Paracellular and transcellular pathways facilitate insulin permeability in rat gut
Bibliographic record
Abstract
The aim of this study was to conduct a systematic investigation of the absorption of insulin in the rat intestine in the presence of permeation enhancers and protease inhibitors. An in-situ perfused rat gut model was used for the co-perfusion of insulin and PEG 4000 in the presence or absence of bile salts, bile salt:fatty acid surfactant systems and protease inhibitors. Perfusion experiments were conducted for 180 min with perfusate and blood collection at regular intervals. Permeability coefficients for insulin were calculated from plasma insulin and PEG 4000 permeability coefficients were calculated from lumenal disappearance data. In the absence of enzyme inhibitors, insulin permeability was consistently lower than PEG 4000, but increased in proportion to PEG 4000 permeability. Large increases in insulin permeability were obtained for mixed micellar systems and protease inhibitors. In the presence of protease inhibitors and simple micelle systems, PEG 4000 permeability was three-fold greater than insulin permeability. In the presence of absorption enhancers, PEG 4000 permeability increased up to a maximum value of 3.63 x 10(-6)cm s(-1), a value five-fold less than that of the estimated aqueous boundary layer permeability for PEG 4000. This suggests that PEG 4000 permeability is primarily membrane controlled. Insulin permeability is enhanced to a maximum value of 9.17 x 10(-6)cm s(-1), suggesting that paracellular transport routes do not account exclusively for insulin permeation across the intestinal epithelium. The results add support to suggestions that routes other than the paracellular route may contribute to insulin absorption in rat gut.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".