Decoding the neuroimmune axis in the atopic march: mechanisms and implications
Bibliographic record
Abstract
The immune and nervous systems have co-evolved complex mechanisms to sense environmental dangers and orchestrate a concerted response to safeguard tissue and mobilize host defenses. This sophisticated interplay, marked by a shared repertoire of receptors and ligands, influences disease pathogenesis. Neuro-immune interactions in allergic diseases are pivotal for symptom development, from anaphylaxis to chronic conditions like asthma and atopic dermatitis. This review explores the neuro-immune interplay within the atopic march, emphasizing its role in host defense, inflammation resolution, and tissue repair. We delve into the multifaceted functions of nociceptors in orchestrating type 2 immune responses and the progression of allergic disorders, focusing on key regulators such as CGRP-RAMP1 and SP-MRGPRB2/A2. Additionally, we discuss the potential of nociceptor neuron-blocking drugs to target neuro-immunity, offering the possibility of reversing the progression of the atopic march. Altogether, we underscore the need for targeted interventions to disrupt the pathological processes and enhance therapeutic outcomes at various stages of the atopic march.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".