Can Modulating Inflammatory Response be a Good Strategy to Treat Neuropathic Pain?
Bibliographic record
Abstract
Neuronal injury not only results in severe alteration in the function of primary sensory neurons and their central projection pathway, but is also associated with a robust immune response at almost every level of the somatosensory system. Evidence from animal studies suggests undoubtedly that bi-directional signalling between the immune system and the nervous system contribute to the development and maintenance of chronic neuropathic pain. Non-neuronal cells, including peripheral immune cells, CNS/PNS glial cells and endothelial cells play important roles in the neuroimmune interaction and subsequent persistent hypersensitivity. Various cytokines and chemokines have been identified as key signalling molecules in the crosstalk. However, majority evidence showing inflammation in neuropathic pain was generated from animal models at acute phase. Whether and to what extent inflammation or non-neuronal cells are involved at chronic stage of neuropathic pain needs to be further explored, and evidence of inflammation in chronic pain from human studies is still largely awaited. Therapeutic agents targeting inflammation provide an exciting prospect. Yet, considering the heterogeneous conditions presented in neuropathic pain, no matter the etiologies, or the pathophysiology during different stages of the disease; and the complexity of the immune response to the damage on the nervous system, it appears that finely tuned strategies of modulating inflammation are essential to warrant an effective treatment for neuropathic pain. We want to reduce pain; we also want to promote tissue repair and functional recovery.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".