The role of neutrophils in pain: systematic review and meta-analysis of animal studies
Bibliographic record
Abstract
ABSTRACT: The peripheral inflammatory response is an attractive therapeutic target for pain treatment. Neutrophils are the first circulating inflammatory cells recruited to sites of injury, but their contribution to pain outcomes is unclear. We performed a systematic review and meta-analysis of original preclinical studies, which evaluated the effect of preemptive neutrophil depletion on pain outcomes (PROSPERO registration number: CRD42022364004). Literature search (PubMed, January 19, 2023) identified 49 articles, which were meta-analyzed using a random-effects model. The risk of bias was evaluated using SYRCLE's tool. The pooled effect considering all studies showed that neutrophil depletion induced a consistent pain reduction. Inflammatory, joint, neuropathic, and visceral pain showed significant pain alleviation by neutrophil depletion with medium-large effect sizes. However, muscle and postoperative pain were not significantly alleviated by neutrophil depletion. Further analysis showed a differential contribution of neutrophils to pain outcomes. Neutrophils had a higher impact on mechanical hyperalgesia, followed by nociceptive behaviors and mechanical allodynia, with a smaller contribution to thermal hyperalgesia. Interspecies (mice or rats) differences were not appreciated. Analyses regarding intervention unveiled a lower pain reduction for some commonly used methods for neutrophil depletion, such as injection of antineutrophil serum or an anti-Gr-1 antibody, than for other agents such as administration of an anti-Ly6G antibody, fucoidan, vinblastine, CXCR1/2 inhibitors, and etanercept. In conclusion, the contribution of neutrophils to pain depends on pain etiology (experimental model), pain outcome, and the neutrophil depletion strategy. Further research is needed to improve our understanding on the mechanisms of these differences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".