血清IL-6和胃蛋白酶原Ⅰ/Ⅱ比值对老年胃癌患者预后评估的价值

目的探究老年胃癌患者术前血清白细胞介素6 (IL-6)、胃蛋白酶原(PG)Ⅰ、Ⅱ及PG-Ⅰ/Ⅱ比值在患者预后评估中的意义。方法选择2016年1月—2017年1月南通大学附属海安医院收治的101例外科手术治疗的老年胃癌患者为研究对象。收集患者的临床病理资料,检测血清IL-6和PG-Ⅰ/Ⅱ水平。以全因死亡为随访终点,使用Kaplan-Meier曲线明确患者预后与各指标间的关系。结果受试者工作特征曲线提示,IL-6、PG-Ⅰ和PG-Ⅰ/Ⅱ比值在最佳切割值为10.23pg/mL、30.65μg/L和2.44时,可评估患者的预后,而PG-Ⅱ则无法评估患者的预后。与IL-6≤10.23pg/mL组相比,IL-6> 10.23pg/mL组的肿瘤细胞分化更差、TNM分期Ⅲ期比例明显升高。Kaplan-Meier曲线结果表明,IL-6> 10.23pg/mL组的中位生存期显著短于IL-6≤10.23pg/mL组(28个月vs47个月,P<0.001);PG-Ⅰ≤30.65μg/L组的中位生存期显著短于PG-Ⅰ>30.65μg/L组(34个月vs49个月,P=0.008);PG-Ⅰ/Ⅱ≤2.44组的中位生存期显著短于PG-Ⅰ/Ⅱ> 2.44组(29个月vs56个月,P=0.02)。多因素Cox回归分析表明,肿瘤分化程度、TNM分期、IL-6及PG-Ⅰ/Ⅱ比值是影响患者生存率的独立危险因素。结论术前检测IL-6及PG-Ⅰ/Ⅱ比值有助于评估老年胃癌患者的预后。     

Analyses of rare predisposing variants of lung cancer in 6,004 whole genomes in Chinese

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Renal NF-κB activation impairs uric acid homeostasis to promote tumor-associated mortality independent of wasting

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Comparing Radiomics features of tumour and healthy liver tissue in a limited CT dataset: A machine learning study

IntroductionLiver cancer lesions on Computed Tomography (CT) withholds a great amount of data, which is not visible to the radiologists and radiographer. Radiomics features can be extracted from the lesions and used to train Machine Learning (ML) algorithms to predict between tumour and liver tissue. The purpose of this study was to investigate and classify Radiomics features extracted from liver tumours and normal liver tissue in a limited CT dataset.MethodsThe Liver Tumour Segmentation Benchmark (LiTS) dataset consisting of 131 CT scans of the liver with segmentations of tumour tissue and healthy liver was used to extract Radiomic features. Extracted Radiomic features included size, shape, and location extracted with morphological and statistical techniques according to the International Symposium on Biomedical Imaging manual. Relevant features was selected with chi2 correlation and principal component analysis (PCA) with tumour and healthy liver tissue as outcome according to a consensus between three experienced radiologists. Logistic regression, random forest and support vector machine was used to train and validate the dataset with a 10-fold cross-validation method and the Grid Search as hyper-parameter tuning. Performance was evaluated with sensitivity, specificity and accuracy.ResultsThe performance of the ML algorithms achieved sensitivities, specificities and accuracy ranging from 96.30% (95% CI: 81.03%–99.91%) to 100.00% (95% CI: 86.77%–100.00%), 91.30% (95% CI: 71.96%–98.93%) to 100.00% (95% CI: 83.89%–100.00%)and 94.00% (95% CI: 83.45%–98.75%) to 100.00% (95% CI: 92.45%–100.00%), respectively.ConclusionML algorithms classifies Radiomics features extracted from healthy liver and tumour tissue with perfect accuracy. The Radiomics signature allows for a prognostic biomarker for hepatic tumour screening on liver CT.Implications for practiceDifferentiation between tumour and liver tissue with Radiomics ML algorithms have the potential to increase the diagnostic accuracy, assist in the decision-making of supplementary multiphasic enhanced medical imaging, as well as for developing novel prognostic biomarkers for liver cancer patients.     

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。     

北京市三级甲等医院结直肠癌患者就医流向规律分析

目的 分析不同特征的结直肠癌患者就医行为选择在中医院（含中西医结合医院）、西医院及肿瘤专科医院间的差异，为合理引导结直肠癌患者适宜就医及制订相关政策提供依据。方法 收集北京地区2018年1月-2019年12月17家三级甲等医院21894例首诊结直肠癌成年住院患者的病案首页数据，采用EmpowerStats 2.0对数据进行描述性分析。结果 21894例结直肠癌患者中就诊于中医院的有862例（3.93%），西医院的有8723例（39.85%），肿瘤专科医院的有12309例（56.22%）。对于不同医疗机构，男性占比均大于女性，58-68岁患者占比最大。且结直肠癌患者年龄、医疗付款方式及肿瘤分期在不同医疗机构间的分布存在差异（P<0.001）。西医院及肿瘤专科医院结直肠癌Ⅲ期患者占比最大，而就诊于中医院患者中结直肠癌Ⅳ期最多。从地域分布来看，异地就诊比例（57.32%）大于本地，且就诊于肿瘤专科医院的患者中73.66%来自外地。患者来源前三名分别是北京市、河北省及内蒙古自治区。而在北京市内，西医院患者主要来源于朝阳区、海淀区及西城区，中医院患者主要来源于海淀区、朝阳区及丰台区，肿瘤专科医院则主要来源于朝阳区、海淀区及丰台区。结论 应大力倡导年轻以及早期结直肠癌患者向中医院分流，充分施展中医药在结直肠癌患者中的治疗优势；发挥三级医院带动作用，建立对口帮扶医院，减少不必要的跨省流动及提倡结直肠癌的早筛早治，以降低结直肠癌死亡率。     

乙肝相关性肝癌临床病理学特征与高敏C反应蛋白和溶血磷脂酸表达的相关性

目的探讨乙肝相关性肝癌临床病理学特征与溶血磷脂酸(LPA)和高敏C反应蛋白(hs-CRP)表达的相关性。方法选取2019年1月至2020年1月间河南省驻马店市中心医院收治的198例乙肝相关性肝癌患者作为乙肝组,198例酒精相关性肝癌患者作为酒精组。两组患者都进行血清hs-CRP和LPA表达检测,调查患者的病理学特征并进行相关性分析。结果乙肝组患者血清hs-CRP和LPA含量均高于酒精组,差异均有统计学意义(均P <0.05)。两组患者血清ALP、AFP、ALT、AST和GGT含量比较,差异均无统计学意义(P> 0.05)。乙肝组不同临床分期和组织学分化患者的血清hs-CRP和LPA含量比较,差异均有统计学意义(均P <0.05)。乙肝组患者的临床分期和组织学分化与血清hs-CRP和LPA表达均存在相关性,差异均有统计学意义(均P <0.05)。患者的临床分期和组织学分化均为影响hs-CRP和LPA表达的重要因素,差异均有统计学意义(均P <0.05)。结论相对于酒精相关性肝癌,乙肝相关性肝癌的血清hs-CRP和LPA呈现高表达,与患者的临床病理学特征存在相关性。     

Use and Applicability of the Gail Model to Calculate Breast Cancer Risk: A Scoping Review

Objective: To perform a scoping review of the applicability of the Gail model in different countries for different ethnicities. Methods: The review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist and search strategies based on the PICOS approach. The reviewed articles were included if they were published between 2013 and 2018 in English, Portuguese, or Spanish; were original articles available in full online; and described the use of the Gail model. The PubMed, Embase, and Web of Science data bases were searched. Results: A total of 38 articles eligible for analysis were identified, of which 16 used the Gail model to assess breast cancer risk in women, eight analyzed the applicability of this tool in their population, seven compared the tool and/or modified it according to the specific risk factors of their population, and seven cited the model in determining eligibility for chemoprevention. Conclusion: The Gail model has different applicabilities Greater effectiveness and breast cancer risk are found in developed countries.