Diagnóstico de la enfermedad de Wilson y sus fenotipos usando inteligencia artificial
DOI:
https://doi.org/10.54502/msuceva.v3n1a5Palabras clave:
Ciclo de Krebs, ciclo de la urea, cobre, hígado, metabolismo intermediario, mitocondria, red neuronal artificialResumen
La EW es causada por las variantes de ATP7B que alteran el eflujo de cobre y provocan una acumulación excesiva de cobre, principalmente en el hígado y el cerebro. El diagnóstico de la EW se ve dificultado por su evolución clínica variable, su aparición, su morbilidad y el tipo de variante ATP7B. Actualmente se diagnostica mediante una combinación de síntomas/signos clínicos, parámetros aberrantes del metabolismo del cobre (por ejemplo, niveles séricos bajos de ceruloplasmina y concentraciones elevadas de cobre urinario y hepático) y cuando están disponibles, a través de pruebas genéticas de mutaciones ATP7B. Dado que el diagnóstico y el tratamiento precoces son clave para obtener resultados favorables, es fundamental identificar a los sujetos antes de la aparición de manifestaciones clínicas manifiestamente perjudiciales. Con este fin, tratamos de mejorar el diagnóstico de la EW mediante algoritmos de redes neuronales artificiales (parte de la inteligencia artificial) integrando los parámetros clínicos y moleculares disponibles. Sorprendentemente, el diagnóstico de la EW se basó en los niveles plasmáticos de glutamato, asparagina, taurina y el cociente de Fischer. Dado que estos aminoácidos están relacionados con los ciclos urea-Krebs, nuestro estudio no sólo subraya el papel central de las mitocondrias hepáticas en la patología de la EW, sino también que la mayoría de los pacientes con EW presentan una disfunción hepática subyacente. Nuestro estudio aporta pruebas novedosas de que la inteligencia artificial utilizada para el análisis integrado de la EW puede dar lugar a un diagnóstico más precoz y a tratamientos mecánicamente relevantes para los pacientes con EW.
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