基于全基因组测序的耐碳青霉烯类抗生素鲍曼不动杆菌耐药基因、毒力因子及同源性分析

摘要：目的 通过应用全基因组测序(whole genome sequencing, WGS)技术分析某三级医疗机构耐碳青霉烯类抗生素鲍曼不 动杆菌(carbapenem-resistant Acinetobacter baumannii, CRAB)的耐药基因、毒力因子及同源性。方法 收集该院2020年1月至3月 重症监护病房(Intensive care unit, ICU)、神经外科分离的11株医院感染CRAB菌株，通过二代测序平台进行全基因组测序, 应用 基因组流行病学中心(Center for genomic epidemiology, CGE)ResFinder 4. 0软件分析其耐药基因型，并应用MORPHEUS在线制作 热图，应用毒力因子数据库(virulence factors of pathogenic bacteria, VFDB)VFanalyzer软件筛选毒力因子，应用MLST软件检测菌 株的ST型，应用Kaptive软件检测荚膜型，应用CSI Phylogeny 1. 4软件及FigTree v1. 4. 4软件构建最大似然树(maximum likelihood tree, MLT)以分析其同源性。结果 11株CRAB对亚胺培南、美罗培南、头孢菌素、环丙沙星均呈现耐药，而对阿米卡星、左氧 氟沙星耐药的菌株株数较少。11株CRAB共检测出18种耐药基因，11株同时携带碳青霉烯酶耐药基因blaOXA-23和blaOXA-66，β-内酰 胺酶耐药基因以blaTEM-1D和blaADC-25为主，大部分菌株携带多种外排泵相关耐药基因及抗菌药物修饰酶耐药基因。11株CRAB均携 带多种毒力因子，包括外膜孔蛋白、脂多糖、生物膜、外排泵、磷脂酶和效应蛋白等，如OmpA、Lps、Csu、Pga、Ade、Plc、 Bas、Bau、Ent、Hem、Aba、Bfm、Pbp和Kat等。11株CRAB均为ST2-K22型，同源性分析结果显示C组内同源性关系相近，存 在院内传播的可能。结论 该院CRAB的耐药性、毒力特征复杂多样，同源性分析显示该院存在1种优势克隆株，该克隆株有医 院内传播的风险。     

慢性疼痛中枢调控网络的研究现状与思考＊

慢性疼痛是一种复杂的身心疾病，包括躯体痛觉异常、认知障碍、负性情绪等多个方面的改变，同时伴随着神经系统的功能以及结构的改变。本文将对慢性疼痛与下行疼痛调节通路、疼痛情感-认知调控网络以及中脑边缘奖赏网络的相关性，以及针刺镇痛的中枢机制相关研究文献进行综述，旨在探讨下行疼痛调节通路、疼痛情感-认知调控网络以及中脑边缘奖赏网络在慢痛发生机制中的作用，为临床治疗慢性疼痛类疾病提供更优势的治疗方案。     

Risk as a pattern over time: Delineation of time-dependent risk factors in biological,psychological, and social variables in cancer patients

Background

Survival in cancer patients is associated with a multitude of biological, social, and psychological factors. Although it is well established that all these factors add to overall mortality, it is not well understood how the predictive power of these parameters changes in a comprehensive model and over time.

Methods

Patients who attended the authors’ outpatient clinic were invited to participate. The authors followed 5180 mixed cancer patients (51.1% female; mean age, 59.1 years [SD = 13.8]) for up to 16 years and analyzed biological (age, sex, cancer site, anemia), psychological (anxiety, depression), and social variables (marital status, education, employment status) potentially predicting overall survival in a Cox proportional hazards model.

Results

The median survival time for the entire sample was 4.3 years (95% confidence interval, 4.0–4.7). The overall survival probabilities for 1 and 10 years were 76.8% and 38.0%, respectively. Following an empirical approach, the authors split the time interval into five periods: acute, subacute, short-term, medium-term, and long-term. A complex pattern of variables predicted overall survival differently in the five periods. Biological parameters were important throughout most of the time, social parameters were either time-independent predictors or tended to be more important in the longer term. Of the psychological parameters, only depression was a significant predictor and lost its predictive power in the long-term.

Conclusions

The findings of this study allow the development of comprehensive patient-specific models of risk and resilience factors addressing biopsychosocial needs of cancer patients, paving the way for a personalized treatment plan that goes beyond biomedical cancer care.