Photobiomodulation can improve ovarian activity in polycystic ovary syndrome-induced rats.

Follicular cystic ovary disease is a common reproductive disorder in women and females of domestic animals, characterized by anovulation and the persistence of follicle is a common cause of reproductive failure in mammalian. Polycystic ovary syndrome (PCOS) is characterized by hyperandrogenism (HA), chronic anovulation and polycystic ovaries, and it is a common reproductive endocrine disease with clinical manifestations including hirsutism, acne, infertility and obesity that can affect 5-20% of women in their reproductive age. Photobiomodulation (PBM) has been investigated and used in clinical practice, related to biomodulatory influences on cellular functions in animals and humans, both in vivo and in vitro. In this study, we include endocrine and reproductive features in a rat model for PCOS and the effects of PBM on ovarian activities. Forty-five adult female Wistar rats PCOS-induced by a single dose of the estradiol valerate (EV) were used in the study. After the EV injection for PCO induction, rats were divided into 9 groups (n = 5/group) named C30, C45 and C60 (Control group), S30, S45 and S60 (PCO group) and L30, L45 and L60 (PCO/Laser group). The rats were irradiated with laser 3 times/week. The results shown that EV PCO-induced rats had increased body mass, reduced ovary mass, and reduced GSI. The plasma levels of P4 and T were increased, and the LH plasma level was decreased by PBM stimulation. The number of ovarian follicles and corpus luteum were increased, and the number of ovarian cysts was decreased by PBM stimulation. Thus, reproductive and endocrine characteristics were modulated by PBM.

A statistical model investigating the prevalence of tuberculosis in New York City using counting processes with two change-points.

We considered a Bayesian analysis for the prevalence of tuberculosis cases in New York City from 1970 to 2000. This counting dataset presented two change-points during this period. We modelled this counting dataset considering non-homogeneous Poisson processes in the presence of the two-change points. A Bayesian analysis for the data is considered using Markov chain Monte Carlo methods. Simulated Gibbs samples for the parameters of interest were obtained using WinBugs software.

An application of Bayesian analysis to medical follow-up data.

Posterior distributions can provide effective summaries of the main conclusions of medical follow-up studies. In this article, we use Bayesian methods for the analysis of survival data. We describe posterior distributions for various parameters of clinical interest in the presence of arbitrary right censorship. Non-informative reference priors result from transformation of a two-parameter Weibull model into a location-scale family. We suggest an approach for checking adequacy. For illustration, we apply the methods to a well-known acute leukemia data set.