The modern view on the role of glyprolines by metabolic syndrome

NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study

Neurodegenerative diseases involve progressive neuronal death. Traditional treatments often struggle due to solubility, bioavailability, and crossing the Blood-Brain Barrier (BBB). Nanoparticles (NPs) in biomedical field are garnering growing attention as neurodegenerative disease drugs (NDDs) carrier to the central nervous system. Here, we introduced computational and experimental analysis. In the computational study, a specific IFPTML technique was used, which combined Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) to select the most promising Nanoparticle Neuronal Disease Drug Delivery (N2D3) systems. For the application of IFPTML model in the nanoscience, NANO.PTML is used. IF-process was carried out between 4403 NDDs assays and 260 cytotoxicity NP assays conducting a dataset of 500,000 cases. The optimal IFPTML was the Decision Tree (DT) algorithm which shown satisfactory performance with specificity values of 96.4% and 96.2%, and sensitivity values of 79.3% and 75.7% in the training (375k/75%) and validation (125k/25%) set. Moreover, the DT model obtained Area Under Receiver Operating Characteristic (AUROC) scores of 0.97 and 0.96 in the training and validation series, highlighting its effectiveness in classification tasks. In the experimental part, two samples of NPs (Fe3O4_A and Fe3O4_B) were synthesized by thermal decomposition of an iron(III) oleate (FeOl) precursor and structurally characterized by different methods. Additionally, in order to make the as-synthesized hydrophobic NPs (Fe3O4_A and Fe3O4_B) soluble in water the amphiphilic CTAB (Cetyl Trimethyl Ammonium Bromide) molecule was employed. Therefore, to conduct a study with a wider range of NP system variants, an experimental illustrative simulation experiment was performed using the IFPTML-DT model. For this, a set of 500,000 prediction dataset was created. The outcome of this experiment highlighted certain NANO.PTML systems as promising candidates for further investigation. The NANO.PTML approach holds potential to accelerate experimental investigations and offer initial insights into various NP and NDDs compounds, serving as an efficient alternative to time-consuming trial-and-error procedures.

Neuroprotective Peptides and New Strategies for Ischemic Stroke Drug Discoveries

Ischemic stroke continues to be one of the leading causes of death and disability in the adult population worldwide. The currently used pharmacological methods for the treatment of ischemic stroke are not effective enough and require the search for new tools and approaches to identify therapeutic targets and potential neuroprotectors. Today, in the development of neuroprotective drugs for the treatment of stroke, special attention is paid to peptides. Namely, peptide action is aimed at blocking the cascade of pathological processes caused by a decrease in blood flow to the brain tissues. Different groups of peptides have therapeutic potential in ischemia. Among them are small interfering peptides that block protein-protein interactions, cationic arginine-rich peptides with a combination of various neuroprotective properties, shuttle peptides that ensure the permeability of neuroprotectors through the blood-brain barrier, and synthetic peptides that mimic natural regulatory peptides and hormones. In this review, we consider the latest achievements and trends in the development of new biologically active peptides, as well as the role of transcriptomic analysis in identifying the molecular mechanisms of action of potential drugs aimed at the treatment of ischemic stroke.