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.

In Silico Prediction of Drug-Drug Interactions Mediated by Cytochrome P450 Isoforms.

Drug-drug interactions (DDIs) can cause drug toxicities, reduced pharmacological effects, and adverse drug reactions. Studies aiming to determine the possible DDIs for an investigational drug are part of the drug discovery and development process and include an assessment of the DDIs potential mediated by inhibition or induction of the most important drug-metabolizing cytochrome P450 isoforms. Our study was dedicated to creating a computer model for prediction of the DDIs mediated by the seven most important P450 cytochromes: CYP1A2, CYP2B6, CYP2C19, CYP2C8, CYP2C9, CYP2D6, and CYP3A4.

Prediction of Protein-ligand Interaction Based on Sequence Similarity and Ligand Structural Features.

Computationally predicting the interaction of proteins and ligands presents three main directions: the search of new target proteins for ligands, the search of new ligands for targets, and predicting the interaction of new proteins and new ligands. We proposed an approach providing the fuzzy classification of protein sequences based on the ligand structural features to analyze the latter most complicated case. We tested our approach on five protein groups, which represented promised targets for drug-like ligands and differed in functional peculiarities.

Functional and structural features of proteins associated with alternative splicing.

The alternative splicing is a mechanism increasing the number of expressed proteins and a variety of these functions. We uncovered the protein domains most frequently lacked or occurred in the splice variants. Proteins presented by several isoforms participate in such processes as transcription regulation, immune response, etc.

Prediction of Protein-Ligand Interaction Based on the Positional Similarity Scores Derived from Amino Acid Sequences.

The affinity of different drug-like ligands to multiple protein targets reflects general chemical-biological interactions. Computational methods estimating such interactions analyze the available information about the structure of the targets, ligands, or both. Prediction of protein-ligand interactions based on pairwise sequence alignment provides reasonable accuracy if the ligands' specificity well coincides with the phylogenic taxonomy of the proteins.

Prediction of Severity of Drug-Drug Interactions Caused by Enzyme Inhibition and Activation.

Drug-drug interactions (DDIs) severity assessment is a crucial problem because polypharmacy is increasingly common in modern medical practice. Many DDIs are caused by alterations of the plasma concentrations of one drug due to another drug inhibiting and/or inducing the metabolism or transporter-mediated disposition of the victim drug. Accurate assessment of clinically relevant DDIs for novel drug candidates represents one of the significant tasks of contemporary drug research and development and is important for practicing physicians.

Drug-drug interaction prediction using PASS.

Simultaneous use of the drugs may lead to undesirable Drug-Drug Interactions (DDIs) in the human body. Many DDIs are associated with changes in drug metabolism that performed by Drug-Metabolizing Enzymes (DMEs). In this case, DDI manifests itself as a result of the effect of one drug on the biotransformation of other drug(s), its slowing down (in the case of inhibiting DME) or acceleration (in case of induction of DME), which leads to a change in the pharmacological effect of the drugs combination.

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes.

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME).

[Determination of Amino Acid Residues Responsible for Specific Interaction of Protein Kinases with Small Molecule Inhibitors].

Identifying amino acid positions that determine the specific interaction of proteins with small molecule ligands, is required for search of pharmaceutical targets, drug design, and solution of other biotechnology problems. We studied applicability of an original method SPrOS (specificity projection on sequence) developed to recognize functionally significant positions in amino acid sequences. The method allows residues specific to functional subgroups to be determined within the protein family based on their local surroundings in amino acid sequences.

[Application of molecular descriptors for recognition of phosphorylation sites in amino acid sequences].

Recognition of the phosphorylation sites in proteins is required for reconstruction of regulatory processes in living systems. This task is complicated because the phosphorylation motifs in amino acid sequences are considerably degenerated. To improve the prediction efficacy researchers often use additional descriptors, which should reflect physicochemical features of site-surrounding regions.

Spatial features of proteins related to their phosphorylation and associated structural changes.

Protein phosphorylation is widely used in biological regulatory processes. The study of spatial features related to phosphorylation sites is necessary to increase the efficacy of recognition of phosphorylation patterns in protein sequences. Using the data on phosphosites found in amino acid sequences, we mapped these sites onto 3D structures and studied the structural variability of the same sites in different PDB entries related to the same proteins.

Prediction of amino acid positions specific for functional groups in a protein family based on local sequence similarity.

The exchange of single amino acid residue in protein can substantially affect the specificity of molecular recognition. Many protein families can be divided into the groups based on specificity to recognized ligands. Prediction of group-discriminating residues within the certain family is extremely necessary for theoretical studies, enzyme engineering, drug design, and so on.