Immunoglobulin G: A Potential Treatment to Attenuate Neuroinflammation Following Spinal Cord Injury
Bibliographic record
Abstract
INTRODUCTION: Spinal cord injury (SCI) is caused by two related but mechanistically distinct events: the primary injury to the spinal cord is caused by a mechanic trauma; the secondary injury is a cascade of cellular and molecular events that exacerbates the initial damage. MATERIALS AND METHODS: Neuroinflammation, an important event in the secondary injury cascade, is critical in the clearance of cellular debris after SCI. However, leukocytes and microglia, recruited to the injury site during neuroinflammation, can exacerbate the initial damage following SCI by secreting reactive oxygen species, matrix-metalloproteinase, and proinflammatory cytokines. Therefore, attenuating the activity of leukocytes and microglia is an attractive therapeutic strategy to reduce the neurological deficit associated with SCI. DISCUSSION: In this regard, immunoglobulin G (IgG) is a potential treatment candidate. IgG has been used clinically to treat autoimmune disease and has been demonstrated to attenuate the activities of leukocytes and microglia. In this review, we discuss the potential use of IgG for SCI based on the current understanding of the immune-modulating mechanism of IgG and the role of neuroinflammation in 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".