Elevated Serum Titers of Proinflammatory Cytokines and CNS Autoantibodies in Patients with Chronic Spinal Cord Injury
Bibliographic record
Abstract
This study characterized the proinflammatory cytokines, interleukin-2 (IL-2) and tumor necrosis factor alpha (TNFalpha), the antiinflammatory cytokines, IL-4 and IL-10, autoantibodies specific for GM1 ganglioside (anti-GM1), IgG and IgM, and myelin-associated glycoprotein (anti-MAG), in the sera of infection-free, chronic (>12 months), traumatically injured SCI patients (n = 24). Healthy able-bodied subjects (n = 26) served as controls. The proinflammatory cytokines and anti-GM1 antibodies were of particular interest as they have been implicated in an autoimmune "channelopathy" component to central and peripheral conduction deficits in various chronic neuroinflammatory diseases. Antibody and cytokine titers were established using enzyme-linked immunosorbent assays (ELISA). The mean anti-GM(1) (IgM) titer value for the SCI group was significantly higher (p < 0.05) than controls. The SCI group also demonstrated significantly higher titers (p < 0.05) of IL-2 and TNF alpha than controls. No differences were found between the SCI group and control group mean levels of IL-4 or IL-10. Overall, the serum of 57% of SCI patients contained increased levels of autoantibodies or proinflammatory cytokines relative to control values. These results provide preliminary support for the hypothesis that chronic immunological activation in the periphery occurs in a subpopulation of chronic SCI patients. It remains to be established whether elevated serum titers of proinflammatory cytokines and autoantibodies against GM1 are beneficial to the patients or whether they are surrogate markers of a channelopathy that compounds the neurological impairment associated with traumatic axonopathy or myelinopathy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".