Anti-inflammatory mechanisms in Wallerian degeneration in injured peripheral nerve
Bibliographic record
Abstract
Axonal injury induces a coordinated set of cellular responses termed Wallerian degeneration (WD). In the peripheral nervous system (PNS), WD is initiated by pro-inflammatory chemokines and cytokines. These pro-inflammatory signals are promptly switched off by various anti-inflammatory mechanisms. In this way, the inflammatory response within the nerve is contained. At present, although the network of pro-inflammatory cytokines and chemokines that drive WD is well-characterized, little is known about the nature of the anti-inflammatory mediators that terminate this response in the peripheral nerve. However, in other tissues following injury, multiple mechanisms have been identified that can act at the extracellular or intracellular level to ensure the inflammatory response is successfully resolved. The aim of this thesis is to assess the expression and functional role of three key anti-inflammatory molecules in WD in the mouse sciatic nerve after crush injury. Two extracellular mechanisms were examined: the anti-inflammatory cytokine IL-10, and the selective IL-1 receptor antagonist (IL-1ra). In addition, one intracellular mechanism was also studied: the suppressors of cytokine signalling family (SOCS). IL-10 null mice and IL-1ra null mice were used to assess the roles of endogenous IL-10 and IL-1ra, respectively. To assess the role of the SOCS, a SOCS1-mimetic peptide was administered to mice following nerve cut/ligation injury. The results presented in this thesis demonstrate that each of these molecules contributes uniquely to terminating inflammation in the injured mouse sciatic nerve. In addition, a lack of IL-10 or IL-1ra further influences regenerative processes and subsequent functional recovery. In conclusion, the present thesis provides evidence for the involvement of multiple anti-inflammatory mechanisms in the regulation of WD in the injured peripheral nerve. This work may help to build a better understanding of inflammatory diseases of the peripheral
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".