Adaptation of intestinal secretomotor function and nutrient absorption in response to diet-induced obesity
Bibliographic record
Abstract
BACKGROUND: The gut plays a significant role in the development of obesity, notably through peptide signaling to the brain. However, few studies have investigated intestinal function per se in a rodent model of diet-induced obesity (DIO). Our aim was to investigate intestinal secretomotor function and glucose transport in DIO and diet-resistant (DR) rat jejunum. METHODS: Male outbred Sprague-Dawley rats were maintained on a medium high fat diet for 9-10 weeks and split into DIO and DR groups based on weight gain. Mucosal-submucosal preparations of the proximal jejunum were mounted in Ussing chambers and voltage-clamped at 0 mV. Glucose (10 mmol L(-1)), 2-deoxy-D-glucose (10 mmol L(-1)), and leptin (10 nmol L(-1)) were added to the luminal side of the tissue and veratridine (30 micromol L(-1)), bethanechol (100 micromol L(-1)), and forskolin (10 micromol L(-1)) were added to the basolateral side of the tissue. KEY RESULTS: Secretomotor responses were significantly decreased in DIO jejunum compared to DR tissues. Glucose-stimulated increases in I(sc) in DR animals, that were sensitive to leptin inhibition, were significantly reduced in DIO rats. Decreased sodium glucose transporter-1 mediated glucose transport was accompanied by a concomitant increase in the expression of jejunal glucose transporter-2. CONCLUSIONS & INFERENCES: These data suggest that submucosal nerve function is compromised in DIO rats and electrogenic glucose transport is significantly decreased. The latter may represent an adaptive response to limit nutrient absorption in the jejunum from DIO rats. However, the loss of secretomotor control may lead to an altered host defense with a resultant change in intestinal flora contributing to the maintenance of obesity.
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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".