Complement activation in the peripheral nervous system following the spinal nerve ligation model of neuropathic pain ☆
Bibliographic record
Abstract
Neuroinflammatory and neuroimmune mechanisms, as exemplified by infiltrating immune cells and activation of resident endothelial/glial cells, respectively, are known to be involved in the establishment and maintenance of chronic pain. An immune system pathway that may be involved in the activation of both immune and glial cells is complement. The complement pathway is made up of a large number of distinct plasma proteins which react with one another to opsonize pathogens and induce a series of inflammatory responses to help fight infection. Cleaved products and complexes produced by complement activation are responsible for a range of effects including mediation of immune infiltration, activation of phagocytes, opsonization/lysis of pathogens and injured cells, and production of vasoactive amines such as histamine and serotonin. Gene-expression microarray-analysis performed on the rat spinal nerve ligation (SNL) model of neuropathic pain revealed that multiple complement components including the C1 inhibitor, C1q alpha, beta, and gamma, C1r, C1s, C2, C3, C4, C7, and factors B, D, H, and P, were up-regulated while DAF was down-regulated. Regulation of C3 and DAF was confirmed by real-time RT-PCR and in situ hybridization. To test the hypothesis that complement plays a role in neuropathic pain, SNL rats were treated with cobra venom factor (CVF) to deplete plasma of complement component C3. Pain behavior was significantly attenuated in SNL rats treated with CVF as was complement activity at the ipsilateral dorsal root ganglia. Our results suggest the complement pathway might be a novel target for the development of neuropathic pain therapeutics.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".