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

Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Mouse Models of Cancer Study02:43

Mouse Models of Cancer Study

Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
The development of transgenic, knockout, and knock-in mice has led to an exponential increase in their use as model organisms in research,...
Chronic Obstructive Pulmonary Disease01:24

Chronic Obstructive Pulmonary Disease

COPD is defined as a heterogeneous lung condition marked by persistent respiratory symptoms such as dyspnea, cough, and sputum production, caused by abnormalities in the airways that cause airflow obstruction.
Smoking is a primary risk factor for COPD, with over 80% of patients having a history of it. Patients typically experience progressive dyspnea or labored breathing, frequent coughing, and recurrent pulmonary infections. Many eventually succumb to respiratory failure, characterized by...
Chronic Obstructive Pulmonary Disease-II: Pathophysiology01:20

Chronic Obstructive Pulmonary Disease-II: Pathophysiology

Chronic Obstructive Pulmonary Disease (COPD) pathophysiology is intricate and multifaceted, involving a complex interplay of physiological processes. Understanding these mechanisms is crucial for effectively managing and treating COPD. Here is an in-depth look at the critical elements in the pathophysiology of COPD:
Chronic Inflammation
Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies01:27

Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies

Assessing and diagnosing Chronic Obstructive Pulmonary Disease (COPD) involves a detailed approach that includes a comprehensive review of medical history, physical examination, and a variety of diagnostic tests. This thorough evaluation is essential to ensure an accurate diagnosis and guide effective management strategies.
Medical History
Chronic Obstructive Pulmonary Disease II: Emphysema01:23

Chronic Obstructive Pulmonary Disease II: Emphysema

Emphysema, a major phenotype of chronic obstructive pulmonary disease (COPD), is characterized by irreversible destruction of alveolar walls and permanent enlargement of distal airspaces. Unlike chronic bronchitis, which primarily affects the airways, emphysema predominantly involves the lung parenchyma, where structural damage leads to airflow limitation.PathophysiologyIt most commonly results from prolonged exposure to cigarette smoke and other toxic gases, particularly cigarette smoke.

You might also read

Related Articles

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

Sort by
Same author

New-onset erythrodermic psoriasis associated with antiepileptic drug use.

Dermatology online journal·2026
Same author

Risk factors associated with clinical-histopathological discordance in keratoacanthoma diagnosis.

Journal of the American Academy of Dermatology·2026
Same author

Dose-dependent phytotoxic effects of biosynthesized ZnO nanoparticles from <i>Saraca asoca</i> on rice seedlings: oxidative stress and growth inhibition.

Physiology and molecular biology of plants : an international journal of functional plant biology·2026
Same author

Expanded chromatin accessibility mapping explains genetic variation associated with complex traits in liver.

American journal of human genetics·2026
Same author

Evaluation of Time and Productivity Costs of Purified Protein Derivative (PPD) Testing.

Quality management in health care·2026
Same author

Mimicking opioid analgesia in cortical pain circuits.

Nature·2026

Related Experiment Video

Updated: Jun 10, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

Implementation of PCA enabled Support Vector Machine using cytokines to differentiate smokers versus nonsmokers.

Seema Singh Saharan1,2,3, Pankaj Nagar1, Kate Townsend Creasy4

  • 1Department of Statistics, University of Rajasthan, Jaipur, India.

Proceedings. International Conference on Computational Science and Computational Intelligence
|November 4, 2024
PubMed
Summary

Machine learning identified key plasma cytokines that distinguish smokers from nonsmokers, improving early disease detection and enabling precision medicine interventions.

Keywords:
ClassificationCytokinesPrediction AccuracyPrincipal Component AnalysisSupport Vector Machine

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Author Spotlight: Development and Characterization of an In Vitro Model to Study Chronic Cigarette Smoke Exposure and Its Impact on Airway Epithelial Cells in COPD Research
09:07

Author Spotlight: Development and Characterization of an In Vitro Model to Study Chronic Cigarette Smoke Exposure and Its Impact on Airway Epithelial Cells in COPD Research

Published on: July 12, 2024

951

Related Experiment Videos

Last Updated: Jun 10, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Author Spotlight: Development and Characterization of an In Vitro Model to Study Chronic Cigarette Smoke Exposure and Its Impact on Airway Epithelial Cells in COPD Research
09:07

Author Spotlight: Development and Characterization of an In Vitro Model to Study Chronic Cigarette Smoke Exposure and Its Impact on Airway Epithelial Cells in COPD Research

Published on: July 12, 2024

951

Area of Science:

  • Biomarker discovery
  • Translational medicine
  • Computational biology

Background:

  • Smoking is linked to severe diseases like COPD, cancer, and cardiac conditions.
  • Cytokines play a role in inflammatory responses associated with smoking-related illnesses.
  • Early diagnosis and intervention are crucial for managing smoking-related diseases.

Purpose of the Study:

  • To investigate the association between elevated plasma cytokine levels and smoking status.
  • To develop a machine learning model for differentiating smokers from nonsmokers using cytokine profiles.
  • To identify key cytokine biomarkers for disease prognosis and diagnosis.

Main Methods:

  • Applied Support Vector Machine (SVM) algorithm to analyze 65 plasma cytokines and traditional biomarkers.
  • Utilized Principal Component Analysis (PCA), 10-fold cross-validation, and variable importance for optimization.
  • Evaluated classification performance using Area Under the Receiver Operating Curve (AUROC).

Main Results:

  • SVM achieved an AUROC of 89.2% (95% CI: 85.4%, 93.1%) in differentiating smokers and nonsmokers.
  • Key cytokines identified include I-TAC, G-CSF-CSF-3, and MDC-CCL22.
  • Optimizing with the top five cytokines improved AUROC to 93% (95% CI: 90.1%, 99.5%).

Conclusions:

  • Machine learning, specifically SVM, effectively identifies smoking status based on plasma cytokine profiles.
  • Selected cytokines serve as potent biomarkers for distinguishing smokers, aiding in early disease detection.
  • These findings support the application of machine learning in translational and precision medicine for smoking-related diseases.