Actions of the Anti-Angiogenic Compound Angiostatin in an Animal Model of Alzheimer’s Disease
Bibliographic record
Abstract
We have examined the anti-angiogenic compound, angiostatin as a modulator of inflammatory reactivity and vascular responses and for neuroprotection in an animal model of Alzheimer's disease (AD). Intra-hippocampal amyloidbeta (Aβ₁₋₄₂) injection, relative to controls phosphate buffer saline (PBS) or reverse peptide Aβ₄₂₋₁, increased gliosis in the molecular layer (ML) of rat hippocampus. Vascular remodeling was indicated from increased microvessel immunoreactivity (ir) in ML suggesting the possibility of an angiogenic response to peptide injection. Administration of Aβ₁₋₄₂ also induced a loss of neurons in the granule cell region of hippocampus relative to controls. Treatment of peptide-injected rats with angiostatin was associated with a spectrum of modulatory effects including reduced microgliosis (by 34%), diminished microvessel ir (by 36%) and increased neuronal viability (by 31%) compared with peptide injection alone. Angiostatin treatment was ineffective in reducing astrogliosis induced by Aβ₁₋₄₂ and applied alone the compound had no significant effect to alter gliosis, microvessel ir or neuronal viability compared with PBS control. In vitro, angiostatin significantly attenuated secretion of the pro-angiogenic agent, vascular endothelial growth factor (VEGF) in lipopolysaccharide (LPS)-stimulated THP-1 cells. Our findings provide novel evidence for a broad spectrum of angiostatin effects in an animal model of AD including actions to reduce inflammatory reactivity, stabilize vascular remodeling and confer neuroprotection. The overall effects of angiostatin are consistent with actions of the compound to inhibit microglial secretion of VEGF.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
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".