Effect of acute administration of different ketamine doses on anxiety and metabolic activity of the amygdala in rats.

Introduction: Anxiety has been described in the initial stages of schizophrenia, and affective flattening in the chronic illness. The etiology remains unknown. Ketamine, a noncompetitive N-Methyl-D-amino-aspartate acid (NMDA) receptor antagonist, is used in rats as a translational model of schizophrenia.

An Explainable Radiomics-Based Classification Model for Sarcoma Diagnosis.

: This study introduces an explainable, radiomics-based machine learning framework for the automated classification of sarcoma tumors using MRI. The approach aims to empower clinicians, reducing dependence on subjective image interpretation. : A total of 186 MRI scans from 86 patients diagnosed with bone and soft tissue sarcoma were manually segmented to isolate tumor regions and corresponding healthy tissue.

Cardiac mechanical synchrony couples with myocardial sympathetic innervation. A retrospective study with novel echocardiographic assessment in patients with I-mIBG imaging.

Purpose: Left ventricular (LV) mechanical synchrony is essential for efficient cardiac function, and its disruption (dyssynchrony) negatively impacts outcomes, particularly in heart failure. Although sympathetic innervation modulates electrical activation and contraction timing, its relationship with mechanical synchrony is underexplored. This study aimed to assess their association using echocardiographic synchrony indices and I-mIBG scintigraphy, introducing a novel index (AMP-sync).

Feature Selection in Healthcare Datasets: Towards a Generalizable Solution.

Background And Objective: The increasing dimensionality of healthcare datasets presents major challenges for clinical data analysis and interpretation. This study introduces a scalable ensemble feature selection (FS) strategy optimized for multi-biometric healthcare datasets aiming to: address the need for dimensionality reduction, identify the most significant features, improve machine learning models' performance, and enhance interpretability in a clinical context.

Methods: The novel waterfall selection, that integrates sequentially (a) tree-based feature ranking and (b) greedy backward feature elimination, produces as output several sets of features.

Toward New Assessment in Sarcoma Identification and Grading Using Artificial Intelligence Techniques.

Sarcomas are a rare and heterogeneous group of malignant tumors, which makes early detection and grading particularly challenging. Diagnosis traditionally relies on expert visual interpretation of histopathological biopsies and radiological imaging, processes that can be time-consuming, subjective and susceptible to inter-observer variability. In this study, we aim to explore the potential of artificial intelligence (AI), specifically radiomics and machine learning (ML), to support sarcoma diagnosis and grading based on MRI scans.