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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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Asthma: Pathogenesis and Management01:20

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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.
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Asthma-IV: Diagnostic and Management01:30

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Clinical Assessment for Asthma:
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Asthma-I: Introduction01:29

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Asthma is a chronic respiratory ailment that requires careful management due to its varying symptoms and influencing factors. It is characterized by airway inflammation, bronchial hyperresponsiveness, and reversible airflow obstruction, leading to symptoms like wheezing, shortness of breath, chest tightness, and coughing. The symptom frequency and intensity may vary considerably over time. It is also linked to immune system responses to allergens and irritants, highlighting the complex...
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Background and Environment Affect Phenotype02:27

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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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Asthma-III: Symptoms and Complications01:24

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Asthma, a common chronic respiratory condition, is classified considering the frequency and severity of symptoms alongside lung function impairment. Understanding this classification is essential for appropriate treatment and management. Here's a detailed look at the classification of asthma and its clinical features and complications:
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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A Systematic Review of Asthma Phenotypes Derived by Data-Driven Methods.

Francisco Cunha1, Rita Amaral2,3,4,5, Tiago Jacinto2,4

  • 1Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal.

Diagnostics (Basel, Switzerland)
|April 30, 2021
PubMed
Summary

Data-driven methods for asthma phenotyping show significant variability. Future research should focus on population-based samples and longitudinal consistency for better clinical management of asthma.

Keywords:
asthmaphenotypessystematic reviewsunsupervised analysis

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Area of Science:

  • Pulmonology
  • Clinical Research
  • Data Science

Background:

  • Asthma phenotype classification impacts clinical management.
  • Data-driven phenotyping offers insights into disease heterogeneity.
  • Understanding asthma trait variability is crucial for personalized medicine.

Purpose of the Study:

  • To systematically review and characterize asthma phenotypes identified through data-driven statistical methods.
  • To summarize the common approaches and variables used in data-driven asthma phenotyping.
  • To assess the heterogeneity and consistency of identified asthma phenotypes.

Main Methods:

  • Systematic literature review adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.
  • Inclusion of studies on adult asthma phenotypes derived from easily accessible clinical variables.
  • Methodological quality assessment using the ROBINS-I tool and data extraction by two independent reviewers.

Main Results:

  • 68 studies were included from 7446 initial results.
  • Hierarchical cluster analysis was the most frequent data-driven method (n=19).
  • Key phenotyping domains included personal, functional, and clinical variables; common phenotypes related to atopy, gender, and disease severity.

Conclusions:

  • Significant variability exists in data-driven asthma phenotypes, influenced by sample characteristics and methodology.
  • Current data-driven phenotyping lacks consistent application and validation across diverse populations.
  • Further research necessitates population-based samples and longitudinal assessments to enhance the clinical utility of data-driven asthma phenotypes.