Forskolin attenuates the action of insulin on the Akt–mTOR pathway in human skeletal muscle
Bibliographic record
Abstract
Forskolin (FSK) is capable of both stimulating and inhibiting the intracellular signaling pathways of protein synthesis tissues other than skeletal muscle. The purpose of this investigation was to determine if FSK administration affects various elements of the protein kinase B (Akt)-mammalian target of rapamycin (mTOR) pathway in human skeletal muscle. Ten (n = 10) healthy, young (21.6 +/- 1.3 years), nonobese (body mass index = 25.5 +/- 3.5 kg.m-2), recreationally active males were selected for participation. Following an 8 h fast, 2 muscle biopsies of the vastus lateralis were performed. The samples were sectioned and exposed to 4 in vitro treatment conditions: basal, FSK, insulin (INS), and FSK+INS. The samples were then analyzed for total and phosphorylated levels of Akt, mTOR, S6 kinase (S6K1), and 4E binding protein (4EBP1). Akt phosphorylation was significantly greater in the INS-treated samples compared with the basal and FSK conditions (p = 0.007). Furthermore, the ratio of phosphorylated Akt to total Akt (P/T) was higher in the INS samples compared with the basal and FSK samples (p = 0.001). There were no differences in mTOR phosphorylation among the 4 groups; however, total mTOR was significantly greater in the FSK+INS group (p = 0.006). There were also no differences in phosphorylated or total levels of S6K1 among the 4 groups. However, 4EBP1 phosphorylation was significantly greater in the INS-treated samples compared with the basal (p = 0.003) and FSK (p = 0.004) treatments. There were no differences in the ratio of phosphorylated 4EBP1 to total 4EBP1 (P/T) among the 4 groups. These results indicate that FSK does not activate the Akt-mTOR pathway in human skeletal muscle; however, these results suggest that FSK may inhibit the actions of INS on this pathway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".