The potential of MAO inhibitors as chemotherapeutics in cancer: A literature survey.

Drug resistance in cancer is determined by genetic mutations and adaptations of tumor cells to drug treatments, raising a challenge in the treatment of cancer. Factors such as prolonged drug exposure, genetic variability among patients, and tumor heterogeneity have been established as contributors to rising incidence of drug resistance, prompting ongoing research into alternative therapies and combination treatments to overcome this challenge. Monoamine oxidases (MAOs), including both isoforms MAO-A and MAO-B, are mitochondrial enzymes responsible for the catabolism of monoamine neurotransmitters such as dopamine, norepinephrine, and serotonin.

Inflammaging and Immunosenescence in the Post-COVID Era: Small Molecules, Big Challenges.

Aging naturally involves a decline in biological functions, often triggering a disequilibrium of physiological processes. A common outcome is the altered response exerted by the immune system to counteract infections, known as immunosenescence, which has been recognized as a primary cause, among others, of the so-called long-COVID syndrome. Moreover, the uncontrolled immunoreaction leads to a state of subacute, chronic inflammatory state known as inflammaging, responsible in turn for the chronicization of concomitant pathologies in a self-sustaining process.

Augmented Reality Navigation System (SIRIO) for Neuroprotection in Vertebral Tumoral Ablation.

(1) This study evaluates the impact of the CT-guided SIRIO augmented reality navigation system on the procedural efficacy and clinical outcomes of neuroprotection in vertebral thermal ablation (RTA) for primary and metastatic bone tumors. (2) Methods: A retrospective non-randomized analysis of 28 vertebral RTA procedures was conducted, comparing 12 SIRIO-assisted and 16 non-SIRIO-assisted procedures. The primary outcomes included dose-length product (DLP) and epidural dissection time.

TIRESIA and TISBE: Explainable Artificial Intelligence Based Web Platforms for the Transparent Assessment of the Developmental Toxicity of Chemicals and Drugs.

Developmental toxicity is key human health endpoint, especially relevant for safeguarding maternal and child well-being. It is an object of increasing attention from international regulatory bodies such as the US EPA (US Environmental Protection Agency) and ECHA (European CHemicals Agency). In this challenging scenario, non-test methods employing explainable artificial intelligence based techniques can provide a significant help to derive transparent predictive models whose results can be easily interpreted to assess the developmental toxicity of new chemicals at very early stages.