Alteration in the number of neuronal and non-neuronal cells in mouse models of obesity
Bibliographic record
Abstract
Abstract Obesity is defined as abnormal or excessive fat accumulation that may impair health and is a risk factor for developing other diseases, such as type 2 diabetes and cardiovascular disorder. Obesity is also associated with structural and functional alterations in the brain, and this condition has been shown to increase the risk of Alzheimer’s disease. However, while obesity has been associated with neurodegenerative processes, its impact on brain cell composition remains to be determined. In the current study, we used the isotropic fractionator method to determine the absolute composition of neuronal and non-neuronal cells in different brain regions of the genetic mouse models of obesity Lepob/ob and LepRNull/Null. Our results show that 10- to 12-month-old female Lepob/ob and LepRNull/Null mice have reduced neuronal number and density in the hippocampus compared to C57BL/6 wild-type mice. Furthermore, LepRNull/Null mice have increased density of non-neuronal cells, mainly glial cells, in the hippocampus, frontal cortex and hypothalamus compared to wild-type or Lepob/ob mice, indicating enhanced inflammatory responses in different brain regions of the LepRNull/Null model. Collectively, our findings suggest that obesity might cause changes in brain cell composition that are associated with neurodegenerative and inflammatory processes in different brain regions of female mice.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".