[Translated article] AEDV Expert Document on the Management of Ulcerative Venereal Infections.

Currently, ulcerative sexually transmitted infections, including syphilis, herpes simplex virus (HSV), lymphogranuloma venereum (LGV), chancroid, donovanosis and, more recently, monkeypox (MPOX), represent a growing challenge for health care professionals. The incidence of syphilis and LGV has increased in recent years in Spain. Additionally, HSV, syphilis and chancroid can also increase the risk of HIV acquisition and transmission.

Causal Learning: Monitoring Business Processes Based on Causal Structures.

Conventional methods for process monitoring often fail to capture the causal relationships that drive outcomes, making hard to distinguish causal anomalies from mere correlations in activity flows. Hence, there is a need for approaches that allow causal interpretation of atypical scenarios (anomalies), allowing to identify the influence of operational variables on these anomalies. This article introduces (), an innovative technique based on causality techniques, applied during the planning phase in business process environments.

A match-pair analysis of single row vs transosseous equivalent double row in massive posterosuperior rotator cuff tears in patients > 70 years old.

Introduction: The aim of this study was to compare the results of single versus double row (TEO) in massive tears of the posterosuperior rotator cuff in patients older than 70 years old.

Methods: Between October 2019 and July 2022, 46 patients, older than 70 years old, were operated on, in two centers, by one surgeon (FM), in one center, we performed a single-row repair, while in the other a double row, transosseous equivalent. Patients were paired by age and gender.

Predicting the translation efficiency of messenger RNA in mammalian cells.

The degree to which translational control is specified by mRNA sequence is poorly understood in mammalian cells. Here, we constructed and leveraged a compendium of 3,819 ribosomal profiling datasets, distilling them into a transcriptome-wide atlas of translation efficiency (TE) measurements encompassing >140 human and mouse cell types. We subsequently developed RiboNN, a multitask deep convolutional neural network, and classic machine learning models to predict TEs in hundreds of cell types from sequence-encoded mRNA features, achieving state-of-the-art performance (r=0.