Alteration in the number of neuronal and non-neuronal cells in mouse models of obesity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it