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Infection01:20

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When a pathogen enters the body and reproduces, it can cause an infection, damage body cells, and cause illness symptoms that eventually lead to disease. Therefore, its prevention requires breaking the chain of infection.
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Urinary Tract Infection II: Pathophysiology01:25

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The pathophysiology of urinary tract infections (UTIs) encompasses several progressive stages, beginning with bacterial colonization and culminating in potential systemic complications if untreated. UTIs are primarily initiated by bacteria, such as Escherichia coli, which often originate from the gastrointestinal tract and migrate to the urinary system through the periurethral area. This migration can occur via several routes, including improper hygiene practices, sexual activity, or...
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Cystic Fibrosis: Pathogenesis01:23

Cystic Fibrosis: Pathogenesis

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Cystic fibrosis (CF), an autosomal recessive disorder, significantly affects the function of exocrine glands. This genetically inherited disease is characterized by the production of thick and sticky mucus, which can severely affect various organs and systems in the body.
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Pneumonia II: Pathophysiology01:29

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The pathophysiology of pneumonia involves the following steps:
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Stages of Infection01:26

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Stages of infection describe what happens to a susceptible host once a pathogen invades the human body. The stages of infection are incubation, prodromal, illness, stage of decline, and convalescence. The incubation stage is the period from exposure to a pathogen until symptoms start. The infected person is unaware of impending illness as the pathogens grow and multiply within the body. The duration may vary depending on the type of infection. The incubation period of measles averages ten to...
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Defense Against Bacterial Pathogens01:31

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The human immune system is a complex network of cells, tissues, and organs that work together to defend the body against bacterial infections. It consists of various immune cells, each playing a specific role in the defense mechanism.
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Video Experimental Relacionado

Updated: Jan 7, 2026

Mouse Footpad Inoculation Model to Study Viral-Induced Neuroinflammatory Responses
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Ciencia básica y patogénesis

Anil R Wadhwani1, David C Goldberg2, Philip L De Jager3

  • 1Department of Neurology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.

Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
PubMed
Resumen
Este resumen es generado por máquina.

La metilación diferencial del ADN (DNAm) puede distinguir la tauopatía primaria relacionada con la edad (PART) de la enfermedad de Alzheimer (EA). Una herramienta de aprendizaje automático que utiliza DNAm predice con precisión PART vs. EA y estratifica casos indeterminados, revelando programas biológicos distintos y resiliencia cognitiva en PART.

Palabras clave:
metilación del ADNtauopatía primaria relacionada con la edadenfermedad de Alzheimeraprendizaje automáticoepigenética

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Área de la Ciencia:

  • Neurociencia
  • Genética
  • Biología Computacional

Sus antecedentes:

  • La tauopatía primaria relacionada con la edad (PART) y la enfermedad de Alzheimer (EA) comparten patología tau pero difieren en la carga de amiloide.
  • La distinción entre PART y EA es crucial para comprender la progresión de la enfermedad y desarrollar terapias dirigidas.
  • La metilación diferencial del ADN (DNAm) ofrece información sobre mecanismos biológicos específicos de la enfermedad.

Objetivo del estudio:

  • Desarrollar y validar un clasificador de aprendizaje automático utilizando DNAm para diferenciar PART de EA.
  • Aplicar el clasificador a casos neuropatológicamente indeterminados para estratificarlos en grupos similares a PART o similares a EA.
  • Investigar las diferencias biológicas y cognitivas entre los grupos predichos de PART y EA.

Principales métodos:

  • Se entrenó un clasificador de máquina de vectores de soporte con datos de DNAm de 707 individuos (cohorte ROSMAP) con PART o EA confirmados.
  • El clasificador se validó en una cohorte independiente de 142 casos del Mount Sinai Brain Bank.
  • Se compararon los perfiles neuropatológicos, cognitivos, de DNAm y transcriptómicos entre los grupos definidos por el clasificador.

Principales resultados:

  • El clasificador de DNAm logró una alta precisión en la distinción de PART de EA en cohortes.
  • Los casos neuropatológicamente indeterminados se estratificaron en grupos Predichos-PART y Predichos-EA.
  • Estos grupos, a pesar de una carga similar de tau y amiloide, exhibieron patrones distintos de DNAm, perfiles de expresión génica y rendimiento cognitivo (puntuaciones MMSE).

Conclusiones:

  • La DNAm sirve como un biomarcador robusto para diferenciar PART de EA.
  • La herramienta de aprendizaje automático desarrollada predice con precisión PART vs. EA y estratifica casos indeterminados.
  • Programas epigenéticos y biológicos distintos subyacen a la PART, lo que podría contribuir a la resiliencia cognitiva frente a la patología de la EA.