Background: The involvement of intercellular tight junctions and, in particular, the modulation of their competency by the zonulin pathway with a subsequent increase in epithelial and endothelial permeability, has been described in several chronic and acute inflammatory diseases. In this scenario, Larazotide, a zonulin antagonist, could be employed as a viable therapeutic strategy. Objective: The present review aims to describe recent research and current observations about zonulin involvement in several diseases and the use of its inhibitor Larazotide for their treatment. Methods: A systematic search was conducted on PubMed and Google Scholar, resulting in 209 publications obtained with the following search query: “Larazotide,” “Larazotide acetate,” “AT-1001,” “FZI/0” and “INN-202.” After careful examination, some publications were removed from consideration because they were either not in English or were not directly related to Larazotide. Results: The obtained publications were subdivided according to Larazotide’s mechanism of action and different diseases: celiac disease, type 1 diabetes, other autoimmune diseases, inflammatory bowel disease, Kawasaki disease, respiratory (infective and/or non-infective) diseases, and other. Conclusions: A substantial role of zonulin in many chronic and acute inflammatory diseases has been demonstrated in both in vivo and in vitro, indicating the possible efficacy of a Larazotide treatment. Moreover, new possible molecular targets for this molecule have also been demonstrated.
Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition affecting approximately 1 out of 70 (range 1:59 – 1:89) children worldwide. It is characterized by a delay in cognitive capabilities, repetitive and restricted behaviors and deficit in communication and social interaction. Several factors seem to be associated with ASD development; its heterogeneous nature makes the diagnosis difficult and slow since it is essentially based on screening tools focused on stereotypical and repetitive behaviors, gait, facial emotion expression and speech assessments. -: Recently, artificial intelligence (AI) has been widely used to investigate ASD with the overall goal of simplifying and speeding up the diagnostic process as well as making earlier access to therapies possible. The aim of this review is to provide an overview of the state-of-the-art research in the ASD field identifying and describing machine learning (ML) approaches in ASD literature that could be used by clinicians to improve diagnostic capability and treatment efficiency. A systematic search was conducted and the resulting articles were subdivided into several categories reflecting the different fields of study associated with ASD research. The existing literature has widely demonstrated the potential of ML in several types of ASD study analyses: behavior, gait, speech, facial emotion expression, neuroimaging, genetics, and metabolomics. Therefore, AI techniques are becoming increasingly implemented and accepted, so highlighting the power of ML approaches to extract and obtain knowledge from a large volume of data. This makes ML a promising tool for future ASD research and clinical endeavors suggesting possible avenues for improving ASD screening, diagnostic and therapeutic tools.
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