Personal comorbidities and their subsequent risks for liver,gallbladder and bile duct cancers

Many environmental risk factors for hepatobiliary cancers are known but whether they are associated with specific cancer types is unclear. We present here a novel approach of assessing standardized incidence ratios (SIRs) of previously diagnosed comorbidities for hepatocellular carcinoma (HCC), gallbladder cancer (GBC), cholangiocarcinoma (CCA) and ampullary cancer. The 13 comorbidities included alcohol and nonalcohol related liver disease, chronic obstructive pulmonary disease, gallstone disease, viral and other kinds of hepatitis, infection of bile ducts, hepatic and other autoimmune diseases, obesity and diabetes. Patients were identified from the Swedish Inpatient Register from 1987 to 2018, and their cancers were followed from 1997 onwards. SIRs for HCC were 80 to 100 in men and women diagnosed with hepatitis C virus and they were also >10 in patients diagnosed with hepatitis B virus, other kind of hepatitis, hepatic autoimmune disease and nonalcohol related liver disease. Many of these risks, as well as alcohol related liver disease, were either specific to HCC or were shared with intrahepatic CCA. For GBC, CCA and ampullary cancer infection of bile ducts was the main risk factor. Gallstone disease, nonhepatic autoimmune diseases and diabetes were associated with all hepatobiliary cancers. The limitations of the study include inability to cover some rare risk factors and limited follow-up time. Many of the considered comorbidities are characterized by chronic inflammation and/or overt immune disturbance in autoimmune diseases. The results suggest that local chronic inflammation and a related immune disturbance is the carcinogenic trigger for all these cancers.     

乳腺癌随访和药物副作用管理策略的研究进展

乳腺癌是全球女性发病率最高的恶性肿瘤,随着诊断技术及治疗手段的不断提高,乳腺癌患者接受系统治疗后生存期明显延长。乳腺癌患者需要长时间的随访以便及早监测到局部复发和远处转移。此外,激素敏感性乳腺癌患者还需要给予5~10年的内分泌治疗,针对其副作用的全程管理也需要随访,进而提高患者的生活质量,减轻患者疾病负担。本文就近年来乳腺癌随访相关方面进行简要综述。     

Experimental study on the biocompatibility and osteogenesis induction ability of PLLA/DDM scaffolds

Odontology - To investigate the characterization and function of a novel porous osteogenic material (PLLA / DDM) containing polylactic acid and demineralized dentin matrix. The surface morphology...     

医疗领域人工智能应用的研究进展＊

人工智能引发了医疗领域的数字革命，在推动行业发展方面具有极大潜力。本研究围绕临床诊疗、医学研究和公共卫生三个基本场景，聚焦人工智能与传统中医药的交叉与融合，并着重介绍人工智能在疾病诊断、决策支持、医学研究以及重大公共卫生事件中的应用。虽然人工智能在诸多方面显示出独特优势，但仍存在透明度不高、缺乏安全性评估和相关法律法规监管等问题需要谨慎解决，以促进人工智能技术在医疗领域的推广。     

七叶一枝花甾体皂苷种类及含量差异研究进展＊

重楼是我国传统的名贵中药，七叶一枝花为历版《中国药典》收载的重楼药材两个基原植物之一，具有清热解毒、消肿止痛、活血化瘀等功效，其主要活性成分为甾体皂苷类物质。由于重楼市场需求量剧增和七叶一枝花成药时间较长，导致大量依靠人工种植。然而，人工种植过程中产地、采收年限和采收月份都会影响七叶一枝花的甾体皂苷含量。本文系统地总结了七叶一枝花中主要活性成分甾体皂苷的种类，以及不同产地、不同生长年限及不同采收月份的七叶一枝花根茎中甾体皂苷含量差异的研究现状，以期为进一步研究七叶一枝花品质差异、野生资源驯化及规范化种植提供参考。     

血清维生素D水平对维持性血液透析患者下肢肌力减退的预测作用

目的探讨维持性血液透析(MHD)患者血清维生素D水平对下肢肌力减退的预测作用。 方法横断面研究设计，选择2018年9月至10月于战略支援部队特色医学中心血液净化中心的95例MHD患者，检测其血清25-羟维生素D₃[25(OH)D₃]水平，采用5次站立-坐下实验(5-STS)评价其下肢肌力。根据5-STS完成时间将MHD患者分为下肢肌力正常组(n＝85)与减退组(n＝10)，比较两组患者人口学特征、实验室指标。采用多因素Logistic回归分析下肢肌力减退的影响因素，绘制受试者工作特征(ROC)曲线分析上述因素预测MHD患者发生下肢肌力减退的特异度和敏感度。 结果95例MHD患者血清25(OH)D₃水平为11.00~99.50 nmol/L，中位数31.23(19.90~43.30)nmol/L；5-STS完成时间为3.55 s~18.71 s，中位数9.81(7.12，12.43)s，下肢肌力减退者10例(10.53%)。多因素Logistic回归分析显示，血清25(OH)D₃是MHD患者下肢肌力减退的保护性因素[OR＝0.761，95%CI(0.592~0.978)，P＝0.033]。进一步ROC曲线分析显示，25(OH)D₃对应的ROC曲线下面积为0.815，其预测MHD患者发生下肢肌力减退的敏感度为80.00%，特异度为80.00%。 结论MHD患者血清25(OH)D₃水平普遍较低，下肢肌力减退者更为明显；血清维生素D水平对MHD患者是否存在下肢肌力减退具有较好的预测价值。     

痛情绪相关神经递质及神经环路研究进展＊

痛情绪是指因疼痛引发的情绪和情感体验，是疾病过程中最常见的一种情绪。痛情绪相关神经机制非常复杂，但主要与单胺类神经递质、神经肽和某些神经环路有关，笔者将结合目前研究现状分别从以上两方面展开，就痛情绪相关单胺类神经递质和神经肽在受体分类、脑区通路、共疾病以及各神经递质之间的联系和痛情绪相关神经环路中各个蛋白的作用机制等方面进行探讨。     

基于生物信息学的肥厚型心肌病易感基因TNNC1单核苷酸多态性致病表型分析

肥厚型心肌病(HCM)是一种常见的遗传性心脏病,是青少年及年轻运动员心源性猝死的最主要原因之一,其分子遗传学基础为基因突变。TNNC1是HCM的重要致病基因之一,目前仅发现其少量非同义单核苷酸多态性(nsSNPs)位点与HCM发病相关,但该基因其他nsSNPs数量较多,结合临床进行实验遗传检测其基因型与表型的关系,工作量巨大,尚不可行。因此,采用生物信息学方法,从dbSNP数据库中筛选出TNNC1基因全部的nsSNPs,联合4款(Mutation Taster、PolyPhen-2、PhD-SNP和MutPred)专业软件,进行有害性分级筛选和致病关联预测,最后进行突变蛋白三维结构建模及可视化分析。结果显示,首次从TNNC1基因102个nsSNPs位点中预测出疾病相关的18个(G159D、S69R、P52R、D149G、D3V、G140E、N51K、D151V、M47R、G110C、A23D、G140R、K158N、C35Y、R147C、L48P、F74C和V44G)高风险nsSNPs。基于生物信息学方法,以TNNC1基因的nsSNPs为示范,分析其nsSNPs与疾病表型的关系,这为其他遗传疾病致病基因nsSNPs的关联分析打下理论研究基础,具有重要的参考价值。