[Glial fibrillary acidic protein: is there a relationship with the course of multiple sclerosis and traumatic brain injury before the onset of multiple sclerosis?].

Objective: To evaluate the relationship between the level of glial fibrillary acidic protein (GFAP) in the blood and the rate of progression (RP) of neurological disorders and exacerbation frequency of multiple sclerosis (MS) in the presence/absence of traumatic brain injury (TBI) before the onset of MS.

Material And Methods: Caucasians born and living in the Altai region of Russia with relapsing-remitting MS in remission took part in a cross-sectional observational randomized study: 43 patients without a history of TBI and 43 patients with TBI before the onset of MS (mean age 36.1±9.

[Trust in socialization institutions as a necessary social condition for effective integration of disabled children into society].

The article is devoted to the analysis of the problem of trust in the institutions of socialization of children with disabilities. The role of such institutions of socialization of disabled children as family, education, healthcare, public organizations, and the media is analyzed. The analysis was based on the results of a sociological study conducted in May-June 2023 among family members raising disabled children (Moscow, St.

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information.

Federated multipartner machine learning has been touted as an appealing and efficient method to increase the effective training data volume and thereby the predictivity of models, particularly when the generation of training data is resource-intensive. In the landmark MELLODDY project, indeed, each of ten pharmaceutical companies realized aggregated improvements on its own classification or regression models through federated learning. To this end, they leveraged a novel implementation extending multitask learning across partners, on a platform audited for privacy and security.

Developing a Kinase-Specific Target Selection Method Using a Structure-Based Machine Learning Approach.

Introduction: Despite recent advances in the drug discovery field, developing selective kinase inhibitors remains a complicated issue for a number of reasons, one of which is that there are striking structural similarities in the ATP-binding pockets of kinases.

Objective: To address this problem, we have designed a machine learning model utilizing various structure-based and energy-based descriptors to better characterize protein-ligand interactions.

Methods: In this work, we use a dataset of 104 human kinases with available PDB structures and experimental activity data against 1202 small-molecule compounds from the PubChem BioAssay dataset "Navigating the Kinome".

Prediction of the secondary structure of short DNA aptamers.

Aptamers have a spectrum of applications in biotechnology and drug design, because of the relative simplicity of experimental protocols and advantages of stability and specificity associated with their structural properties. However, to understand the structure-function relationships of aptamers, robust structure modeling tools are necessary. Several such tools have been developed and extensively tested, although most of them target various forms of biological RNA.