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Infection01:20

Infection

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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.
The chain begins with pathogens: bacteria, viruses, fungi, prions, or parasites such as protozoa helminths. These can be present on the skin as transient or resident flora, or they can be acquired from the environment. Identifying and treating the type of infection and...
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Urinary Tract Infection II: Pathophysiology01:25

Urinary Tract Infection II: Pathophysiology

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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.
CF is primarily caused by a genetic mutation in a chromosome 7 gene coding for the cystic fibrosis transmembrane conductance regulator (CFTR) protein. The most common gene mutation leading to CF is the ΔF508 mutation,...
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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

Stages of Infection

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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

Defense Against Bacterial Pathogens

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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.
Phagocytes
Phagocytes are the frontline soldiers of the immune system. They include neutrophils and macrophages. Neutrophils are the most abundant type of white blood cell and are quickly mobilized to the site of infection. Macrophages are larger cells that patrol...
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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

Bojian Hou1, Zhanliang Wang1, Zhuoping Zhou1

  • 1University of Pennsylvania, Philadelphia, PA, USA.

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

Desarrollamos Fair Representation CCA (FR-CCA) para reducir el sesgo demográfico en el diagnóstico de la enfermedad de Alzheimer utilizando datos multimodales. FR-CCA mejora la equidad hasta en un 105 % manteniendo una alta precisión diagnóstica.

Palabras clave:
sesgo demográficodiagnóstico de la enfermedad de Alzheimerdatos multimodalesequidadprecisión diagnósticaaprendizaje automáticorepresentaciones justasimágenes cerebralesbiomarcadoresmedicina de precisión

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Área de la Ciencia:

  • Ciencia de datos biomédicos
  • Aprendizaje automático para la atención médica
  • Neurociencia

Sus antecedentes:

  • El diagnóstico de la enfermedad de Alzheimer (EA) utiliza cada vez más datos multimodales (neuroimagen, biomarcadores).
  • Los sesgos demográficos en los datos pueden generar disparidades en la precisión diagnóstica entre grupos.
  • Los métodos tradicionales como CCA pueden amplificar estas inequidades.

Objetivo del estudio:

  • Desarrollar un marco novedoso, Fair Representation CCA (FR-CCA), para el análisis equitativo de biomarcadores de la enfermedad de Alzheimer.
  • Armonizar la equidad y la utilidad diagnóstica en la integración de datos multimodales para la DA.
  • Abordar los sesgos demográficos en las representaciones aprendidas para mejorar la toma de decisiones clínicas.

Principales métodos:

  • Se propuso FR-CCA, un marco que maximiza las correlaciones intermodales al tiempo que garantiza la independencia estadística de los atributos sensibles (edad, sexo).
  • Se aprendieron representaciones de baja dimensión a partir de datos multimodales.
  • Se validó FR-CCA en el conjunto de datos Alzheimer's Disease Neuroimaging Initiative (ADNI), comparándolo con CCA convencional y variantes justas.

Principales resultados:

  • FR-CCA demostró intercambios superiores entre equidad y rendimiento, mejorando la equidad en un 26-105 % en datos de ADNI con una pérdida de correlación mínima (<2 %).
  • Redujo significativamente las brechas de paridad demográfica (62-76 %) y de igualdad de oportunidades (81-88 %).
  • Redujo el sesgo relacionado con el sexo en las modalidades de RM y PET en un 84-90 % manteniendo un rendimiento competitivo de clasificación.

Conclusiones:

  • FR-CCA ofrece una solución clínicamente útil para el diagnóstico equitativo de la enfermedad de Alzheimer.
  • Equilibra la equidad y la precisión, garantizando una generalización robusta en diversas poblaciones.
  • Proporciona un marco escalable para otras aplicaciones médicas de alto riesgo que requieren IA equitativa.