Bibliographic record
Abstract
Microglia has the potential to produce and release a range of factors that directly and/or indirectly promote regeneration in the injured nervous system. The overwhelming evidence indicates, however, that this potential is generally not expressed in vivo. Activated microglia may enhance neuronal degeneration following axotomy, thereby counteracting functional recovery. Microglia does not seem to contribute significantly to axonal outgrowth after peripheral nerve injury, since this process proceeds uneventful even if perineuronal microglia is eliminated. The phagocytic phenotype of microglia is highly suppressed during Wallerian degeneration in the central nervous system. Therefore, microglia is incapable of rapid and efficient removal of myelin debris and its putative growth inhibitory components. In this way, microglia may contribute to regeneration failure in the central nervous system. Structural and temporal correlations are compatible with participation by perineuronal microglia in axotomy-induced shedding of presynaptic terminals, but direct evidence for such participation is lacking. Currently, the most promising case for a promoting effect on neural repair by activated microglia appears to be as a mediator of collateral sprouting, at least in certain brain areas. However, final proof for a critical role of microglia in these instances is still lacking. Results from in vitro studies demonstrate that microglia can develop a regeneration supportive phenotype. Altering the microglial involvement following neural injury from a typically passive or even counterproductive state and into a condition where these cells are actively supporting regeneration and plasticity is, therefore, an exciting challenge and probably a realistic goal.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".