The Role of Microglia in Modulating Neuroinflammation after Spinal Cord Injury
Bibliographic record
Abstract
The pathobiology of traumatic and nontraumatic spinal cord injury (SCI), including degenerative myelopathy, is influenced by neuroinflammation. The neuroinflammatory response is initiated by a multitude of injury signals emanating from necrotic and apoptotic cells at the lesion site, recruiting local and infiltrating immune cells that modulate inflammatory cascades to aid in the protection of the lesion site and encourage regenerative processes. While peripheral immune cells are involved, microglia, the resident immune cells of the central nervous system (CNS), are known to play a central role in modulating this response. Microglia are armed with numerous cell surface receptors that interact with neurons, astrocytes, infiltrating monocytes, and endothelial cells to facilitate a dynamic, multi-faceted injury response. While their origin and essential nature are understood, their mechanisms of action and spatial and temporal profiles warrant extensive additional research. In this review, we describe the role of microglia and the cellular network in SCI, discuss tools for their investigation, outline their spatiotemporal profile, and propose translationally-relevant therapeutic targets to modulate neuroinflammation in the setting of SCI.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".