The roles of Prostanoid EP receptors in the control of contractions of human myometrium

The primary function of the uterus during pregnancy is to harbour the growing fetus in a quiescent environment. Upon maturation of the fetus, the uterus generates forceful contractions during labour. Prostaglandins are central in the parturition process. PGE2 in particular, is produced in large quantities by fetal membranes and possible roles include cervical ripening and the stimulation of uterine contractions. PGE2 exhibits a wide spectrum of physiological actions depending on the distribution and subtypes of EP receptors. EP1 and EP3 mediate contractions, but there is no consensus about their relative importance. Preliminary experiments were undertaken to examine the possible expression patterns of EP1 and EP3. The expression data showed there to be no significant changes between the localisation in upper or lower segment biopsies using both RNA and protein samples. However, there was a labour associated change seen in EP3 isoform expression at mRNA level together with changes in the protein expression of EP3 in the lower segment using immunofluorescence. Given that the expression studies suggest that it is the EP3 receptor, and not the EP1 receptor, that is most important in myometrial contractility, I established an in vitro tissue bath to conduct functional studies examining contractility in upper and lower segment myometrial biopsies. My data demonstrated that stretch of term human myometrium results in spontaneous contractions. There was no difference between upper and lower segment myometrium, so subsequent experiments were conducted on lower segment biopsies. The addition of acetyl salicylic acid (a non-selective COX inhibitor) did not completely stop spontaneous contractility but reduced the total work done significantly. This could be reversed by add back of PGE2. Prostaglandins are therefore significant but not crucial for spontaneous contractility. Both PGE2 and PGF2α increased the total work done significantly compared to spontaneous contractions; E2 more so than F2α. The PGF2α antagonist did not inhibit spontaneous contractions, but did inhibit PGF2α induced contractions. The EP1 antagonist did not inhibit spontaneous contractions but EP3 antagonist did. These results suggest that an EP3 antagonist is a better candidate as a new tocolytic compound compared to FP or EP1 antagonist

Haemodialysis and peritoneal dialysis patients admitted to intensive care units.

Hutchison and colleagues report a 10-year experience of dialysis patients admitted to intensive care units (ICUs) in the UK excluding Scotland. Their study is the largest published so far and raises issues of interest to both ICU physicians and nephrologists. Overall, the dialysis patients, although sicker on admission and having pre-existing co-morbidities, do as well as other ICU patients. Their clinical progress after leaving the ICU, however, is less good than for other ICU patients, raising the possibility that the patients might be leaving too early, or perhaps that dialysis patients should be discharged to a high-dependency unit rather than go direct to a renal ward. All in all, the paper by Hutchison and colleagues provides a useful foundation for planning the critical care management of dialysis patients in the UK and elsewhere

Improving Sampling from Generative Autoencoders with Markov Chains

We focus on generative autoencoders, such as variational or adversarial autoencoders, which jointly learn a generative model alongside an inference model. We define generative autoencoders as autoencoders which are trained to softly enforce a prior on the latent distribution learned by the model. However, the model does not necessarily learn to match the prior. We formulate a Markov chain Monte Carlo (MCMC) sampling process, equivalent to iteratively encoding and decoding, which allows us to sample from the learned latent distribution. Using this we can improve the quality of samples drawn from the model, especially when the learned distribution is far from the prior. Using MCMC sampling, we also reveal previously unseen differences between generative autoencoders trained either with or without the denoising criterion

Plasma Concentrations of Tranexamic Acid in Postpartum Women After Oral Administration.

OBJECTIVE: To evaluate the pharmacokinetics of tranexamic acid after oral administration to postpartum women. METHODS: We conducted a single-center pharmacokinetic study at Teaching Hospital-Jaffna, Sri Lanka, on 12 healthy postpartum women who delivered vaginally. After oral administration of 2 g of immediate-release tranexamic acid 1 hour after delivery, pharmacokinetic parameters were measured on plasma samples at 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6, 8, 10, and 12 hours. Plasma tranexamic acid concentrations were determined by high-performance liquid chromatography. The outcome measures were maximum observed plasma concentration, time to maximum plasma concentration, time to reach effective plasma concentration, time period effective serum concentration lasted, area under the curve for drug concentration, and half-life of tranexamic acid. RESULTS: The mean maximum observed plasma concentration was 10.06 micrograms/mL (range 8.56-12.22 micrograms/mL). The mean time to maximum plasma concentration was 2.92 hours (range 2.5-3.5 hours). Mean time taken to reach the effective plasma concentration of 5 micrograms/mL and the mean time this concentration lasted were 0.87 hours and 6.73 hours, respectively. Duration for which plasma tranexamic acid concentration remained greater than 5 micrograms/mL was 5.86 hours. Half-life was 1.65 hours. Area under the curve for drug concentration was 49.16 micrograms.h/mL (range 43.75-52.69 micrograms.h/mL). CONCLUSION: Clinically effective plasma concentrations of tranexamic acid in postpartum women may be achieved within 1 hour of oral administration. Given the promising pharmacokinetic properties, we recommend additional studies with larger sample sizes to investigate the potential of oral tranexamic acid for the treatment or prophylaxis of postpartum hemorrhage

Classifying Options for Deep Reinforcement Learning

In this paper we combine one method for hierarchical reinforcement learning - the options framework - with deep Q-networks (DQNs) through the use of different "option heads" on the policy network, and a supervisory network for choosing between the different options. We utilise our setup to investigate the effects of architectural constraints in subtasks with positive and negative transfer, across a range of network capacities. We empirically show that our augmented DQN has lower sample complexity when simultaneously learning subtasks with negative transfer, without degrading performance when learning subtasks with positive transfer.Comment: IJCAI 2016 Workshop on Deep Reinforcement Learning: Frontiers and Challenge

Institutionalization of postpartum intrauterine devices.

Globally, 225 million women need contraception. Birth spacing reduces perinatal and maternal morbidity and mortality. PPIUD is cost‐effective and reversible with minimal expulsions and complications

Deep unsupervised clustering with Gaussian mixture variational autoencoders

We study a variant of the variational autoencoder model with a Gaussian mixture as a prior distribution, with the goal of performing unsupervised clustering through deep generative models. We observe that the standard variational approach in these models is unsuited for unsupervised clustering, and mitigate this problem by leveraging a principled information-theoretic regularisation term known as consistency violation. Adding this term to the standard variational optimisation objective yields networks with both meaningful internal representations and well-defined clusters. We demonstrate the performance of this scheme on synthetic data, MNIST and SVHN, showing that the obtained clusters are distinct, interpretable and result in achieving higher performance on unsupervised clustering classification than previous approaches