Fibrin depletion decreases inflammation and delays the onset of demyelination in a tumor necrosis factor transgenic mouse model for multiple sclerosis
Bibliographic record
Abstract
In multiple sclerosis, in which brain tissue becomes permeable to blood proteins, extravascular fibrin deposition correlates with sites of inflammatory demyelination and axonal damage. To examine the role of fibrin in neuroinflammatory demyelination, we depleted fibrin in two tumor necrosis factor transgenic mouse models of multiple sclerosis, transgenic lines TgK21 and Tg6074. In a genetic analysis, we crossed TgK21 mice into a fibrin-deficient background. TgK21fib(-/-) mice had decreased inflammation and expression of major histocompatibility complex class I antigens, reduced demyelination, and a lengthened lifespan compared with TgK21 mice. In a pharmacologic analysis, fibrin depletion, by using the snake venom ancrod, in Tg6074 mice also delayed the onset of inflammatory demyelination. Overall, these results indicate that fibrin regulates the inflammatory response in neuroinflammatory diseases. Design of therapeutic strategies based on fibrin depletion could potentially benefit the clinical course of demyelinating diseases such as multiple sclerosis.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".