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的一个原因。然而，阴道微生物浓度与PTD之间的关系、作为PTD重要危险因素的关键微生物种群的鉴定以及细菌促进PTD的病理生理机制仍有待确定。此外，对PTD有重要意义的微生物群体之间的相互作用尚不清楚。早产可能是由细菌磷脂酶A2引发的，它释放花生四烯酸——前列腺素的主要物质，前列腺素被认为是分娩开始的基础。据推测，脂肪酶和磷脂酶的联合活性也解释了与膜损伤相关的各种效应。然而，阴道内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地收集微生物种群和酶活性的定量数据，并在两个PTD高风险队列中接受细菌性阴道病的存在或不存在，一个记录两个PTD高风险队列和一个低风险队列中细菌性阴道病的存在或不存在。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。具体目标如下：(1)定量确定关键微生物种群作为PTD的危险因素；(2)确定导致PTD的微生物种群之间的相互作用；(3)确定阴道酶（脂肪酶、磷脂酶）活性与PTD之间是否存在关联；(4)记录是否存在细菌性阴道病与PTD之间的关系；(4)记录存在或不存在细菌性阴道病与PTD之间的关系。识别PTD的预测途径可以识别PTD的风险个体，可以识别PTD的风险个体，并最终实施新的预防策略。

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.