Reviewer #1 (Public review): Nociceptor Neurons Control Pollution-Mediated Neutrophilic Asthma
Bibliographic record
Abstract
The immune and sensory nervous systems, having evolved together, use a shared language of receptors and transmitters to maintain homeostasis by responding to external and internal disruptions. Although beneficial in many cases, neurons can exacerbate inflammation during allergic reactions, such as asthma. Our research modeled asthma aggravated by pollution, exposing mice to ambient PM2.5 particles and ovalbumin. This exposure significantly increased bronchoalveolar lavage fluid neutrophils and γδ T cells compared to exposure to ovalbumin alone. We normalized airway inflammation and lung neutrophil levels by silencing nociceptor neurons at inflammation’s peak using intranasal QX-314 or ablating TRPV1-expressing neurons. Additionally, we observed heightened sensitivity in chemical-sensing TRPA1 channels in neurons from pollution-exacerbated asthmatic mice. Elevated levels of artemin were detected in the bronchoalveolar lavage fluid from pollution-exposed mice, with artemin levels normalizing in mice with ablated nociceptor neurons. Upon exposure PM2.5 particles, alveolar macrophages expressing pollution-sensing aryl hydrocarbon receptors, were identified as the source of artemin. This molecule enhanced TRPA1 responsiveness and increased neutrophil influx, providing a novel mechanism by which lung-innervating neurons respond to air pollution and suggesting a potential therapeutic target for controlling neutrophilic airway inflammation in asthma, a clinically intractable condition.
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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 | 0.032 | 0.227 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.017 | 0.007 |
| Insufficient payload (model declined to judge) | 0.077 | 0.041 |
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".