CD11b <sup>+</sup> Ly6G <sup>−</sup> myeloid cells mediate mechanical inflammatory pain hypersensitivity
Bibliographic record
Abstract
Pain hypersensitivity at the site of inflammation as a result of chronic immune diseases, pathogenic infection, and tissue injury is a common medical condition. However, the specific contributions of the innate and adaptive immune system to the generation of pain during inflammation have not been systematically elucidated. We therefore set out to characterize the cellular and molecular immune response in two widely used preclinical models of inflammatory pain: (i) intraplantar injection of complete Freund's adjuvant (CFA) as a model of adjuvant- and pathogen-based inflammation and (ii) a plantar incisional wound as a model of tissue injury-based inflammation. Our findings reveal differences in temporal patterns of immune cell recruitment and activation states, cytokine production, and pain in these two models, with CFA causing a nonresolving granulomatous inflammatory response whereas tissue incision induced resolving immune and pain responses. These findings highlight the significant differences and potential clinical relevance of the incisional wound model compared with the CFA model. By using various cell-depletion strategies, we find that, whereas lymphocyte antigen 6 complex locus G (Ly)6G(+)CD11b(+) neutrophils and T-cell receptor (TCR) β(+) T cells do not contribute to the development of thermal or mechanical pain hypersensitivity in either model, proliferating CD11b(+)Ly6G(-) myeloid cells were necessary for mechanical hypersensitivity during incisional pain, and, to a lesser extent, CFA-induced inflammation. However, inflammatory (CCR2(+)Ly6C(hi)) monocytes were not responsible for these effects. The finding that a population of proliferating CD11b(+)Ly6G(-) myeloid cells contribute to mechanical inflammatory pain provides a potential cellular target for its treatment in wound inflammation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".