Endogenous Interleukin-10 Deficiency Exacerbates Vascular Pathology in Traumatic Cervical Spinal Cord Injury
Bibliographic record
Abstract
Although the majority of traumatic spinal cord injuries (SCIs) take place at the cervical level, pre-clinical studies have been disproportionally focused on thoracic insults. With differences in anatomy, physiology, and immune response between spinal cord levels, there is evidence that injury pathophysiology may vary, requiring tailored treatment paradigms. Further, as only a few therapies have been successfully translated to the clinic, cervical models are increasingly recognized as essential for the characterization of trauma and therapy. Using a novel and clinically relevant cervical contusion-compression mouse model of bilateral incomplete injury, this study aimed to assess the role of interleukin10 (IL-10), a potent cytokine with broad anti-inflammatory effects, in SCI vascular pathology. While the effects of IL-10 loss have been previously evaluated, the vascular changes are poorly characterized. Here, using in vivo high-resolution ultrasound imaging, we demonstrate that IL-10 deficiency is associated with increased acute vascular damage. Importantly, the loss of endogenous IL-10 led to significant differences in the acute systemic response to SCI, specifically the circulating levels of IL-12 (p70), LIX (CXCL5), IL-1β, tumor necrosis factor (TNF)-α, and IL-6 relative to genotype sham controls. These effects also fostered modest impairments in long-term functional recovery, assessed by the Basso Mouse Scale, as well as histological outcomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".