Weight Perturbation Alters Leptin Signal Transduction in a Region-Specific Manner throughout the Brain
Bibliographic record
Abstract
Diet-induced obesity (DIO) resulting from consumption of a high fat diet (HFD) attenuates normal neuronal responses to leptin and may contribute to the metabolic defense of an acquired higher body weight in humans; the molecular bases for the persistence of this defense are unknown. We measured the responses of 23 brain regions to exogenous leptin in 4 different groups of weight- and/or diet-perturbed mice. Responses to leptin were assessed by quantifying pSTAT3 levels in brain nuclei 30 minutes following 3 mg/kg intraperitoneal leptin. HFD attenuated leptin sensing throughout the brain, but weight loss did not restore central leptin signaling to control levels in several brain regions important in energy homeostasis, including the arcuate and dorsomedial hypothalamic nuclei. Effects of diet on leptin signaling varied by brain region, with results dependent on the method of weight loss (restriction of calories of HFD, ad lib intake of standard mouse chow). High fat diet attenuates leptin signaling throughout the brain, but some brain regions maintain their ability to sense leptin. Weight loss restores leptin sensing to some degree in most (but not all) brain regions, while other brain regions display hypersensitivity to leptin following weight loss. Normal leptin sensing was restored in several brain regions, with the pattern of restoration dependent on the method of weight loss.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".