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相关概念视频

Asthma-II: Pathophysiology and Classification01:26

Asthma-II: Pathophysiology and Classification

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Asthma is a prevalent chronic respiratory condition marked by inflammation and hyperresponsiveness of the airways. Its pathophysiology involves complex interactions among inflammatory pathways, immune responses, and neural mechanisms.
Additionally, environmental and genetic factors play crucial roles in determining an individual's susceptibility to asthma and the severity of their condition.
Critical processes in asthma pathophysiology include:
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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Asthma: Pathogenesis and Management01:20

Asthma: Pathogenesis and Management

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Asthma is a chronic pulmonary condition involving inflammation of the airways, hyper-reactivity, and reversible obstruction of the airways. This condition can significantly impact a person's quality of life, making breathing difficult and leading to distressing symptoms.
Asthma is classified as allergic and non-allergic. Allergens such as dust mites, pollen, and pet dander trigger allergic asthma, while factors like cold air, intense emotions, or exercise can induce non-allergic asthma.
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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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这页已由机器翻译。其他页面可能仍然显示为英文。View in English
  1. 首页
  2. 研究领域
  3. 生物医学和临床科学
  4. 瘤学和致癌症
  5. 预测和预后标志物
  6. 综合转录组分析和机器学习揭示了喘和肺癌之间的共享诊断基因和潜在机制

综合转录组分析和机器学习揭示了喘和肺癌之间的共享诊断基因和潜在机制

Ling-Jun Zen1, Jun-Cai Tian1, Xu Hu1

  • 1Department of Pulmonary and Critical Care Medicine, West China Hospital of Sichuan University-Ziyang Hospital, Ziyang Central Hospital, Ziyang Sichuan.

European journal of translational myology
|August 28, 2025

相关实验视频

Author Spotlight: Exploring the Role of Inflammation in the Co-occurrence of Primary Sjogren's Syndrome and Lung Adenocarcinoma
10:21

Author Spotlight: Exploring the Role of Inflammation in the Co-occurrence of Primary Sjogren's Syndrome and Lung Adenocarcinoma

Published on: September 20, 2024

541
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

892
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

405

在PubMed 上查看摘要

概括
此摘要是机器生成的。

这项研究揭示了喘和肺癌之间的共同基因和途径,确定了P2RY14,ANXA3和SLIT2作为这些呼吸系统疾病的潜在诊断生物标志物.

科学领域:

  • 肺部医学
  • 分子生物学
  • 遗传学

背景情况:

  • 肺癌是一个严重的健康问题,
  • 虽然吸烟是主要的危险因素,
  • 喘和肺癌之间的分子联系尚不清楚.

研究的目的:

  • 在喘和肺癌之间识别共同的分子机制和基因.
  • 探索这些疾病的共同致病途径.
  • 发现两种疾病的潜在诊断生物标志物.

主要方法:

  • 综合多队列患者数据.
  • 应用权重基因表达网络分析 (WGCNA) 发现共享的基因.
  • 构建功能性基因网络以分析路径参与.
  • 使用机器学习进行生物标志物选.

主要成果:

  • 在肺癌和喘群体中发现了保存的共享基因.
  • 突出了肺部发育和新陈代谢平衡途径的重要性.
  • 发现了三个枢纽生物标志物:P2RY14,ANXA3和SLIT2.

结论:

相关实验视频

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Author Spotlight: Exploring the Role of Inflammation in the Co-occurrence of Primary Sjogren's Syndrome and Lung Adenocarcinoma

Published on: September 20, 2024

541
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

892
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

405
  • 共同的遗传和路径失调有助于喘和肺癌的发展.
  • P2RY14,ANXA3和SLIT2作为诊断生物标志物非常有前途.
  • 这项研究提供了有关这些呼吸系统疾病的相互联系的见解.