Quantitative estimation of the effects of bile salt surfactant systems on insulin stability and permeability in the rat intestine using a mass balance model
Bibliographic record
Abstract
The oral delivery of peptides and proteins is compromised by chemical and proteolytic instability as well as by permeability limitations. The aim of this study was to delineate the relative contributions of simple bile salt and bile salt:fatty acid mixed micellar systems to protein stability vs permeability enhancement in the rat intestine. Insulin disappearance from the rat intestine was evaluated when administered in simple micellar systems of sodium cholate (NaC), sodium taurocholate (NaTC) and sodium glycocholate (NaGC), and in mixed micellar systems of these bile salts and linoleic acid (LA). In-vitro stability studies were used to evaluate the extent of insulin degradation in the different micellar systems. After correction for insulin degradation in all systems a mass balance model was used to estimate the fractions of insulin absorbed for all systems. Mass balance estimates for the extent of insulin absorption in control perfusion systems were consistent with previously reported predictions of the model for ileal insulin absorption. Mass balance estimates for NaGC suggested no significant effects on the fraction of insulin absorbed relative to control. However, insulin absorption was estimated to occur to a significantly greater extent for NaTC simple micellar systems and was coincident with increased permeability of the hydrophilic marker molecule PEG 4000. The mass balance model estimated higher fractions of insulin absorbed for all mixed micellar systems in line with enhanced plasma insulin levels and higher PEG 4000 permeabilities for these systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".