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Updated: Sep 9, 2025

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Los principales cambios fisiopatológicos en la enfermedad pulmonar proporcionaron una visión molecular basada en un

Swaraj Mohanty1, Poornima Sharma1, Yasmin Ahmad2

  • 1Disruptive & Deterrence Technology (DDT) Division, Defence Institute of Physiology & Allied Sciences (DIPAS), Defence R & D Organization (DRDO), Timarpur, New Delhi, 110054, India.

Scientific reports
|August 31, 2025
PubMed
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Este resumen es generado por máquina.

El diagnóstico tardío de los trastornos pulmonares dificulta el tratamiento eficaz. Este estudio utiliza el aprendizaje automático profundo para identificar vías moleculares clave y biomarcadores para mejorar el diagnóstico y la terapia de enfermedades de las vías respiratorias.

Área de la Ciencia:

  • Medicina de los pulmones
  • La bioinformática
  • Inteligencia artificial

Sus antecedentes:

  • Los trastornos pulmonares presentan importantes desafíos para la salud, a menudo exacerbados por el diagnóstico y el tratamiento tardíos.
  • Los métodos tradicionales luchan con enfermedades complejas de las vías respiratorias debido a una comprensión fisiopatológica y molecular incompleta.
  • La investigación existente carece de datos completos sobre las vías moleculares y las moléculas de la firma, lo que limita las opciones terapéuticas como el óxido nítrico (NO) y el sulfuro de hidrógeno (H2S).

Objetivo del estudio:

  • Aprovechar el aprendizaje automático profundo y el análisis de redes para dilucidar las vías moleculares en las principales enfermedades pulmonares.
  • Identificar actores moleculares críticos y sus interconexiones en la progresión de la enfermedad de las vías respiratorias.
  • Proporcionar una base para nuevas intervenciones terapéuticas dirigidas a las vías identificadas.
Palabras clave:
Vías de la red de enfermedadesOntología genéticaHipóxiaEnfermedad pulmonar- ¿Qué es eso?

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Principales métodos:

  • Un enfoque de aprendizaje automático profundo que utiliza la IA como un motor de búsqueda para analizar estudios científicos y bases de datos.
  • MEDLINE búsqueda de estudios publicados sobre las principales enfermedades pulmonares.
  • La base de datos de los NIH se utiliza para obtener información sobre genes y proteínas.
  • Herramienta ShinyGo empleada para el análisis de vías y moléculas.
  • Análisis bioinformático de perfiles de expresión génica e interacciones proteína-proteína mediante la red STRING.

Principales resultados:

  • Identificación de las vías moleculares clave correlacionadas con enfermedades pulmonares.
  • Demostración de la interacción entre las moléculas perturbadas en la progresión de la enfermedad de las vías respiratorias.
  • El análisis de la red reveló interconexiones moleculares significativas relevantes para la patología de la enfermedad.

Conclusiones:

  • Las moléculas perturbadas juegan un papel crucial en la progresión de las enfermedades de las vías respiratorias.
  • La orientación de las vías moleculares identificadas presenta una estrategia terapéutica potencial para los trastornos pulmonares.
  • Este enfoque impulsado por la IA mejora la comprensión de los mecanismos moleculares para mejorar el manejo de la enfermedad.