Bibliographic record
Abstract
AIM: We examined the effect of proinflammatory and anti-inflammatory interleukins on jejunal nutrient transport and expression of the sodium-glucose linked cotransporter (SGLT-1). METHODS: 3-O-methyl glucose and L-proline transport rates were examined in New Zealand White rabbit stripped, short circuited jejunal tissue. The effects of the proinflammatory cytokines interleukin (IL)-1alpha, IL-6, and IL-8, IL-1alpha plus the specific IL-1 antagonist, IL-1ra, and the anti-inflammatory cytokine IL-10 were investigated. In separate experiments, passive tissue permeability was assessed and brush border SGLT-1 expression was measured by western blot in tissues exposed to proinflammatory interleukins. RESULTS: The proinflammatory interleukins IL-6, IL-1alpha, and IL-8 significantly increased glucose absorption compared with control levels. This increase in glucose absorption was due to an increase in mucosal to serosal flux. IL-1alpha and IL-8 also significantly increased L-proline absorption due to an increase in absorptive flux. The anti-inflammatory IL-10 had no effect on glucose transport. The receptor antagonist IL-1ra blocked the ability of IL-1alpha to stimulate glucose transport. IL-8 had no effect on passive tissue permeability. SGLT-1 content did not differ in brush border membrane vesicles (BBMV) from control or interleukin treated tissue. CONCLUSIONS: These findings suggest that intestinal inflammation and release of inflammatory mediators such as interleukins increase nutrient absorption in the gut. The increase in glucose transport does not appear to be due to changes in BBMV SGLT-1 content.
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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".