QUANTITIVE MICRBIOLOGIC MODEL FOR PRETERM DELIVERY

早产（PTD）是美国婴儿发病率和死亡率的主要原因，预防PTD是围产期保健的主要目标。最近的数据表明，产妇生殖道感染，包括细菌性阴道病，和PTD之间有很强的联系，有令人信服的证据表明微生物是PTD的原因。然而，阴道微生物浓度和PTD之间的关系，确定关键微生物种群是PTD的重要危险因素，以及细菌导致PTD的病理生理机制仍有待确定。此外，对PTD具有重要意义的微生物种群之间的相互作用尚不清楚。早产可由细菌磷脂酶A2引发，其释放花生四烯酸--被认为是引发分娩的基本物质的花生四烯酸。据推测，脂肪酶和磷脂酶的组合活性也解释了与膜损伤相关的各种效应。然而，阴道中这些联合酶活性与妊娠结局之间的关系尚未确定。拟议项目的目的是前瞻性收集微生物种群和酶活性的定量数据，并在PTD高风险的两个队列中接受是否存在细菌性阴道病，在PTD高风险的两个队列和低风险的一个队列中记录是否存在细菌性阴道病。广义估计方程回归将用于分析重复测量数据，并制定定量模型来预测PTD。将致力于实现以下具体目标：（1）定量鉴定作为PTD风险因素的关键微生物种群，（2）鉴定促成PTD的微生物种群之间的相互作用，（3）确定阴道酶与PTD之间是否存在关联，（脂肪酶、磷脂酶）活性和PTD，和（4）记录细菌性阴道病的存在或不存在与PTD之间的任何关系，和（4）记录细菌性阴道病的存在或不存在与PTD之间的任何关系。识别PTD的预测途径可以识别PTD风险个体，并最终实施预防PTD的诺埃尔策略。

Preterm delivery (PTD) is the leading cause of infant morbidity and mortality in the United States, and prevention of PTD is a primary goal in perinatal health care. Recent data indicates a strong association a strong association between maternal genital tract infection, including bacterial vaginosis, and PTD, with compelling evidence that micro- organisms are a cause of PTD. However, the relationship between concentrations of vaginal microorganisms and PTD, the identification of key microbial populations that are significant risk factors for PTD, and the pathophysiologic mechanisms by which bacteria contribute to PTD remain to be determined. In addition, the interactions between microbial populations of significance to PTD are unknown. Preterm labor may be initiated by bacterial phospholipases A2 and which release arachidonic acid-the primary substance for prostaglandins which are considered fundamental to the initiation of labor. It has been hypothesized that the combined activities of lipase and phospholipases also account for a variety of effects associated with membrane damage. However, the relationship between these combined enzyme activities in the vagina and pregnancy outcome has not been determined. The objective of the proposed project is to prospectively collect quantitative data on microbiologic populations and enzyme activities and to receptor the presence or absence of bacterial vaginosis in two cohorts at high risk for PTD and one to record the presence or absence of bacterial vaginosis in two cohorts at high risk for PTD and one cohort at low risk. Generalized estimating equation regression will be used to analyze repeat measurement data and to formulate quantitative models to predict PTD. The following Specific Aims will be addressed: (1) quantitatively identify key microbial population as risk factors for PTD, (2) identify interactions between microbial populations that contribute to PTD, (3) determine whether there is an association between vaginal enzyme (lipase, phospholipase) activities and PTD, and (4) document any relationship between the presence or absence of bacterial vaginosis and PTD, and (4) document any relationship between the presence of absence of bacterial vaginosis and PTD. Recognition of predictive pathway(s) for PTD may permit the identification of individuals at risk for PTD may permit the identification of individuals at risk for PTD and, ultimately, the implementation of noel strategies for its prevention.