Optimization and Mechanistic Exploration of Intravenous Human Immunoglobulin G for Treatment of Traumatic Cervical Spinal Cord Injury
Bibliographic record
Abstract
Spinal cord injury (SCI) causes devastating functional impairments and socioeconomic consequences for patients and family members. The pathology is characterized by an initial physical trauma which is amplified by secondary injury mechanisms including vascular and inflammatory processes. The blood-spinal cord-barrier (BSCB) consists of pericytes, astrocytes and endothelial cells, which collectively form the neurovascular unit (NVU). After SCI, a compromised BSCB enables peripheral immune cells to enter the injury site. This inflammatory response has polarizing effects. While immune cells are beneficial by secreting growth factors that promote regeneration after SCI, they also propagate injury when sterilizing the injured area. Thus, immunomodulation is becoming the preferred clinical intervention to immunosuppression when resolving neuroinflammation. Approved by regulatory authorities as an immunomodulatory therapy, intravenous human immunoglobulin G (IVIg) may be suitable for treating SCI. While prior work has suggested a possible beneficial role for IVIg in the treatment of acute SCI, the clinical translation of this work has been hindered by critical knowledge gaps including uncertainty around mechanism of action, the optimal dosing and the post-injury therapeutic window. Therefore, this thesis aims to use a clinically relevant rat model of SCI to provide insights into the optimal dose, therapeutic time window and mechanism of action. My work determined that the optimal therapeutic dose of IVIg is 2g/kg and that beneficial effects were maintained when administered up to 4 hours post-SCI. My work showed that IVIg binds to BSCB cells, strengthening the BSCB and reducing neutrophil infiltration at 24 hours post-SCI. This is associated with long-term motor and sensory recovery and spinal cord tissue preservation. Immunomodulatory effects mediated by IVIg (2g/kg) might be explained in two-fold, where human immunoglobulin G (hIgG) antagonizes neutrophil infiltration into the spinal cord by co-localizing with endothelial cell ligands that mediate neutrophil extravasation. Moreover, IVIg traffics neutrophils to the spleen by increasing expression of neutrophil chemoattractants in spleen and sera. In conclusion, this work demonstrates that IVIg might be a suitable treatment for SCI with potential for clinical translation. IVIg (2g/kg) enhances short-term and long-term benefits after SCI by modulating local and systemic neuroinflammatory cascades.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".