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Related Concept Videos

Respiratory Syncytial Virus Disease01:29

Respiratory Syncytial Virus Disease

Human respiratory syncytial virus (RSV) is a widespread pathogen that primarily targets infants and young children but also poses a serious health risk to elderly and immunocompromised individuals. Belonging to the Pneumoviridae family, RSV is a negative-sense, single-stranded RNA virus within the Pneumovirus genus. Its global health burden is significant, with millions of cases annually resulting in hospitalizations and mortality, particularly in resource-limited settings. Although most...
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Assessment of Ventilation I: Respiratory Rate

Assessment of Ventilation
A Ventilation assessment is critical for monitoring a patient's health status. Respiration, one of the most accessible vital signs, provides insights into the function of numerous body systems and can indicate serious health issues, such as brainstem injuries from head trauma.
Critical Guidelines for Assessing Ventilation:
Olfactory Receptors: Location and Structure01:03

Olfactory Receptors: Location and Structure

The process of olfaction, also known as the sense of smell, is a sophisticated chemical response system. The specialized sensory neurons that facilitate this process, known as olfactory receptor neurons, are situated in an upper segment of the nasal cavity, known as the olfactory epithelium. Olfactory sensory neurons are bipolar, with their dendrites extending from the epithelium's apex into the mucus that lines the nasal cavity. Airborne molecules, when inhaled, traverse the olfactory...
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Respiratory system abnormalities are a significant concern in healthcare due to their potential to indicate underlying severe conditions like Chronic Obstructive Pulmonary Disease (COPD), asthma, and pneumonia. These abnormalities can often be detected through physical examination methods like inspection and percussion.
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Assessment of Respiration

The respiratory system's basic structures and primary functions lay the foundation for nurses' comprehensive respiratory assessments. This assessment includes subjective and objective data to gauge the patient's respiratory health.
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Related Experiment Video

Updated: Jun 24, 2026

Real-time In Vitro Monitoring of Odorant Receptor Activation by an Odorant in the Vapor Phase
09:53

Real-time In Vitro Monitoring of Odorant Receptor Activation by an Odorant in the Vapor Phase

Published on: April 23, 2019

Real-Time and Non-Invasive Detection of Respiratory Viral Infections Using an Intelligent Odor Monitoring System

Yajie Shen1, Weifeng Yuan1, Long Li2

  • 1State Key Laboratory of Virology and Biosafety, Institute for Vaccine Research, College of Life Sciences, Wuhan University, Wuhan, Hubei, P. R. China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|June 23, 2026
PubMed
Summary

This study developed an Intelligent Odor Monitoring System (IOMS) for early respiratory infection detection using volatile organic compounds (VOCs). The system achieved 99.88% accuracy in identifying infection stages in mice, enabling early detection within 9 hours post-infection.

Keywords:
machine learningrespiratory infectionsensor array

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Last Updated: Jun 24, 2026

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08:23

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Published on: March 9, 2018

Area of Science:

  • Biomedical Engineering
  • Respiratory Medicine
  • Analytical Chemistry

Background:

  • Real-time monitoring of volatile organic compounds (VOCs) presents a non-invasive method for early respiratory infection detection.
  • Challenges include complex VOC mixtures, low analyte concentrations, and biological variability, hindering diagnostic precision.

Purpose of the Study:

  • To introduce an Intelligent Odor Monitoring System (IOMS) for enhanced early respiratory infection detection.
  • To identify key infection-associated VOC biomarkers to guide sensor development.
  • To validate the system's accuracy and generalizability in preclinical models.

Main Methods:

  • Developed a high-sensitivity sensor array integrated into an individually ventilated cage (IVC) platform, guided by identified biomarkers (ethyl lactate, 3,5-dimethyloctane).
  • Collected longitudinal VOC data at 1 Hz over a 7-day infection cycle in a murine model.
  • Utilized machine learning models (KNN, SVM, LDA) to analyze temporal VOC dynamics for infection stage modeling.

Main Results:

  • The IOMS captured distinct odor-response dynamics between infected and uninfected groups, revealing stage-associated temporal patterns.
  • Machine learning models achieved up to 99.88% internal accuracy and demonstrated generalizability in a blinded cohort.
  • Early sensor-response shifts were detected at 7-8 hours post-infection (hpi), with robust discrimination by 9 hpi.

Conclusions:

  • The biomarker-guided IOMS, coupled with machine learning, enables autonomous, continuous surveillance for preclinical respiratory infection studies.
  • This approach highlights the potential of longitudinal VOC monitoring for early and precise detection of respiratory infections.
  • The system demonstrates feasibility for early-stage infection monitoring in murine models, paving the way for future clinical applications.