Amyloid-β25–35, an Amyloid-β1–42 Surrogate, and Proinflammatory Cytokines Stimulate VEGF-A Secretion by Cultured, Early Passage, Normoxic Adult Human Cerebral Astrocytes
Bibliographic record
Abstract
Cerebrovascular angiopathy affects late-onset Alzheimer's disease (LOAD) brains by possibly increasing vascular endothelial growth factor (VEGF). A expression, thereby stimulating endothelial cell proliferation and migration. Indeed, VEGF-A gene upregulation, with increased VEGF-A protein content of reactive astrocytes and microglia, occurs in LOAD brains, and neovascularization was observed one week after injecting amyloid-β (Aβ)(1-42) into rat hippocampus. We have now found, with cultured 'normoxic' normal adult human astrocytes (NAHAs), that fibrillar Aβ(25-35) (an active Aβ(1-42) fragment) or a cytokine mixture (the (CM)-trio (interleukin [IL]-1β+interferon [IFN]-γ+tumor necrosis factor [TNF]-α), or pair (IFN-γ+TNF-α) like those produced in LOAD brains) stimulates the nuclear translocation of stabilized hypoxia-inducible factor (HIF)-1α protein and its binding to VEGF-A hypoxia-response elements; the mRNA synthesis for three VEGF-A splice variants (121, 165, 189); and the secretion of VEGF-A165. The CM-trio was the most powerful stimulus, IFN-γ+TNF-α was less potent, and other cytokine pairs or single cytokines or Aβ(35-25) were ineffective. While Aβ(25-35) did not change HIF-1β protein levels, the CM-trio increased both HIF-1α and HIF-1β protein levels, thereby giving an earlier and stronger stimulus to VEGF-A secretion by NAHAs. Thus, increased VEGF-A secretion from astrocytes stimulated by Aβ(1-42) and by microglia-released cytokines might restore angiogenesis and Aβ(1-42) vascular clearance.
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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 | 0.000 |
| 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".