Treatment of Spinal Cord Injury with Intravenous Immunoglobulin G: Preliminary Evidence and Future Perspectives
Bibliographic record
Abstract
Neuroinflammation plays an important role in the secondary pathophysiological mechanisms of spinal cord injury (SCI) and can exacerbate the primary trauma and thus worsen recovery. Although some aspects of the immune response are beneficial, it is thought that leukocyte recruitment and activation in the acute phase of injury results in the production of cytotoxic substances that are harmful to the nervous tissue. Therefore, suppression of excessive inflammation in the spinal cord could serve as a therapeutic strategy to attenuate tissue damage. The immunosuppressant methylprednisolone has been used in the setting of SCI, but there are complications which have attenuated the initial enthusiasm. Hence, there is interest in other immunomodulatory approaches, such as intravenous Immunoglobulin G (IVIg). Importantly, IVIg is used clinically for the treatment of several auto-immune neuropathies, such as Guillain-Barre syndrome, chronic inflammatory demyelinating polyneuropathy (CIPD) and Kawasaki disease, with a good safety profile. Thus, it is a promising treatment candidate for SCI. Indeed, IVIg has been shown by our team to attenuate the immune response and result in improved neurobehavioral recovery following cervical SCI in rats through a mechanism that involves the attenuation of neutrophil recruitment and reduction in the levels of cytokines and cytotoxic enzymes Nguyen et al. (J Neuroinflammation 9:224, 2012). Here we review published data in the context of relevant mechanisms of action that have been proposed for IVIg in other conditions. We hope that this discussion will trigger future research to provide supporting evidence for the efficiency and detailed mechanisms of action of this promising drug in the treatment of SCI, and to facilitate its clinical translation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".