Investigating the role of the immune cell response for successful spinal cord regeneration in the zebrafish model
Bibliographic record
Abstract
Spinal cord injury (SCI) is a life changing condition affecting individuals within Canada and worldwide with no effective treatment to date. A limitation in humans, like other mammals, is that they cannot repair the damaged central nervous system. By contrast, the zebrafish model has a remarkable ability to regenerate the brain and spinal cord after injury, due to populations of ependymoglia. Previous work has shown that for ependymoglia-driven neural regeneration to occur in zebrafish, immune cells are a key requirement. This opposes the immune response in mammals that demonstrates a prolonged pro-inflammatory phase that prevents recovery after SCI. How the activation of the zebrafish immune response results in successful spinal cord repair remains poorly characterized. In this study, we hypothesized that the inflammatory response following SCI in zebrafish is regulated by a longer anti-inflammatory response that is important for successful regeneration. By studying the spatiotemporal dynamics of immune cells post-SCI, we observed that overtime immune cells infiltrate into the injury site, correlating with a peak in proliferation of ependymoglia. Interestingly, analysis of pro- and anti-inflammatory cytokines from our initial qRT-PCR experiments suggest that anti-inflammatory cytokines remain stable across multiple time-points post-SCI in comparison to pro-inflammatory cytokines. These findings propose that in order for successful spinal cord regeneration to occur, a shorter pro-inflammatory response that is tightly controlled by anti-inflammatory cytokines is necessary.
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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 | 0.001 | 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) | 0.002 | 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".