An elevated matrix metalloproteinase (MMP) in an animal model of multiple sclerosis is protective by affecting Th1/Th2 polarization
Bibliographic record
Abstract
Inflammation in multiple sclerosis and its animal model, experimental autoimmune encephalomyelitis (EAE), is manifested by changes in matrix metalloproteinase (MMP) expression and in the ratio of T helper (Th) 1 and 2 effector cytokines. Here, we provide a comprehensive documentation of MMPs in EAE and report that of all the MMPs that could be measured at peak disease in spinal cord tissue, MMP-12 was the most highly up-regulated. In contrast to previously published findings of MMPs in EAE, this increase in MMP-12 expression was associated with protection, as MMP-12 null mice had significantly worse maximum severity and EAE disease burden compared with wild-type (WT) controls. When spleen and lymph node cells were removed from EAE-afflicted WT and MMP-12 null mice at the same disease score before divergence of disease and restimulated in vitro, the MMP-12 null cells had significantly higher Th1 to Th2 cytokine ratio. Measurements of the transcriptional regulators of T cell polarization revealed that MMP-12 null cells had increased T-bet and reduced GATA-3 expression, a condition that favors a Th1 bias. These results emphasize that specific MMPs can have beneficial roles in inflammation, and they implicate MMPs in T effector polarization for the first time.
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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 | 0.001 | 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".