Reversible Perfusion Changes during Acute Attacks in Glucose Transporter Type 1 Deficiency Syndrome: A Pediatric Case Series.

Glucose transporter type 1 deficiency syndrome (GLUT1-DS) is an uncommon condition represented by an infantile-onset disorder, frequently arising from heterozygous mutations in the gene. Individuals with GLUT1-DS may present with early-onset seizures (typically manifesting before 4 years of age), developmental delay, and complex movement disorders. In fewer cases, stroke-like events or hemiplegic migraine-like symptoms are also reported, defined by unilateral paresis affecting 1 side of the body and/or one-half of the face, occasionally accompanied by speech impairment.

Periodontitis in Psoriatic Patients: Epidemiological Insights and Putative Etiopathogenic Links.

Psoriasis, a systemic autoimmune disorder primarily affecting the skin, manifests through erythematous plaques and scales, impacting approximately 2-3% of the global population. Chronic periodontitis, a prevalent oral disease characterized by the destruction of tooth-supporting tissues, affects roughly 10-15% of adults worldwide. Emerging evidence suggests a bidirectional relationship between psoriasis and chronic periodontitis, supported by epidemiological studies indicating a higher prevalence of periodontitis among individuals with psoriasis and vice versa.

AI in Radiology: Navigating Medical Responsibility.

The application of Artificial Intelligence (AI) facilitates medical activities by automating routine tasks for healthcare professionals. AI augments but does not replace human decision-making, thus complicating the process of addressing legal responsibility. This study investigates the legal challenges associated with the medical use of AI in radiology, analyzing relevant case law and literature, with a specific focus on professional liability attribution.

The Role of Artificial Intelligence and Texture Analysis in Interventional Radiological Treatments of Liver Masses: A Narrative Review.

Liver lesions, including both benign and malignant tumors, pose significant challenges in interventional radiological treatment planning and prognostication. The emerging field of artificial intelligence (AI) and its integration with texture analysis techniques have shown promising potential in predicting treatment outcomes, enhancing precision, and aiding clinical decision-making. This comprehensive review aims to summarize the current state-of-the-art research on the application of AI and texture analysis in determining treatment response, recurrence rates, and overall survival outcomes for patients undergoing interventional radiological treatment for liver lesions.