Decreasing Hospital-acquired Pressure Injuries During the COVID-19 Pandemic: A 5-step Quality Improvement Approach.

Background: Hospital-acquired pressure injuries (HAPIs) are common adverse events with large burdens on patients and health systems. In 2020, during the initial waves of the COVID-19 pandemic, the incidence of admitted patients with HAPIs of stage II and above in our health system rose from 2.92% to 3.

Extracting information on virus-human interactions and on antiviral compounds based on automated analysis of large text collections.

The development of effective antivirals is of great importance due to the threat associated with the rapid spread of viral infections. The accumulation of data in scientific publications and in databases of biologically active compounds provides an opportunity to extract specific information about interactions between chemicals and their viral and host targets. This information can be used for elucidation of knowledge about potential antiviral activity of chemical compounds, their side effects and toxicities.

[Characteristics of bloodstain patterns in bleeding from a contused wound of head with rupture of a branch of superficial temporal artery].

An expert case is presented in which a man was found dead in his apartment, on the bed. Upon examination of the crime scene, the deceased was found to have a contused wound of the frontoparietal region on the left side. The apartment contained a large number of bloodstains, including patterns characteristic of arterial spurt.

Prediction of viral protease inhibitors using proteochemometrics approach.

Being widely accepted tools in computational drug search, the (Q)SAR methods have limitations related to data incompleteness. The proteochemometrics (PCM) approach expands the applicability area by using description for both protein and ligand structures. The PCM algorithms are urgently required for the development of new antiviral agents.

The method predicting interaction between protein targets and small-molecular ligands with the wide applicability domain.

Prediction of protein-ligand interaction is necessary for drug design, gene regulatory networks investigation, and chemical probes detection. The existing methods commonly demonstrate high prediction accuracy for the particular groups of protein and their ligands. We developed an approach suited for the wider applicability and tested it on three dataset types significantly differing by protein homology.