Hypoxia up‐regulates the angiogenic cytokine secretoneurin <i>via</i> an HIF‐1α‐ and basic FGF‐dependent pathway in muscle cells
Bibliographic record
Abstract
Expression of angiogenic cytokines like vascular endothelial growth factor is enhanced by hypoxia. We tested the hypothesis that decreased oxygen levels up‐regulate the angiogenic factor sec‐retoneurin. In vivo, muscle cells of mouse ischemic hind limbs showed increased secretoneurin expression, and inhibition of secretoneurin by a neutralizing antibody impaired the angiogenic response in this ischemia model. In a mouse soft tissue model of hypoxia, secretoneurin was increased in subcutaneous muscle fibers. In vitro, secretoneurin mRNA and protein were up‐regulated in L6 myoblast cells after exposure to low oxygen levels. The hypoxia‐depen‐dent regulation of secretoneurin was tissue specific and was not observed in endothelial cells, vascular smooth muscle cells, or AtT20 pituitary tumor cells. The hypoxia‐dependent induction of secretoneurin in L6 myoblasts is regulated by hypoxia‐inducible factor‐la, since inhibition of this factor using si‐RNA inhibited up‐regulation of secretoneurin. Induction of secretoneurin by hypoxia was dependent on basic fibroblast growth factor in vivo and in vitro, and inhibition of this regulation by heparinase suggests an involvement of low‐affinity basic fibroblast growth factor binding sites. In summary, our data show that the angiogenic cytokine secretoneurin is up‐regulated by hypoxia in muscle cells by hypoxia‐inducible factor‐1α‐ and basic fibroblast growth factor‐dependent mechanisms.—Egger, M., Schgoer, W., Beer, A. G. E., Jeschke, J., Leierer, J., Theurl, M., Frauscher, S., Tepper, O. M., Niederwanger, A., Ritsch, A., Kearney, M., Wanschitz, J., Gurtner, G. C., Fischer‐Colbrie, R., Weiss, G., Piza‐Katzer, H., Losordo, D. W., Patsch, J. R., Schratzberger, P., Kirchmair, R. Hypoxia up‐regulates the angiogenic cytokine secretoneurin via an HIF‐1α‐ and basic FGF‐dependent pathway in muscle cells. FASEB J. 21, 2906–2917 (2007)
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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".