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相关概念视频

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.
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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 (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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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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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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基础科学和病原发生学

Yuhan Cui1, Dhivya Srinivasan1, Zhijian Yang1

  • 1Artificial Intelligence in Biomedical Imaging Laboratory, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

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概括
此摘要是机器生成的。

深度学习确定了不同的大脑衰老模式,揭示了与神经退行症的遗传联系. 这些发现为影响大脑衰老和阿尔茨海默病的遗传因素提供了新的见解.

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科学领域:

  • 神经成像是一种神经成像.
  • 遗传学 遗传学 是一个
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 深度学习模型可以识别异质的大脑衰老模式和神经退行症标志物.
  • 与年龄相关的大脑缩模式的遗传基础是复杂的,需要进一步调查.

研究的目的:

  • 通过深度学习来调查通过深度学习识别的不同大脑衰老模式的遗传关联.
  • 评估以前报告的大脑衰老模式和诊断组之间的关联的可重现性.

主要方法:

  • 从T1扫描和来自阿尔茨海默氏病测序项目 (ADSP) 的全基因组测序 (WGS) 数据中分析了区域大脑体积 (n=2401).
  • 研究了5种已确定的大脑衰老模式 (R指数) 和它们与诊断组 (认知正常与轻度认知障碍/阿尔茨海默病) 的关联.
  • 进行全基因组关联分析以确定与R指数的遗传关联,控制混因素.

主要成果:

  • 在诊断组之间观察到大脑衰老模式 (R2,R3,R5) 的显著差异.
  • 全基因组关联分析确定了5个与特定大脑衰老模式显著相关的基因 (R1,R2,R3,R5).
  • 与R指数相关的确定的基因与各种特征有先前的联系,包括BMI,认知能力,心血管风险因素和血液细胞测量.

结论:

  • 已建立的大脑衰老指数 (R指数) 成功捕捉了ADSP参与者的结构性大脑衰老异质性.
  • 复制了R指数和阿尔茨海默病群体之间的关联,证明了模型的概括性.
  • 发现了大脑衰老模式和特定基因之间的新兴关联,提供了对神经退行异质性和遗传风险因素的见解.