Effects of betaine on ethanol-stimulated secretion of IGF-I and IGFBP-1 in rat primary hepatocytes: Involvement of p42/44 MAPK activation
Bibliographic record
Abstract
AIM: To evaluate the effects of betaine on the ethanol-induced secretion of IGF-I and IGFBP-1 using radioimmunoassay and Western blotting, respectively, in primary cultured rat hepatocytes. METHODS: Hepatocytes isolated from male Sprague-Dawley rats were incubated with various concentrations of ethanol and PD98059 procedures. The hepatocytes were also treated with different doses of betaine (10(-5), 10(-4), and 10(-3) mol/L). We measured IGF-I and IGFBP-1 using radioimmunoassay and Western blotting, respectively. RESULTS: The ethanol-induced inhibition of IGF-I secretion was attenuated by betaine in a concentration-dependent manner in primary cultured rat hepatocytes. At 10(-3) mol/L, betaine significantly increased IGF-I secretion but decreased IGFBP-1 secretion. In addition, p42/44 mitogen-activated protein kinase (MAPK) activity was accelerated significantly from 10 min to 5 h after treatment with 10(-3) mol/L betaine. Furthermore, the changes in IGF-1 and IGFBP-1 secretion resulting from the increased betaine-induced p42/44 MAPK activity in primary cultured rat hepatocytes was blocked by treatment with the MAPK inhibitor PD98059. Betaine treatment blocked the ethanol-induced inhibition of IGF-I secretion and p42/44 MAPK activity, and the ethanol-induced increase in IGFBP-1 secretion. CONCLUSION: Betaine modulates the secretion of IGF-I and IGFBP-1 via the activation of p42/44 MAPK in primary cultured rat hepatocytes. Betaine also alters the MAPK activations induced by ethanol.
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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".