Subdiaphragmatic Vagotomy With Pyloroplasty Ameliorates the Obesity Caused by Genetic Deletion of the Melanocortin 4 Receptor in the Mouse
Bibliographic record
Abstract
Background/Objectives: We tested the hypothesis that abolishing vagal nerve activity will reverse the obesity phenotype of melanocortin 4 receptor knockout mice (Mc4r -/-). Subjects/Methods: In two separate studies, we examined the efficacy of bilateral subdiaphragmatic vagotomy (SDV) with pyloroplasty in the prevention and treatment of obesity in Mc4r -/-mice. Results: In the first study, SDV prevented > 20% increase in body weight (BW) associated with this genotype. This was correlated with a transient reduction in overall food intake (FI) in the preventative arm of the study. Initially, SDV mice had reduced weekly FI; however, FI normalized to that of controls and baseline FI within the 8-week study period. In the second study, the severe obesity that is characteristic of the adult Mc4r -/- genotype was significantly improved by SDV with a magnitude of 30% loss in excess BW over a 4-week period. Consistent with the first preventative study, within the treatment arm, SDV mice also demonstrated a transient reduction in FI relative to control and baseline levels that normalized over subsequent weeks. In addition to the accompanying loss in weight, mice subjected to SDV showed a decrease in respiratory exchange ratio (RER), and an increase in locomotor activity (LA). Analysis of the white fat-pad deposits of these mice showed that they were significantly less than the control groups. Conclusions: Altogether, our data demonstrates that SDV both prevents gain in BW and causes weight loss in severely obese Mc4r -/- mice. Moreover, it suggests that an important aspect of weight reduction for this type of monogenic obesity involves loss of signaling in the efferent vagal motor neurons.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".