Hypogonadism in the Aging Male Diagnosis, Potential Benefits, and Risks of Testosterone Replacement Therapy

Hypogonadism in older men is a syndrome characterized by low serum testosterone levels and clinical symptoms often seen in hypogonadal men of younger age. These symptoms include decreased libido, erectile dysfunction, decreased vitality, decreased muscle mass, increased adiposity, depressed mood, osteopenia, and osteoporosis. Hypogonadism is a common disorder in aging men with a significant percentage of men over 60 years of age having serum testosterone levels below the lower limits of young male adults. There are a variety of testosterone formulations available for treatment of hypogonadism. Data from many small studies indicate that testosterone therapy offers several potential benefits to older hypogonadal men. A large multicenter NIH supported double blind, placebo controlled study is ongoing, and this study should greatly enhance the information available on efficacy and side effects of treatment. While safety data is available across many age groups, there are still unresolved concerns associated with testosterone therapy. We have reviewed the diagnostic methods as well as benefits and risks of testosterone replacement therapy for hypogonadism in aging men

Twin pregnancy with rupture of cerebral arterio venous malformation with intracranial hemorrhage

Cerebral AV malformation in pregnancy is a rare condition with a prevalence rate of approximately 0.01-0.5%. It generally presents symptoms at 20-40 years of age most commonly at around 30 years of age. It affects both men and women but more prevalent in women at this age group. We presented a case of primi gravida at 22 weeks of gestation with DCDA type of twin presenting in emergency department with intracranial hemorrhage secondary to rupture of AV malformation.

Abstract syntax as interlingua: Scaling up the grammatical framework from controlled languages to robust pipelines

Syntax is an interlingual representation used in compilers. Grammatical Framework (GF) applies the abstract syntax idea to natural languages. The development of GF started in 1998, first as a tool for controlled language implementations, where it has gained an established position in both academic and commercial projects. GF provides grammar resources for over 40 languages, enabling accurate generation and translation, as well as grammar engineering tools and components for mobile and Web applications. On the research side, the focus in the last ten years has been on scaling up GF to wide-coverage language processing. The concept of abstract syntax offers a unified view on many other approaches: Universal Dependencies, WordNets, FrameNets, Construction Grammars, and Abstract Meaning Representations. This makes it possible for GF to utilize data from the other approaches and to build robust pipelines. In return, GF can contribute to data-driven approaches by methods to transfer resources from one language to others, to augment data by rule-based generation, to check the consistency of hand-annotated corpora, and to pipe analyses into high-precision semantic back ends. This article gives an overview of the use of abstract syntax as interlingua through both established and emerging NLP applications involving GF

Gynecomastia and hypertension in a patient treated with posaconazole

Posaconazole therapy may lead to increased serum estradiol levels and development of gynecomastia. Early detection by endocrine hormone measurements may help preventing gynecomastia

Quantum circuit fidelity estimation using machine learning

The computational power of real-world quantum computers is limited by errors. When using quantum computers to perform algorithms which cannot be efficiently simulated classically, it is important to quantify the accuracy with which the computation has been performed. In this work we introduce a machine-learning-based technique to estimate the fidelity between the state produced by a noisy quantum circuit and the target state corresponding to ideal noise-free computation. Our machine learning model is trained in a supervised manner, using smaller or simpler circuits for which the fidelity can be estimated using other techniques like direct fidelity estimation and quantum state tomography. We demonstrate that, for simulated random quantum circuits with a realistic noise model, the trained model can predict the fidelities of more complicated circuits for which such methods are infeasible. In particular, we show the trained model may make predictions for circuits with higher degrees of entanglement than were available in the training set, and that the model may make predictions for non-Clifford circuits even when the training set included only Clifford-reducible circuits. This empirical demonstration suggests classical machine learning may be useful for making predictions about beyond-classical quantum circuits for some non-trivial problems.Comment: 27 pages, 6 figure

The Longest path transit function of a graph and betweenness

Postprint (published version