Characterization of the Antibody Response after Cervical Spinal Cord Injury
Bibliographic record
Abstract
The immune system plays a critical and complex role in the pathobiology of spinal cord injury (SCI), exerting both beneficial and detrimental effects. Increasing evidence suggests that there are injury level-dependent differences in the immune response to SCI. Patients with traumatic SCI have elevated levels of circulating autoantibodies against components of the central nervous system, but the role of these antibodies in SCI outcomes remains unknown. In rodent models of mid-thoracic SCI, antibody-mediated autoimmunity appears to be detrimental to recovery. However, whether autoantibodies against the spinal cord are generated following cervical SCI (cSCI), the most common level of injury in humans, remains undetermined. To address this knowledge gap, we investigated the antibody responses following cSCI in a rat model of injury. We found increased immunoglobulin G (IgG) and IgM antibodies in the spinal cord in the subacute phase of injury (2 weeks), but not in more chronic phases (10 and 20 weeks). At 2 weeks post-cSCI, antibodies were detected at the injury epicenter and co-localized with the astroglial scar and neurons of the ventral horn. These increased levels of antibodies corresponded with enhanced activation of immune responses in the spleen. Higher counts of antibody-secreting cells were observed in the spleen of injured rats. Further, increased levels of secreted IgG antibodies and enhanced proliferation of T-cells in splenocyte cultures from injured rats were found. These findings suggest the potential development of autoantibody responses following cSCI in the rat. The impact of the post-traumatic antibody responses on functional outcomes of cSCI is a critical topic that requires further investigation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".