Involvement of IGF‐1R and PKCδ in the Protein Kinase B (PKB) phosphorylation induced by Bis(maltolato)‐oxovanadium (IV) (BMOV) in human hepatoma cells (HepG2).
Bibliographic record
Abstract
BMOV, an organo‐vanadium compound, is a potent insulinomimetic agent and improves glucose homeostasis in various models of diabetes. We have shown earlier that BMOV stimulates the phosphorylation of PKB in HepG2 which may contribute as one of the mechanism for the insulinmimetic effect of this compound. However, the upstream mechanism of BMOV‐induced PKB phosphorylation remains elusive. Therefore, in this study we have examined the upstream events leading to BMOV‐induced PKB phosphorylation in HepG2. Since BMOV is an inhibitor of Protein Tyrosine Phosphatases and can impact on various Protein tyrosine kinases (PTK), we have investigated the potential role of different receptor or non receptor PTK in mediating BMOV‐induced PKB phosphorylation. Among several pharmacological inhibitors tested, only AG1024, a selective inhibitor of IGF‐1R‐PTK almost completely blocked BMOV‐stimulated phosphorylation of PKB. In contrast, AG 1295, AG 1478 and PP‐2, specific inhibitors of PDGFR, EGFR and c‐Src respectively were unable to block the BMOV response. A role of PKC in BMOV‐induced response was also tested. Chronic treatment with PMA, or pharmacological inhibition with Chelerythrine a non‐selective PKC inhibitor or Rottlerin a PKCδ inhibitor attenuated BMOV‐induced PKB phosphorylation. In contrast, Gö6976 a PKC©α/β selective inhibitor failed to alter BMOV effect. Taken together, these data suggest that IGF‐1R and PKCδ are required to stimulate PKB phosphorylation in response to BMOV in HepG2. (Supported by grants from Canadian Institutes of Health Research).
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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) | 0.002 | 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".