Resident immune responses to spinal cord injury: role of astrocytes and microglia
Bibliographic record
Abstract
Spinal cord injury can be traumatic or non-traumatic in origin, with the latter rising in incidence and prevalence with the aging demographics of our society. Moreover, as the global population ages, individuals with co-existent degenerative spinal pathology comprise a growing number of traumatic spinal cord injury cases, especially involving the cervical spinal cord. This makes recovery and treatment approaches particularly challenging as age and comorbidities may limit regenerative capacity. For these reasons, it is critical to better understand the complex milieu of spinal cord injury lesion pathobiology and the ensuing inflammatory response. This review discusses microglia-specific purinergic and cytokine signaling pathways, as well as microglial modulation of synaptic stability and plasticity after injury. Further, we evaluate the role of astrocytes in neurotransmission and calcium signaling, as well as their border-forming response to neural lesions. Both the inflammatory and reparative roles of these cells have eluded our complete understanding and remain key therapeutic targets due to their extensive structural and functional roles in the nervous system. Recent advances have shed light on the roles of glia in neurotransmission and reparative injury responses that will change how interventions are directed. Understanding key processes and existing knowledge gaps will allow future research to effectively target these cells and harness their regenerative potential.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".