尘肺病患者心理、社会支持状况调查及相关性研究

目的为了解尘肺病患者心理状况及社会支持对患者心理健康的影响,为对尘肺病患者建立干预措施提供技术支撑。方法采用SCL-90症状清单、社会支持量表对在全省尘肺病住院病人较多的3家医院进行尘肺病患者问卷调查。结果 208名尘肺病患者男性205人,女性3人。文化程度均为高中及以下;Ⅰ期尘肺148人,Ⅱ期尘肺41人,Ⅲ期尘肺19人;尘肺患者SCL-90症状清单总分为162.00±64.77,低于全国常模,差异有统计学意义(P0.05);不同分期、不同年齡段、不同病程尘肺病患者各组之间社会支持得分差异无统计学意义。结论尘肺病患者心理健康水平低于全国水平,社会支持各维度得分与SCL-90各因子得分呈负相关,建立尘肺病患者心理干预体系十分必要。     

AXL表达与乳腺癌预后及临床病理特征关系的Meta分析

目的 探讨受体酪氨酸激酶AXL蛋白的表达对乳腺癌患者预后及临床病理特征的意义。方法 在中国知网(CNKI)、万方医学网(Wanfang Data)、 Cochrane Library、Embase、PubMed和Medline数据库中,搜索国内外公开发表的有关AXL与乳腺癌预后关系的文献,检索时限均为从建库至2019年10月。2位研究者按纳入和排除标准独立筛选文献、提取资料和评价纳入研究的偏倚风险,并进行Meta分析。结果 共纳入7篇文献,包括933例乳腺癌患者。AXL高表达组的乳腺癌患者总生存期(overall survival,OS)明显低于低表达组(HR=2.36,95%CI 1.60~3.46,P<0.000 1),但其表达在无复发生存期(relapse free survival,RFS)和无疾病生存期(disease free survival,DFS)中差异无统计学意义,且与乳腺癌的年龄、临床分期、组织分级、淋巴结转移及受体表达的相关性分析无统计学意义。结论 AXL的阳性表达与乳腺癌患者预后不良有关,是乳腺癌个体化治疗的潜在分子标志物。     

封场生活垃圾填埋场好氧治理通风系统计算与应用

从质量守恒定律出发,以Buswell提出的垃圾好氧降解反应方程式为基础,建立了计算通风量的化学计量模型;以达西定律为基础,建立了计算通风压力的数学模型;并将其应用于某封场垃圾填埋场原位好氧治理工程的计算,验证了2个模型的合理性和准确性,可为生活垃圾填埋场好氧生物反应器工艺设计和设备选型提供理论数据参考。     

The influence of processed total non-forward and non-motile sperm count on the outcome of artificial insemination with the husband’s semen

BackgroundArtificial insemination with the husband’s semen (AIH) is an economical and noninvasive method of infertility treatment. However, AIH’s pregnancy rate is much lower than in vitro fertilization (IVF) as its multiple and complex uncertainty factors. Semen quality has been one of the main factors which affect the pregnancy outcome of AIH.MethodsThe relevant parameters of 1,142 AIH cycles were retrospectively studied, including the general parameters and the semen quality parameters among clinical pregnancy, biochemical pregnancy, non-pregnancy group, age, infertility duration, infertility type, body mass index (BMI), cycle count, morphology in previously semen examination, and semen quality parameters on the day of AIH.ResultsThe statistically significant difference was only found on processed total non-forward and non-motile sperm count (N-TFMSC). The mean processed N-TFMSC in the biochemical pregnancy group was 6.37±4.27 million, significantly higher than the other two groups (vs. 4.40±3.15 million or vs. 4.48±3.60 million, P<0.05). The study was then divided into two groups according to processed N-TFMSC, Group 1 ≤5.0 million, and Group 2 >5.0 million. A statistical increase in biochemical pregnancy rate was observed when the processed N-TFMSC was >5.0 million (2.72% vs. 0.90%).ConclusionsProcessed N-TFMSC may be one of the independent factors on AIH’s outcome; it should be given equal attention the same as processed total forward motile sperm count (TFMSC).     

Genetic toxicity assessment using liver cell models: past,present, and future

ABSTRACT

Genotoxic compounds may be detoxified to non-genotoxic metabolites while many pro-carcinogens require metabolic activation to exert their genotoxicity in vivo. Standard genotoxicity assays were developed and utilized for risk assessment for over 40 years. Most of these assays are conducted in metabolically incompetent rodent or human cell lines. Deficient in normal metabolism and relying on exogenous metabolic activation systems, the current in vitro genotoxicity assays often have yielded high false positive rates, which trigger unnecessary and costly in vivo studies. Metabolically active cells such as hepatocytes have been recognized as a promising cell model in predicting genotoxicity of carcinogens in vivo. In recent years, significant advances in tissue culture and biological technologies provided new opportunities for using hepatocytes in genetic toxicology. This review encompasses published studies (both in vitro and in vivo) using hepatocytes for genotoxicity assessment. Findings from both standard and newly developed genotoxicity assays are summarized. Various liver cell models used for genotoxicity assessment are described, including the potential application of advanced liver cell models such as 3D spheroids, organoids, and engineered hepatocytes. An integrated strategy, that includes the use of human-based cells with enhanced biological relevance and throughput, and applying the quantitative analysis of data, may provide an approach for future genotoxicity risk assessment.