Manipulation of the follicular phase: Uterodomes and pregnancy - is there a correlation?

BACKGROUND: Manipulation of the follicular phase uterine epithelium in women undergoing infertility treatment, has not generally shown differing morphological effects on uterine epithelial characteristics using Scanning Electron Microscopy (SEM) and resultant pregnancy rates have remained suboptimal utilising these manipulations. The present study observed manipulation of the proliferative epithelium, with either 7 or 14 days of sequential oestrogen (E) therapy followed by progesterone (P) and assessed the appearance of pinopods (now called uterodomes) for their usefulness as potential implantation markers in seven women who subsequently became pregnant. Three endometrial biopsies per patient were taken during consecutive cycles: day 19 of a natural cycle - (group 1), days 11/12 of a second cycle after 7 days E then P - (group 2), and days 19/22 of a third cycle after 14 days E then P - (group 3). Embryo transfer (ET) was performed in a subsequent long treatment cycle (as per Group 3). RESULTS: Seven pregnancies resulted in seven viable births including one twins and one miscarriage. Analysis of the individual regimes showed 5 days of P treatment to have a higher correlation for uterodomes in all 3 cycles observed individually. It was also observed that all 7 women demonstrated the appearance of uterodomes in at least one of their cycles. CONCLUSIONS: We conclude that manipulation of the follicular phase by shortening the period of E exposure to 7 days, does not compromise uterine epithelial morphology and we add weight to the conclusion that uterodomes indicate a receptive endometrium for implantation

Biofluid Biomarkers in Huntington's Disease

Huntington's disease (HD) is a chronic progressive neurodegenerative condition where new markers of disease progression are needed. So far no disease-modifying interventions have been found, and few interventions have been proven to alleviate symptoms. This may be partially explained by the lack of reliable indicators of disease severity, progression, and phenotype.Biofluid biomarkers may bring advantages in addition to clinical measures, such as reliability, reproducibility, price, accuracy, and direct quantification of pathobiological processes at the molecular level; and in addition to empowering clinical trials, they have the potential to generate useful hypotheses for new drug development.In this chapter we review biofluid biomarker reports in HD, emphasizing those we feel are likely to be closest to clinical applicability

A naïve Bayes classifier for planning transfusion requirements in heart surgery

Rationale, aims and objectives  Transfusion of allogeneic blood products is a key issue in cardiac surgery. Although blood conservation and standard transfusion guidelines have been published by different medical groups, actual transfusion practices after cardiac surgery vary widely among institutions. Models can be a useful support for decision making and may reduce the total cost of care. The objective of this study was to propose and evaluate a procedure to develop a simple locally customized decision-support system. Methods  We analysed 3182 consecutive patients undergoing cardiac surgery at the University Hospital of Siena, Italy. Univariate statistical tests were performed to identify a set of preoperative and intraoperative variables as likely independent features for planning transfusion quantities. These features were utilized to design a naïve Bayes classifier. Model performance was evaluated using the leave-one-out cross-validation approach. All computations were done using spss and matlab code. Results  The overall correct classification percentage was not particularly high if several classes of patients were to be identified. Model performance improved appreciably when the patient sample was divided into two classes (transfused and non-transfused patients). In this case the naïve Bayes model correctly classified about three quarters of patients with 71.2% sensitivity and 78.4% specificity, thus providing useful information for recognizing patients with transfusion requirements in the specific scenario considered. Conclusions  Although the classifier is customized to a particular setting and cannot be generalized to other scenarios, the simplicity of its development and the results obtained make it a promising approach for designing a simple model for different heart surgery centres needing a customized decision-support system for planning transfusion requirements in intensive care unit