Genetic and epigenetic alterations of the EGFR and mutually independent association with BRCA1, MGMT,and RASSF1A methylations in Vietnamese lung adenocarcinomas

Genetic and epigenetic alterations importantly contribute to the pathogenesis of lung cancer. In the study, we measured the frequency and distribution of molecular abnormalities of EGFR as well as the aberrant promoter methylations of BRCA1, MGMT, MLH1, and RASSF1A in Vietnamese lung adenocarcinomas. We investigated the association between genetic and epigenetic alteration, and between each abnormality with clinicopathologic parameters. Somatic EGFR mutation that was found in 49/139 (35.3%) lung adenocarcinomas showed a significant association with young age, female gender, and non-smokers. EGFR overexpression was identified in 82 tumors (59.0%) and statistical relationships with EGFR or BRCA1 methylation but not EGFR mutation. In addition, EGFR, BRCA1, MGMT, MLH1, and RASSF1A methylations were found in 33 (23.7%), 41 (29.5%), 46 (33.1%), 28 (20.1%), and 41 (29.5%) cases of a total of 139 lung adenocarcinomas, respectively. The RASSF1A methylation was found to be linked to the smoking habit. Methylations in MGMT and RASSF1A were also found to correlate with metastasis status. Furthermore, the distribution of EGFR mutation and that of BRCA1, MGMT or RASSF1A methylation were significantly exclusive in lung adenocarcinomas. The main finding of our study demonstrate that epigenetic abnormalities might play a critical role for the lung tumorigenesis in patients with smoking history and metastasis, and partly affect the predictive value of EGFR mutations through blocking expression due to promoter EGFR hypermethylation. Mutually exclusive distribution of genetic and epigenetic alterations reflects differently biological characteristics in the etiology of lung adenocarcinomas.     

T3、T4期结直肠癌淋巴结转移危险因素分析

目的探讨T3、T4期结直肠癌患者淋巴结转移危险因素,为临床诊疗提供参考。方法回顾性分析2008年1月至2017年12月在空军军医大学西京消化病医院行结直肠癌根治术的1112例T3、T4期结直肠癌患者的临床病理资料,分析淋巴结转移状态与临床病理因素及肿瘤标志物的相关性,应用logistic多因素回归法分析淋巴结转移的相关危险因素。结果单因素分析结果显示,性别、年龄、肿瘤部位分层的结直肠癌患者间淋巴结转移率差异均无统计学意义(均P>0.05),淋巴结转移率在不同肿瘤长径[<5 cm和≥5 cm分别为37.75%(211/559)、52.26%(289/553),χ^2=23.666,P<0.01]、大体类型[浸润、溃疡、蕈伞、隆起分别为37.04%(20/54)、47.52%(432/909)、34.33%(23/67)、69.51%(57/82),χ^2=13.787,P=0.003]、分化程度[高、中、低分化分别为34.11%(102/299)、49.00%(317/647)、48.80%(81/166),χ^2=19.771,P<0.01]、错配修复缺陷(dMMR)[是和否分别为26.34%(64/243)、50.17%(436/869),χ^2=43.996,P<0.01]、神经侵犯[是和否分别为48.17%(421/874)、33.20%(79/238),χ^2=16.954,P<0.01]、脉管侵犯[是和否分别为79.16%(338/427)、23.65%(162/685),χ^2=327.493,P<0.01]以及术前癌胚抗原(CEA)[阳性(≥5 mg/ml)和阴性(<5 mg/ml)分别为52.87%(249/471)、39.16%(251/641),χ^2=20.162,P<0.01]和CA199[阳性(≥35 U/ml)和阴性(<35 U/ml)分别为59.33%(124/209)、41.64%(376/903),χ^2=21.465,P<0.01]分层患者间差异均有统计学意义。logistic多因素回归分析显示,脉管侵犯和术前CA199阳性是T3、T4期结直肠癌患者淋巴结转移独立危险因素(OR=13.006,95%CI 9.329~17.276,P<0.01;OR=2.194,95%CI 1.513~3.181,P<0.01),dMMR阳性是淋巴结转移的保护性因素(OR=0.279,95%CI 0.190~0.411,P<0.01)。结论脉管侵犯是T3、T4期结直肠癌患者淋巴结转移的主要危险因素。术前肿瘤标志物CA199的检测可以作为预测T3、T4期结直肠癌患者淋巴结转移状态的指标,一定程度上可为诊疗方案的制订提供参考。     

40岁以上高龄女性体外受精-胚胎移植妊娠结局分析

目的：探讨40岁以上高龄女性体外受精-胚胎移植（IVF-ET）的妊娠结局，旨在为高龄女性提供生育咨询以及为改善高龄女性个体化辅助生殖治疗结局提供临床依据。方法：选择我院生殖中心2015年1月—2017年12月女方年龄≥40岁且使用自身卵子行体外受精的共2 467个治疗周期资料，对各项临床数据进行回顾性分析。结果：40岁及以上行辅助生殖治疗的患者，随着女性年龄增加获卵数明显减少（40～48岁女性平均获卵数分别为2.97、 2.69、2.17、2.01、1.77、1.61、1.68、1.29和1.00，44～48岁与40～43岁依次组间比较均P＜0.05），尤其是44岁以上女性胚胎发育潜能明显降低（40～48岁囊胚形成率分别为48.90%、43.72%、33.67%、34.29%、24.39%、21.14%、26.32%、16.67%和0%，44～48岁与40～43岁组间依次比较均P＜0.05）。共有518个周期行新鲜胚胎移植，结果显示，随女性年龄增加，临床妊娠率（40～48岁临床妊娠率分别为26.92%、21.15%、20.79%、10.96%、18.87%、11.11%、5.88%、0%和0%，43～48岁与40～42岁组间依次比较均P＜0.05）、种植率（40～48岁种植率分别为23.65%、19.51%、17.70%、8.54%、7.49%、10.81%、5.56%、0%和0%，43～48岁与40～42岁组间依次比较均P＜0.05）和活产率均显著降低（40～46岁活产率分别为18.46%、10.58%、9.90%、5.48%、5.66%、2.78%和5.88%，43～46岁与40～42岁组间依次比较均P＜0.05），43岁以上者结局更差。44岁以上女性自然流产率明显增高（40～45岁流产率分别为31.43%、50.00%、52.38%、50.00%、70.00%和75.00%，44～45岁与40～43岁组间依次比较均P＜0.05）。46岁女性仅1例妊娠并分娩，47岁和48岁女性均无成功妊娠。与抗苗勒管激素（AMH）＞1.0 ng/mL组相比，AMH≤1.0 ng/mL组妊娠率、种植率及活产率均显著下降（27.04% vs. 14.74%，22.99% vs. 13.50%，15.88% vs. 7.37%；均P＜0.05），流产率明显升高（41.27% vs. 50.00%，P＜0.05）。结论：≥40岁高龄女性随年龄增长生育力逐渐降低。40～43岁年龄段女性助孕仍有一定的价值，尤其是卵巢仍有一定储备者（AMH＞1.0 ng/mL），但44岁以上女性原则上不再建议ART助孕，对于46岁以上卵巢功能衰竭的女性强烈建议卵子捐赠或收养。     

沙库巴曲缬沙坦在心力衰竭治疗中的应用

心力衰竭是一种慢性疾病，是心脏无法泵出足够的血液为其他身体器官提供氧气，从而引发呼吸短促、疲劳和水肿等症状。目前其治疗包括病因治疗、一般治疗、药物治疗及非药物治疗等，其中血管紧张素受体脑啡肽酶抑制剂（ARNI）是当前新研发出的一类抗心衰药物，沙库巴曲缬沙坦是其代表药物，具有阻断肾素-血管紧张-醛固酮系统（RAAS）及抑制脑啡肽酶（NEP）的双重作用，新的欧美指南均推荐用于射血分数降低心力衰竭（HFrEF）的治疗。本文现就沙库巴曲缬沙坦在心力衰竭治疗中的作用机理、临床试验、药物安全性作一综述，旨在为该病的临床治疗提供参考。     

偏瘫性肩痛患者腋囊的MRI观测及临床意义

目的通过MRI技术观察偏瘫患者肩疼痛腋囊形态变化,为影像诊断提供依据。方法对47例偏瘫性肩疼痛患者(疼痛组)和47例正常肩部(对照组),运用MRI技术对肩关节腋囊进行测量,观察腋囊下肱盂韧带信号。结果偏瘫性肩疼痛的腋囊厚度(4.1±1.45)mm,高于对照组。腋囊腔高度(8.70±1.80)mm,腋囊腔宽度(4.31±0.56)mm,小于对照组。偏瘫性肩痛下肱盂韧带呈高信号出现率达21%,腋囊厚度与VAS呈正相关,与肩关节外展外旋呈负相关。结论脑中风中后期肩周炎是引起偏瘫性肩疼痛患者的常见原因,腋囊厚度是限制肩关节活动度的重要因素。     

Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning

Conservation laws are considered to be fundamental laws of nature. It has broad applications in many fields, including physics, chemistry, biology, geology, and engineering. Solving the differential equations associated with conservation laws is a major branch in computational mathematics. The recent success of machine learning, especially deep learning in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods. In this paper, we are the first to view numerical PDE solvers as an MDP and to use (deep) RL to learn new solvers. As proof of concept, we focus on 1-dimensional scalar conservation laws. We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner. We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision-making process, and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach. In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts. Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and supervised learning based approach L3D and PINN, and how well it generalizes.