Evaluating the role of IL-11, a novel cytokine in the IL-6 family, in a mouse model of spinal cord injury
Bibliographic record
Abstract
BACKGROUND: Spinal cord injury (SCI) is a devastating condition with substantial functional and social morbidity. Previous research has established that the neuroinflammatory response plays a significant role in cord damage post-SCI. However, global immunosuppressive therapies have demonstrated mixed results. As a result, more specific therapies modulating inflammation after injury are needed. In this regard, research into cytokine signaling has demonstrated that cytokines of the gp130 family including IL-6 and leukemia inhibitory factor (LIF) play key roles in mediating damage to the spinal cord. Since members of the gp130 family all share a common signal transduction pathway via the JAK/STAT system, we performed the first study of a relatively new member of the gp130 family, IL-11, in SCI. METHODS: A validated clip-compression mouse model of SCI was used to assess for temporal changes in expression of IL-11 and its receptor, IL-11Rα, post-SCI. To elucidate the role of IL-II in the pathophysiology of SCI, we compared differences in locomotor recovery (Basso Mouse Score; CatWalk), electrophysiological spinal cord signaling, histopathology, and the acute inflammatory neutrophil response in IL-11Rα knockouts with littermate wild-type C57BL/6 mice. RESULTS: We found an increase in gene expression of IL-11 in the spinal cord to a peak at twenty-four hours post-SCI with increases in IL-11Rα gene expression, peaking at seven days post-SCI. In spite of clear changes in the temporal expression of both IL-11 and its receptor, we found that there were no significant differences in motor function, electrophysiological signaling, histopathology, or neutrophil infiltration into the spinal cord between wild-type and knockout mice. CONCLUSIONS: This is the first study to address IL-11 in SCI. This study provides evidence that IL-11 signaling may not play as significant a role in SCI as other gp130 cytokines, which will ideally guide future therapy design and the signaling pathways those therapies target.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".