Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Asthma-IV: Diagnostic and Management01:30

Asthma-IV: Diagnostic and Management

2.5K
The diagnosis and management of asthma are comprehensive, encompassing clinical assessments, lung function tests, and pharmacological interventions. Here's an overview:
Clinical Assessment for Asthma:
This is the first step in diagnosing and managing asthma. It includes:
2.5K
Asthma-I: Introduction01:29

Asthma-I: Introduction

2.6K
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...
2.6K
Asthma: Pathogenesis and Management01:20

Asthma: Pathogenesis and Management

411
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.
411
Asthma-II: Pathophysiology and Classification01:26

Asthma-II: Pathophysiology and Classification

2.7K
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:
2.7K
Antiasthma Drugs: Mast Cell Stabilizers and Anti-IgE Drugs01:25

Antiasthma Drugs: Mast Cell Stabilizers and Anti-IgE Drugs

305
Asthma is a chronic respiratory condition for which new therapeutic avenues, including anti-inflammatory drugs like mast cell stabilizers and anti-IgE treatments, continue to be developed.
Mast cell stabilizers, such as cromolyn (also known as sodium cromoglycate) and nedocromil (Tilade), are effective drugs in asthma management. These stabilizers hinder histamine release by skillfully obstructing the activation of mast cells and other cellular entities. Notably, they navigate this task without...
305
Drugs Used in Lower Respiratory Disorders: Overview01:17

Drugs Used in Lower Respiratory Disorders: Overview

450
Lower respiratory tract disorders present challenges that often require skilled and nuanced approaches for effective management. Common ailments, such as asthma and chronic obstructive pulmonary disease (COPD), have prompted the development of intricate treatment strategies involving bronchodilators and anti-inflammatory drugs, each tailored to ease breathing and revitalize the lungs.
Bronchodilators, the first step of respiration enhancement, come in various forms, each with its own mechanism...
450

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

An Eight-Century Evolution of Asthma Therapeutics in Western Countries: From Medieval Regimens to Anti-inflammatory Reliever Therapy and Precision Biologics.

Advances in therapy·2026
Same author

AZD8630/AMG 104, an inhaled anti-thymic stromal lymphopoietin antibody fragment, for moderate-to-severe asthma: Phase 1 randomized controlled trial.

The Journal of allergy and clinical immunology·2026
Same author

INSIGHTS Asthma Pragmatic Registry - A Pragmatic Approach to High-Validity Real-World Evidence for Asthma.

Pragmatic and observational research·2025
Same author

Efficacy and Safety of Tezepelumab in Adults With Severe, Uncontrolled Asthma in Asia: Results From the Phase 3 DIRECTION Study.

The journal of allergy and clinical immunology. In practice·2025
Same author

Tezepelumab can Restore Normal Lung Function in Patients with Severe, Uncontrolled Asthma: Pooled Results from the PATHWAY and NAVIGATOR Studies.

Pulmonary therapy·2025
Same author

Tezepelumab for the treatment of chronic spontaneous urticaria: Results of the phase 2b INCEPTION study.

The Journal of allergy and clinical immunology·2025

Related Experiment Video

Updated: Jul 8, 2025

Murine Model of Allergen Induced Asthma
08:05

Murine Model of Allergen Induced Asthma

Published on: May 14, 2012

40.3K

Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review.

Nestor A Molfino1, Gianluca Turcatel2, Daniel Riskin3

  • 1Global Development, Amgen Inc., One Amgen Center Dr, Thousand Oaks, CA, 91320, USA. nmolfino@amgen.com.

Advances in Therapy
|December 19, 2023
PubMed
Summary

Artificial intelligence (AI) and machine learning (ML) can predict asthma exacerbations by analyzing diverse patient data. Further research is needed to explore AI/ML

Keywords:
AlgorithmArtificial intelligenceAsthmaExacerbationMachine learning

More Related Videos

A Reversible, Non-invasive Method for Airway Resistance Measurements and Bronchoalveolar Lavage Fluid Sampling in Mice
09:58

A Reversible, Non-invasive Method for Airway Resistance Measurements and Bronchoalveolar Lavage Fluid Sampling in Mice

Published on: April 13, 2010

22.5K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Related Experiment Videos

Last Updated: Jul 8, 2025

Murine Model of Allergen Induced Asthma
08:05

Murine Model of Allergen Induced Asthma

Published on: May 14, 2012

40.3K
A Reversible, Non-invasive Method for Airway Resistance Measurements and Bronchoalveolar Lavage Fluid Sampling in Mice
09:58

A Reversible, Non-invasive Method for Airway Resistance Measurements and Bronchoalveolar Lavage Fluid Sampling in Mice

Published on: April 13, 2010

22.5K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Area of Science:

  • Healthcare technology
  • Computational medicine
  • Respiratory medicine

Background:

  • Asthma management remains challenging despite advances, with many patients experiencing acute exacerbations.
  • Disease activity is influenced by numerous factors including medical history, environment, and patient habits.
  • Current asthma treatment regimens do not fully prevent exacerbations.

Purpose of the Study:

  • To review the current evidence of artificial intelligence (AI) and machine learning (ML) in asthma management.
  • To explore the potential of AI and ML in predicting asthma exacerbations.
  • To identify future applications and challenges of AI/ML in clinical asthma care.

Main Methods:

  • Literature review of existing scientific evidence on AI and ML in asthma.
  • Analysis of studies demonstrating AI/ML's predictive capabilities for asthma exacerbations.
  • Synthesis of factors influencing asthma exacerbations and their integration into AI/ML models.

Main Results:

  • AI and ML show promise in accurately predicting asthma exacerbations.
  • These technologies can integrate a wide range of patient-specific data for comprehensive analysis.
  • Existing research highlights the potential but also the limitations of current AI/ML applications.

Conclusions:

  • AI and ML offer a promising approach to revolutionize personalized asthma management.
  • Clinical application of AI/ML in asthma care is still in its early stages.
  • Further research is essential to overcome implementation barriers and optimize AI/ML for asthma exacerbation prediction.