18 mm³ (interquartile range, 0.36 mm³) and 1.97 mm³ (interquartile range, 1.99 mm³), respectively. The overall incidence of file separation during retropreparation was 13.33% (4/30). Among the cases analyzed with micro–computed tomography, none showed crack or ledge formation.ConclusionsRetroinstrumentation of surgically resected roots using controlled memory files cleans the canal effectively with relatively low adverse treatment outcomes. Although this novel technique is limited in application, it is a safe and effective way to achieve a deep, clean retropreparation.     

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%.