Treatment with sulodexide restores compromised glucose tolerance in diet‐induced obese mice
Bibliographic record
Abstract
We showed in a previous study that feeding mice a high‐fat diet (HFD) resulted in an early reduction in endothelial glycocalyx barrier properties in hindlimb muscle microcirculation which was associated with increased blood glucose levels during a glucose tolerance test. In the current study we investigated whether sulodexide (SUL), an endothelial glycocalyx mimetic, could improve glucose tolerance at the early stage of diet‐induced obesity (DIO). C57Bl/6 mice were fed a HFD for 6 weeks (n=12); SUL (0.15 mg/mL) was given in the drinking water in the last two weeks (n=5). After 6 weeks the mice were anesthetized and glucose tolerance was measured by i.p. injection of 1 g/kg glucose and the area under the curve of blood glucose (AUCgluc) and plasma insulin (AUCins) were determined for 90 minutes after glucose injection. While baseline glucose and insulin levels were not affected by the SUL treatment, AUCgluc was significantly lower in the SUL treated mice (242 ± 43 versus 404 ±‐ 43 mmol/L * 90 min). AUCins was not affected by the SUL treatment (2108 ± 431 versus 2952 ± 512 μU/ml * 90 min). The results of the current study suggest that preventing or diminishing endothelial glycocalyx loss may be a promising intervention for improving glucose homeostasis in obesity. Supported by DFN 2006.00.027, and CTMM work package 01C‐104–04‐PREDICCT
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".