Risk Factors and Pain Management in the Incidence of Postoperative Delirium in Elderly Patients: A Retrospective Study.

: Postoperative delirium (POD) is a common surgical complication that increases hospital stay duration, hospitalization costs, readmission rates and mortality. This study aims to describe the incidence of POD in an elderly patient population and to investigate pain assessment as a risk factor for postoperative confusion. Additionally, we aim to determine a predictive model for POD.

Diagnostic Accuracy of the Abbott BinaxNOW COVID-19 Antigen Card Test, Puerto Rico.

Background: The COVID-19 pandemic underscored the need for rapid and accurate diagnostic tools. In August 2020, the Abbott BinaxNOW COVID-19 Antigen Card test became available as a timely and affordable alternative for SARS-CoV-2 molecular testing, but its performance may vary due to factors including timing and symptomatology. This study evaluates BinaxNOW diagnostic performance in diverse epidemiological contexts.

Prolactin promotes the recruitment of main olfactory bulb cells and enhances the behavioral exploration toward a socio-sexual stimulus in female mice.

Olfactory communication is triggered by pheromones that profoundly influence neuroendocrine responses to drive social interactions. Two principal olfactory systems process pheromones: the main and the vomeronasal or accessory system. Prolactin receptors are expressed in both systems suggesting a participation in the processing of olfactory information.

Extremely missing numerical data in Electronic Health Records for machine learning can be managed through simple imputation methods considering informative missingness: A comparative of solutions in a COVID-19 mortality case study.

Background And Objective: Reusing Electronic Health Records (EHRs) for Machine Learning (ML) leads on many occasions to extremely incomplete and sparse tabular datasets, which can hinder the model development processes and limit their performance and generalization. In this study, we aimed to characterize the most effective data imputation techniques and ML models for dealing with highly missing numerical data in EHRs, in the case where only a very limited number of data are complete, as opposed to the usual case of having a reduced number of missing values.

Methods: We used a case study including full blood count laboratory data, demographic and survival data in the context of COVID-19 hospital admissions and evaluated 30 processing pipelines combining imputation methods with ML classifiers.