Prevalence of Premature Ovarian Failure in Women with Tuberous Sclerosis

Tuberous Sclerosis Complex (TSC) is an autosomal dominant tumor suppressor disorder characterized by hamartomas, or benign growths, in various organ systems. Inactivating mutations in either the TSC1 or the TSC2 gene cause most cases of TSC. Recently, the use of ovarian specific conditional knock-out mouse models has demonstrated a crucial role of the TSC genes in ovarian function. Mice with complete deletion of Tsc1 or Tsc2 showed accelerated ovarian follicle activation and subsequent premature follicular depletion, consistent with the human condition premature ovarian failure (POF). POF is defined in women as the cessation of menses before the age of 40 and elevated levels of follicle stimulating hormone (FSH). The prevalence of POF is estimated to be 1%, affecting a substantial number of women in the general population. Nonetheless, the etiology of most cases of POF remains unknown. Based on the mouse model results, we hypothesized that the human TSC1 and TSC2 genes are likely to be crucial for ovarian development and function. Moreover, since women with TSC already have one inactivated TSC gene, we further hypothesized that they may show a higher prevalence of POF. To test this hypothesis, we surveyed 1000 women with TSC belonging to the Tuberous Sclerosis Alliance, a national support organization. 182 questionnaires were analyzed for information on menstrual and reproductive function, as well as TSC. This self-reported data revealed 8 women (4.4%) with possible POF, as determined by menstrual history report and additional supportive data. This prevalence is much higher than 1% in the general population. Data from all women suggested other reproductive pathology associated with TSC such as a high rate of miscarriage (41.2%) and menstrual irregularity of any kind (31.2%). These results establish a previously unappreciated effect of TSC on women’s reproductive health. Moreover, these data suggest that perturbations in the cellular pathways regulated by the TSC genes may play an important role in reproductive function.

PREMATURE CONDENSATION OF SPERMATOGENIC CELL CHROMOSOMES

The technique of premature chromosome condensation (PCC) has been used primarily to study interphase chromosomes of somatic cells. In this study, mitotic cells were fused to cells from the mouse testes to examine the chromosomes of germ cells. The testes contain various types of cells, both germinal and nongerminal. In these initial studies, four types of PCC morphologies were observed. Chromosome morphology of the PCC and labeling experiments demonstrated the mouse cell origin of various PCC. Attempts were next made to determine the cell types producing the PCC. Spermatogonia, diplotene spermatocytes, secondary spermatocytes and round spermatids are proposed to be the origin of the PCC morphologies. Some PCC could be banded by G and C banding techniques and the mouse chromosomes identified.^ Studies were subsequently undertaken to evaluate this technique as a method of evaluating damage to germ cells. Testicular cells from irradiated mice were fused to mitotic cells and the PCC examined. Both round spermatids and secondary spermatocytes exhibited chromosome damage in the form of chromatid breaks. A linear correlation was found between the dose of irradiation and the number of breaks per cell. This technique may develop into a useful method for evaluating the clastogenic effect of agents on the germ cells. ^

The differential staurosporine mediated G1 arrest in normal versus tumor cells is dependent on the retinoblastoma and Chk1 proteins

The ability to regulate cell cycle progression is one of the differences that separates normal from tumor cells. A protein, which is frequently mutated or deleted in a majority of tumor cells, is the retinoblastoma protein (pRb). Previously, we reported that normal cells, which have a wild-type Rb pathway, can be reversibly arrested in the G1 phase of the cell cycle by staurosporine (ST), while tumor cells were unaffected by this treatment. As a result, ST may be used to protect normal cells against the toxic affects of chemotherapy. Here we set out to determine the mechanism(s) by which ST can mediate a reversible G1 arrest in pRb positive cells. To this end, we used an isogenic cell model system of normal human mammary epithelial cells (HMEC) with either intact pRb+ (p53-) or p53+ (pRb-) treated with ST. Our results show that pRb+ cells treated with low concentrations of ST, arrested in the G1 phase of the cell cycle; however, in pRb - cells there was no response. This was verified as a true G 1 arrest in pRb+ cells by two different methods for monitoring cell cycle kinetics and in two additional model systems for Rb (i.e. pRb -/- mouse embryo fibroblasts, and downregulation of RB with siRNA). Our results indicated that ST-mediated G1 arrest required pRb, which in turn initiated a cascade of events leading to inhibition of CDK4 and CDK2 activities and up-regulation of p21 protein. Further assessment of this pathway revealed the novel finding that Chk1 expression and activity were required for the Rb-dependent, ST-mediated G1 arrest. In fact, overexpression of Chk1 facilitated recovery from ST-mediated G1 arrest, an effect only observed in RB+ cells. Collectively, our data suggest pRb is able to cooperate with Chk1 to mediate a G1 arrest in pRb+ cells, but not in pRb- cells. The elucidation of this pathway can help identify novel agents that can be used to protect cancer patients against the debilitating affects of chemotherapy, by targeting only the normal proliferating cells in the body that are otherwise destroyed. ^

A constellation of factors associated with premature births among African American women

A cohort, cross-sectional, historical study design was used to study factors related to spontaneous premature birth outcomes among African American women. The cohort consisted of 4,294 mothers drawn from the 1988 National Maternal and Infant Health Survey conducted by the National Center for Health Statistics. The objectives of the study were: (1) to examine the distribution of gestational ages of African American infants for selected variables reported for their families and (2) to describe risk factors associated with birth at 20–31 weeks of gestational age and at 32–36 weeks of gestational age. Risk factors examined include maternal age, maternal marital status, maternal living arrangements, maternal education, maternal work status, household income, gestational bleeding, month prenatal began, adequacy of prenatal care, parity, previous viable preterm birth, and behavioral factors of attitude toward pregnancy, smoking, drug, and alcohol use during pregnancy. Frequency distributions, cross tabulations, stratified analysis, and logistic regression analysis were used. ^ Risk factors associated with a 50 percent or more increase in preterm birth were cocaine use, low maternal education, teenaged mother, prenatal care deficits or overuse, and bleeding during the second half of pregnancy. The other risk factors of not living with the baby's father, smoking cigarettes and having a mistimed pregnancy carried statistically significance but lower strength of association. ^ Health care services, educational systems, and community organizations can develop and evaluate comprehensive health education and information campaigns that address preventable risk factors during pregnancy. Although preterm birth cannot always be prevented, preconception care can help identify and modify maternal risk and promote optimum health before conception. Quality care should include continued risk assessment, health promotion, and interventions. ^

The relationship between premature rupture of the membranes and sepsis in preterm neonates

This investigation was designed as a hospital-based, historical cohort study. The objective of the study was to determine the association between premature rupture of the membranes (PROM) and its duration on neonatal sepsis, infection, and mortality. Neonates born alive with gestational ages between 25 and 35 weeks from singleton pregnancies complicated by PROM were selected. Each of the 507 neonates was matched on gestational age, gender, ethnicity, and month of birth with a neonate without the complication of PROM.^ Data were abstracted from deliveries between January 1979 and December 1985 describing the mother's demographics, labor and delivery treatments and complications, the neonate's demographics, infection status, and medical care. The matched pairs analysis reveals a significant increase in risk of neonatal sepsis (RR = 3.5) and neonatal infection (RR = 2.4) among preterm births complicated by PROM, with a PROM exposure contributing an excess 4 to 5 cases of sepsis per 100 infants (RD = 0.04 for infection and RD = 0.05 for sepsis). Generally PROM remains an important risk factor for sepsis and infection when controlling for various other characteristics, and the risk difference remains constant.^ PROM was not significantly associated with neonatal mortality (RR = 1.02). There is an increase in risk difference for mortality associated with PROM among septic and infected infants, but it is not significant.^ A clear increase in risk of sepsis and infection from PROM occurs when durations of PROM are long (more than 48 hours), e.g., for sepsis the RR is 2.42 for short durations and RR is 6.0 for long durations. No such risk with long duration appears for neonatal mortality.^ This study indicates the importance of close observation of neonates with PROM for sepsis and infection so treatment can be initiated early. However, prematurity is the major risk for sepsis and the practice of early delivery to avoid prolonged durations of PROM does not alter the magnitude of risk. The greatest protection against these infection complications was provided when the neonate weighed over 1500 grams or had more than 33 weeks gestation. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.