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Bronchopulmonary afferent nerves.
Open accessAbstract
Abstract: Vagal afferent nerves are the primary communication pathways between the bronchopulmonary system and the central nervous system. Input from airway afferent nerves to the CNS is integrated in the brainstem and ultimately leads to sensations and various reflex outputs. Afferent nerves innervating the airways can be classified into various distinct phenotypes. However, there is no single classification scheme that takes all features, including conduction velocity, cell body diameter, ganglionic origin, and stimuli to which they respond (modality) into account. At present, bronchopulmonary afferent nerves are typically considered to belong to one of three general categories, namely C‐fibres, rapidly adapting stretch receptors (RARs), and slowly adapting stretch receptors (SARs). As our understanding of bronchopulmonary afferent nerves continues to deepen, we are likely to see more sophisticated classification schemes emerge. It is clear that the function of afferent fibres can be substantively influenced by airway inflammation and remodelling. The perturbations and perversions of afferent nerve function that occur during these states almost certainly contributes to many of the signs and symptoms of inflammatory airway disease. A more lucid characterization of bronchopulmonary afferent nerves, and a better understanding of the mechanisms by which these nerves influence pulmonary physiology during health and disease anticipates future research.
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“Cardiovascular and respiratory afferents activated by breathing are known to project to second-order relay neurons in brainstem autonomic regions, most notably the NTS via the vagus nerve (Malpas, 2002; Carr and Undem, 2003; Kubin et al., 2006).”
From Hypothesis: Pulmonary Afferent Activity Patterns During Slow, Deep Breathing Contribute to the Neural Induction of Physiological Relaxation (2019) · cites this paper as Carr and Undem, 2003
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“RARs have phasic responses to large lung inflations and deflations (Kubin et al., 2006), whereas SARs respond to large hyperinflations (e.g., 30 cm H2O) throughout the duration of the stimulus (Figure 1) (Carr and Undem, 2003).”
From Hypothesis: Pulmonary Afferent Activity Patterns During Slow, Deep Breathing Contribute to the Neural Induction of Physiological Relaxation (2019) · cites this paper as Carr and Undem, 2003
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Metadata
- Authors
- CARR, M. J., & UNDEM, B. J.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- CARR, Michael J.
- Venue
- Respirology
- Publisher
- Wiley
- Published
- 2003-08-07
- Volume
- 8
- Issue
- 3
- Pages
- 291-301
- ISSN
- 1323-7799 (ISSN-L)
- 1440-1843
- OpenAlex
- W1972490783
- Cited by
- 147
- References
- 82
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 4 · 2023: 5 · 2022: 11 · 2021: 10 · 2020: 14 · 2019: 7 · 2018: 7 · 2017: 4 · 2016: 3 · 2015: 4 · 2014: 6 · 2013: 7 · 2012: 3
- FWCI
- 0.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Respirology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Respiratory and Cough-Related Research
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Asthma and respiratory diseases
- Life Sciences › Neuroscience › Endocrine and Autonomic Systems › Neuroscience of respiration and sleep
- Keywords
- vagal afferents, airway sensory nerves, C-fibers, airway inflammation, airway remodeling, inflammatory airway disease, airway reflexes
- MeSH terms
- Afferent Pathways, Bronchi, Humans, Lung, Neural Conduction, Neuronal Plasticity, Pulmonary Stretch Receptors, Respiratory System, Sneezing, Neurotransmitter Agents
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.