Murine Experimental Autoimmune Encephalomyelitis Is Diminished by Treatment with the Angiogenesis Inhibitors B20-4.1.1 and Angiostatin (K1-3)
Bibliographic record
Abstract
Angiogenesis is the formation of new blood vessels form pre-existing vasculature whose contribution to inflammatory conditions of the Central Nervous System is being studied in order to generate novel therapeutic targets. This study is the first to investigate the impact of two particular angiogenesis inhibitors on murine Experimental Autoimmune Encephalomyelitis (EAE), an inflammatory disease that mimics aspects of the human disease Multiple Sclerosis. The inhibitors were chosen to reduce angiogenesis by complimentary means. Extrinsic factors were targeted with B20-4.1.1 through its ability to bind to murine Vascular Endothelial Growth Factor (VEGF). Vascular processes connected to angiogenesis were targeted directly with K(1-3), the first three kringle domains of angiostatin. Mice treated with B20-4.1.1 and K(1-3) from onset of signs had reduced clinical scores 18-21 days after EAE induction. Both agents suppressed spinal cord angiogenesis without effect on local VEGF expression. B20-4.1.1 reduced spinal cord vascular permeability while K(1-3) had no effect. T cell infiltration into the spinal cord at day 21 was unaffected by either treatment. B20-4.1.1 reduced peripheral T cell proliferation while K(1-3) had no effect. Lymphoid cells from treated mice produced reduced levels of the T helper-17 (Th-17) cell cytokine interleukin (IL)-17 with no effect on the Th-1 cytokine interferon (IFN)-γ or Th-2 cytokine IL-4. However, when both drugs were added in vitro to naive T cells or to antigen stimulated T cells from mice with untreated EAE they had no effect on proliferation or levels of IL-17 or IFN-γ. We conclude that these angiogenesis inhibitors mitigate EAE by both suppressing spinal cord angiogenesis and reducing peripheral T cell activation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".