Estrogen reduction by aromatase inhibition for benign prostatic hyperplasia: results of a double-blind, placebo-controlled, randomized clinical trial using two doses of the aromatase-inhibitor atamestane. Atamestane Study Group.

BACKGROUND: The concept of estrogen withdrawal by an aromatase inhibitor in the treatment of benign prostatic hyperplasia (BPH) was assessed in a prospective, randomized, double-blind, placebo-controlled multicenter trial. METHODS: Two hundred and ninety-two patients with clinical symptoms of BPH were randomly allocated to one of the following treatments for 48 weeks: placebo or the selective aromatase inhibitor, atamestane, at a daily dose of 100 mg or 300 mg. Both doses of atamestane significantly reduced serum concentrations of estradiol and estrone, and produced a slight, dose-dependent, counter-regulatory increase in peripheral androgen concentration. RESULTS: Clinical symptoms improved during treatment in all three groups. Even after 48 weeks, the effect of active treatment did not exceed the effect seen with placebo. Overall tolerance of 100 mg atamestane was excellent, but 300 mg showed a slightly increased incidence of side effects compared with placebo. CONCLUSIONS: The conclusion from this study is that the reduction in estrogen concentration using the selective aromatase inhibitor atamestane has no effect on clinically established BPH

HIFLOW: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics Using Normalizing Flows

International audienceA wealth of cosmological and astrophysical information is expected from many ongoing and upcoming large-scale surveys. It is crucial to prepare for these surveys now and develop tools that can efficiently extract most information. We present HIFLOW: a fast generative model of the neutral hydrogen (HI) maps that is conditioned only on cosmology (Ω m and σ 8) and designed using a class of normalizing flow models, the masked autoregressive flow. HIFLOW is trained on the state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project. HIFLOW has the ability to generate realistic diverse maps without explicitly incorporating the expected two-dimensional maps structure into the flow as an inductive bias. We find that HIFLOW is able to reproduce the CAMELS average and standard deviation HI power spectrum within a factor of ≲2, scoring a very high R 2 > 90%. By inverting the flow, HIFLOW provides a tractable high-dimensional likelihood for efficient parameter inference. We show that the conditional HIFLOW on cosmology is successfully able to marginalize over astrophysics at the field level, regardless of the stellar and AGN feedback strengths. This new tool represents a first step toward a more powerful parameter inference, maximizing the scientific return of future HI surveys, and opening a new avenue to minimize the loss of complex information due to data compression down to summary statistics