Corrigendum to "LogNNet model as a fast, simple and economical AI instrument in the diagnosis and prognosis of COVID-19" [MethodsX 10 (2023) 102194].

[This corrects the article DOI: 10.1016/j.mex.

Disruption of RNA Splicing Increases Vulnerability of Cells to DNA-PK Inhibitors.

DNA-dependent protein kinase (DNA-PK) is a key effector of non-homologous end joining (NHEJ)-mediated double-strand break (DSB) repair. Since its identification, a substantial body of evidence has demonstrated that DNA-PK is frequently overexpressed in cancer, plays a critical role in tumor development and progression, and is associated with poor prognosis in cancer patients. Recent studies have also uncovered novel functions of DNA-PK, shifting the paradigm of the role of DNA-PK in oncogenesis and renewing interest in targeting DNA-PK for cancer therapy.

Experimental Reconstruction of the Optic Nerve with a Sural Nerve Graft: An in Vivo Experimental Study.

Background: Neurosurgical interventions and trauma are common causes of damage to the optic nerve. This determines the relevance of research for solutions aimed at restoration of the nerve's anatomical integrity, electrical conductivity, and subsequently - restoration of its function. Restore a damaged (cut) optic nerve using n.

Novel missense variants in brain morphogenic genes associated with depression and schizophrenia.

Introduction: Impaired function of brain morphogenic genes is considered one of the predisposing factors for the manifestation of psychiatric and cognitive disorders, such as paranoid schizophrenia (SCZ) and major depressive disorder (MDD). Identification of such genes (genes of neurotrophic factors and guidance molecules among them) and their deleterious genetic variants serves as a key to diagnosis, prevention, and possibly treatment of such disorders. In this study, we have examined the prevalence of genomic variants in brain morphogenic genes in individuals with SCZ and MDD within a Russian population.

Entropy-Based Machine Learning Model for Fast Diagnosis and Monitoring of Parkinson's Disease.

This study presents the concept of a computationally efficient machine learning (ML) model for diagnosing and monitoring Parkinson's disease (PD) using rest-state EEG signals (rs-EEG) from 20 PD subjects and 20 normal control (NC) subjects at a sampling rate of 128 Hz. Based on the comparative analysis of the effectiveness of entropy calculation methods, fuzzy entropy showed the best results in diagnosing and monitoring PD using rs-EEG, with classification accuracy () of ~99.9%.