Innate Receptors Expression by Lung Nociceptors: Impact on COVID-19 and Aging
Bibliographic record
Abstract
The lungs are constantly exposed to non-sterile air which carries harmful threats, such as particles and pathogens. Nonetheless, this organ is equipped with fast and efficient mechanisms to eliminate these threats from the airways as well as prevent pathogen invasion. The respiratory tract is densely innervated by sensory neurons, also known as nociceptors, which are responsible for the detection of external stimuli and initiation of physiological and immunological responses. Furthermore, expression of functional innate receptors by nociceptors have been reported; however, the influence of these receptors to the lung function and local immune response is poorly described. The COVID-19 pandemic has shown the importance of coordinated and competent pulmonary immunity for the prevention of pathogen spread as well as prevention of excessive tissue injury. New findings suggest that lung nociceptors can be a target of SARS-CoV-2 infection; what remains unclear is whether innate receptor trigger sensory neuron activation during SARS-CoV-2 infection and what is the relevance for the outcomes. Moreover, elderly individuals often present with respiratory, neurological and immunological dysfunction. Whether aging in the context of sensory nerve function and innate receptors contributes to the disorders of these systems is currently unknown. Here we discuss the expression of innate receptors by nociceptors, particularly in the lungs, and the possible impact of their activation on pulmonary immunity. We then demonstrate recent evidence that suggests lung sensory neurons as reservoirs for SARS-CoV-2 and possible viral recognition via innate receptors. Lastly, we explore the mechanisms by which lung nociceptors might contribute to disturbance in respiratory and immunological responses during the aging process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".