Advances in the genetics of endometriosis

Endometriosis is a gynecological disease characterized by implantation of endometrial tissue outside of the uterus. Early familial aggregation and twin studies noted a higher risk of endometriosis among relatives. Studies on the roles of the environment, genetics and aberrant regulation in the endometrium and endometriotic lesions of women with endometriosis suggest that endometriosis arises from the interplay between genetic variants and environmental factors. Elucidating the hereditary component has proven difficult because multiple genes seem to produce a susceptibility to developing endometriosis. Molecular techniques, including linkage and genome-wide analysis, have identified candidate genes located near known loci related to development and regulation of the female reproductive tract. As new candidate genes are discovered and hereditary pathways identified using technologies such as genome-wide analysis, the possibility of prevention and treatment becomes more tangible for millions of women affected by endometriosis. Here, we discuss the advances of genetic research in endometriosis and describe technologies that have contributed to the current understanding of the genetic variability in endometriosis, variability that includes regulatory polymorphisms in key genes

PPAR Action in Human Placental Development and Pregnancy and Its Complications

During pregnancy crucial anatomic, physiologic, and metabolic changes challenge the mother and the fetus. The placenta is a remarkable organ that allows the mother and the fetus to adapt to the new metabolic, immunologic, and angiogenic environment imposed by gestation. One of the physiologic systems that appears to have evolved to sustain this metabolic regulation is mediated by peroxisome proliferator-activated receptors (PPARs). In clinical pregnancy-specific disorders, including preeclampsia, gestational diabetes, and intrauterine growth restriction, aberrant regulation of components of the PPAR system parallels dysregulation of metabolism, inflammation and angiogenesis. This review summarizes current knowledge on the role of PPARs in regulating human trophoblast invasion, early placental development, and also in the physiology of clinical pregnancy and its complications. As increasingly indicated in the literature, pregnancy disorders, such as preeclampsia and gestational diabetes, represent potential targets for treatment with PPAR ligands. With the advent of more specific PPAR agonists that exhibit efficacy in ameliorating metabolic, inflammatory, and angiogenic disturbances, further studies of their application in pregnancy-related diseases are warranted

Enabling Robust State Estimation through Measurement Error Covariance Adaptation

Accurate platform localization is an integral component of most robotic systems. As these robotic systems become more ubiquitous, it is necessary to develop robust state estimation algorithms that are able to withstand novel and non-cooperative environments. When dealing with novel and non-cooperative environments, little is known a priori about the measurement error uncertainty, thus, there is a requirement that the uncertainty models of the localization algorithm be adaptive. Within this paper, we propose the batch covariance estimation technique, which enables robust state estimation through the iterative adaptation of the measurement uncertainty model. The adaptation of the measurement uncertainty model is granted through non-parametric clustering of the residuals, which enables the characterization of the measurement uncertainty via a Gaussian mixture model. The provided Gaussian mixture model can be utilized within any non-linear least squares optimization algorithm by approximately characterizing each observation with the sufficient statistics of the assigned cluster (i.e., each observation's uncertainty model is updated based upon the assignment provided by the non-parametric clustering algorithm). The proposed algorithm is verified on several GNSS collected data sets, where it is shown that the proposed technique exhibits some advantages when compared to other robust estimation techniques when confronted with degraded data quality.Comment: 14 pages, 13 figures, Submitted to IEEE Transactions on Aerospace And Electronic System

Uncertainty Model Estimation in an Augmented Data Space for Robust State Estimation

The requirement to generate robust robotic platforms is a critical enabling step to allow such platforms to permeate safety-critical applications (i.e., the localization of autonomous platforms in urban environments). One of the primary components of such a robotic platform is the state estimation engine, which enables the platform to reason about itself and the environment based upon sensor readings. When such sensor readings are degraded traditional state estimation approaches are known to breakdown. To overcome this issue, several robust state estimation frameworks have been proposed. One such method is the batch covariance estimation (BCE) framework. The BCE approach enables robust state estimation by iteratively updating the measurement error uncertainty model through the fitting of a Gaussian mixture model (GMM) to the measurement residuals. This paper extends upon the BCE approach by arguing that the uncertainty estimation process should be augmented to include metadata (e.g., the signal strength of the associated GNSS observation). The modification of the uncertainty estimation process to an augmented data space is significant because it increases the likelihood of a unique partitioning in the measurement residual domain and thus provides the ability to more accurately characterize the measurement uncertainty model. The proposed batch covariance estimation over an augmented data-space (BCE-AD) is experimentally validated on collected data where it is shown that a significant increase in state estimation accuracy can be granted compared to previously proposed robust estimation techniques.Comment: 6 pages, 5 figures, Correspondence submitted to the IEEE Transactions on Aerospace and Electronic System

Robust Incremental State Estimation through Covariance Adaptation

Recent advances in the fields of robotics and automation have spurred significant interest in robust state estimation. To enable robust state estimation, several methodologies have been proposed. One such technique, which has shown promising performance, is the concept of iteratively estimating a Gaussian Mixture Model (GMM), based upon the state estimation residuals, to characterize the measurement uncertainty model. Through this iterative process, the measurement uncertainty model is more accurately characterized, which enables robust state estimation through the appropriate de-weighting of erroneous observations. This approach, however, has traditionally required a batch estimation framework to enable the estimation of the measurement uncertainty model, which is not advantageous to robotic applications. In this paper, we propose an efficient, incremental extension to the measurement uncertainty model estimation paradigm. The incremental covariance estimation (ICE) approach, as detailed within this paper, is evaluated on several collected data sets, where it is shown to provide a significant increase in localization accuracy when compared to other state-of-the-art robust, incremental estimation algorithms.Comment: 8 pages, 4 figures, 2 tables, submitted to IEEE Robotics and Automation Letter

Secretory Phase and Implantation

This chapter will explore the latter phase of the menstrual cycle focusing on the secretory phase of the endometrium. In particular, focus will be on the mid-secretory endometrium and appropriate markers and hormonal environment for successful implantation. This will be put in the context of the luteal phase of ovulation and the hormonal support that progesterone provides. We will also review pathologic states, such as endometriosis and related progesterone resistance, which affect mid-secretory phase and implantation. Finally, we will provide a detailed review of the literature on what the current state of knowledge is regarding receptivity and the microenvironment of the mid-secretory endometrium which is essential to implantation

Maternal Vitamin D, Folate, and Polyunsaturated Fatty Acid Status and Bacterial Vaginosis during Pregnancy

Objective. To investigate associations among serum 25-hydroxy-vitamin D (25-OH-D), folate, omega-6/omega-3 fatty acid ratio and bacterial vaginosis (BV) during pregnancy. Methods. Biospecimens and data were derived from a random sample (N = 160) of women from the Nashville Birth Cohort. We compared mean plasma nutrient concentrations for women with and without BV during pregnancy (based on Nugent score ≥7) and assessed the odds of BV for those with 25-OH-D <12 ng/mL, folate <5 ug/L, and omega-6/omega-3 ratio >15. Results. The mean plasma 25-OH-D was significantly lower among women with BV during pregnancy (18.00±8.14 ng/mL versus 24.34±11.97 ng/mL, P = 0.044). The adjusted odds of BV were significantly increased among pregnant women with 25-OH-D <12 ng/mL (aOR 5.11, 95% CI: 1.19–21.97) and folate <5 ug/L (aOR 7.06, 95% CI: 1.07–54.05). Conclusion. Vitamin D and folate deficiencies were strongly associated with BV (Nugent score ≥7) during pregnancy

Perioperative safety of two-team simultaneous bilateral total knee arthroplasty in the obese patient

<p>Abstract</p> <p>Background</p> <p>Although the rates of perioperative morbidity and mortality with simultaneous bilateral total knee arthroplasty remain a concern, multiple studies have shown the procedure to be safe in selected patient populations. Evidence also remains mixed regarding the outcomes of total knee arthroplasty in obese patients. The purpose of this paper is to compare the rates of perioperative morbidity and mortality in consecutive obese patients undergoing two-team simultaneous bilateral total knee arthroplasty and unilateral total knee arthroplasty.</p> <p>Methods</p> <p>The records on all two-team simultaneous total knee arthroplasties and unilateral total knee arthroplasties from October 1997 to December 2007 were reviewed. A total of 151 patients with a body mass index (BMI) >30 undergoing two-team simultaneous total knee arthroplasty and 148 patients with a BMI >30 undergoing unilateral total knee arthroplasty were retrospectively reviewed and analyzed to determine perioperative morbidity and mortality as well as one-year mortality rates.</p> <p>Results</p> <p>Preoperative patient characteristics did not show any significant differences between groups. The simultaneous bilateral group had significantly longer operative times (127.4 versus 112.7 minutes, p < 0.01), estimated blood loss (176.7 versus 111.6 mL, p = 0.01), percentage of patients requiring blood transfusion (64.9% versus 13.9%, p < 0.01), length of hospital stay (3.72 versus 3.30 days, p < 0.01), and percentage of patients requiring extended care facility usage at discharge (63.6% versus 27.8%, p < 0.01). No significant difference between unilateral and bilateral groups was seen in regards to total complication rate, major or minor complication subgroup rate, or any particular complication noted. Doubling the variables in the unilateral group for a staged total knee arthroplasty scenario did create significant increases over the simultaneous data in almost every data category.</p> <p>Conclusions</p> <p>Two-team simultaneous total knee arthroplasty appears to be safe in obese patients, with similar complication rates as compared to unilateral procedures. Two-team simultaneous total knee arthroplasty also appears to have potential benefits over a staged procedure in the obese patient, although more study is required regarding this topic.</p