The role of PI 3-kinase in EGF-stimulated jejunal glucose transport
Bibliographic record
Abstract
Epidermal growth factor (EGF) rapidly increases jejunal glucose transport. Signal transduction mechanisms mediating EGF-induced alterations in jejunal glucose transport remain to be determined. New Zealand White rabbit (1 kg) jejunal tissue was stripped and mounted in short-circuited Ussing chambers. The transport of tritiated 3-O-methylglucose was measured in the presence of the PKC agonist 1,2-dioctanoyl-sn-glycerol (1,2-DOG) or the inactive analog 1,3-dioctanoyl-sn-glycerol (1,3-DOG). Additional experiments examined the effect of the PKC inhibitor chelerythrine, the PLC inhibitor U73122, the MAPK inhibitor PD 98059, the G-protein inhibitor GDP-betaS, the PI 3-kinase inhibitor LY294002, or the microtubule inhibitor colchicine on EGF-induced jejunal glucose transport. Net jejunal 3-O-methylglucose absorption was significantly increased following specific activation of PKC. A PKC antagonist inhibited the EGF-induced increase in net 3-O-methylglucose transport, while PI 3-kinase inhibition completely blocked the EGF-induced transport increase. Inhibition of PLC, MAPK, G-proteins, and microtubules had no effect on EGF-stimulated increases in jejunal transport. We conclude that the effect of EGF on jejunal glucose transport is mediated at least in part by PKC and PI 3-kinase.
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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 | 0.001 |
| 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".