ABSTRACT
Inflammation contributes to nearly 4 million global premature births annually. Here, we used a mouse model of intrauterine inflammation to test clinically used formulations, as well as engineered nanoformulations, for the prevention of preterm birth (PTB). We observed that neither systemic 17a-hydroxyprogesterone caproate (Makena) nor vaginal progesterone gel (Crinone) was sufficient to prevent inflammation-induced PTB, consistent with recent clinical trial failures. However, we found that vaginal delivery of mucoinert nanosuspensions of histone deacetylase (HDAC) inhibitors, in some cases with the addition of progesterone, prevented PTB and resulted in delivery of live pups exhibiting neurotypical development. In human myometrial cells in vitro, the P4/HDAC inhibitor combination both inhibited cell contractility and promoted the anti-inflammatory action of P4 by increasing progesterone receptor B stability. Here, we demonstrate the use of vaginally delivered drugs to prevent intrauterine inflammation-induced PTB resulting in the birth of live offspring in a preclinical animal model.
Subject(s)
Pharmaceutical Preparations , Premature Birth , 17 alpha-Hydroxyprogesterone Caproate , Animals , Female , Nanomedicine , Pregnancy , Premature Birth/drug therapy , Premature Birth/prevention & control , Progesterone , ProgestinsABSTRACT
Social learning-the ability to learn from observing the decisions of other people and the outcomes of those decisions-is fundamental to human evolutionary and cultural success. The Internet now provides social evidence on an unprecedented scale. However, properly utilizing this evidence requires a capacity for statistical inference. We examined how people's interpretation of online review scores is influenced by the numbers of reviews-a potential indicator both of an item's popularity and of the precision of the average review score. Our task was designed to pit statistical information against social information. We modeled the behavior of an "intuitive statistician" using empirical prior information from millions of reviews posted on Amazon.com and then compared the model's predictions with the behavior of experimental participants. Under certain conditions, people preferred a product with more reviews to one with fewer reviews even though the statistical model indicated that the latter was likely to be of higher quality than the former. Overall, participants' judgments suggested that they failed to make meaningful statistical inferences.
