Morphology of human endometrial explants and secretion of stromal marker proteins in short- and long-term cultures

Human endometrial tissue is frequently biopsied under surgical and laparoscopic procedures for the investigation of infertility, abdominal, or menstrual pain. These symptoms often but not always are the consequence of endometriosis, which is characterised by the growth of endometrial tissue outside the uterine cavity and affecting 8-10% of women during the fertile age. First-line treatment is often by surgery. Biopsied endometrial tissue is not only used for immunohistochemical examination but has also been cultured in vitro. Explant culture systems maintain the three-dimensional structure of the tissue, but so far no morphological validation studies are available for the stromal cells which are responsible for the production of hormones and inflammatory cytokines in the endometrium. We have documented, by transmission electron microscopy, the morphological alterations of stromal cells in short- (12h) and long-term (7days) cultures of endometrial explants biopsied in the postovulatory phase. The production of prolactin, a stromal cell marker, was determined. We found that the morphological integrity of these cells was starting to be disrupted from as early as 12h in culture. Some stromal cells, however, developed into predecidual cells. After 96h, a large fraction of the cell population was necrotic, and after 7days, the cytoplasm had disappeared. In presence of progesterone, the decay of stromal cell integrity was slowed down. The release of prolactin and IGF-binding protein-1 during culture followed the morphological pattern. We conclude that the explant culture model is viable for not more than 48h in vitro for stromal cells, but that this interval can be prolonged by the addition of progesterone which initiates decidualisatio

Bounded Rational Decision-Making with Adaptive Neural Network Priors

Bounded rationality investigates utility-optimizing decision-makers with limited information-processing power. In particular, information theoretic bounded rationality models formalize resource constraints abstractly in terms of relative Shannon information, namely the Kullback-Leibler Divergence between the agents' prior and posterior policy. Between prior and posterior lies an anytime deliberation process that can be instantiated by sample-based evaluations of the utility function through Markov Chain Monte Carlo (MCMC) optimization. The most simple model assumes a fixed prior and can relate abstract information-theoretic processing costs to the number of sample evaluations. However, more advanced models would also address the question of learning, that is how the prior is adapted over time such that generated prior proposals become more efficient. In this work we investigate generative neural networks as priors that are optimized concurrently with anytime sample-based decision-making processes such as MCMC. We evaluate this approach on toy examples.Comment: Published in ANNPR 2018: Artificial Neural Networks in Pattern Recognitio