SchizoLMNet: a modified lightweight MobileNetV2- architecture for automated schizophrenia detection using EEG-derived spectrograms.

Schizophrenia (SZ) is a chronic neuropsychiatric disorder characterized by disturbances in cognitive, perceptual, social, emotional, and behavioral functions. The conventional SZ diagnosis relies on subjective assessments of individuals by psychiatrists, which can result in bias, prolonged procedures, and potentially false diagnoses. This emphasizes the crucial need for early detection and treatment of SZ to provide timely support and minimize long-term impacts.

Eugenol attenuates aluminium-induced neurotoxicity in rats by inhibiting the activation of STAT3 and NF-кB.

Aluminium is a common metallic toxicant that easily penetrates the brain and exerts severe pathological effects e.g., oxidative stress, inflammation and neurodegeneration.

Clinical and Laboratory Characteristics and Comorbidities in Asthma Endotypes in Children.

Objective: To estimate the proportion of eosinophilic and non-eosinophilic (NEA) endotypes in pediatric asthma, and to compare the clinical, and laboratory characterisitics, and different comorbidities between the two endotypes in the children.

Methods: Children aged 5 to 14 years of age with clinical and/or laboratory-confirmed asthma attending the pediatric outpatient department of a tertiary care hospital in Eastern India between October 1, 2023 and March 31, 2024, were included in this cross-sectional study. Complete hemogram, absolute eosinophil count (AEC), IgE, and pulmonary function tests were performed in all patients.

Analysis of the stability of a predator-prey model including the memory effect, double Allee effect and Holling type-I functional response.

This study proposes and analyses a revised predator-prey model that accounts for a twofold Allee impact on the rate of prey population expansion. Employing the Caputo fractional-order derivative, we account for memory impact on the suggested model. We proceed to examine the significant mathematical aspects of the suggested model, including the uniqueness, non-negativity, boundedness, and existence of solutions to the noninteger order system.

Drug discovery and mechanism prediction with explainable graph neural networks.

Apprehension of drug action mechanism is paramount for drug response prediction and precision medicine. The unprecedented development of machine learning and deep learning algorithms has expedited the drug response prediction research. However, existing methods mainly focus on forward encoding of drugs, which is to obtain an accurate prediction of the response levels, but omitted to decipher the reaction mechanism between drug molecules and genes.