High concentrations of fructose cause brain damage in mice
Bibliographic record
Abstract
Excessive fructose consumption is associated with the incidence of obesity and systemic inflammation, resulting in increased oxidative damage and failure to the function of brain structures. Thus, we hypothesized that fructose consumption will significantly increase inflammation, oxidative damage, and mitochondrial dysfunction in the mouse brain and, consequently, memory damage. The effects of different fructose concentrations on inflammatory and biochemical parameters in the mouse brain were evaluated. Male Swiss mice were randomized into four groups: control, with exclusive water intake, 5%, 10%, and 20% fructose group. The 10% and 20% fructose groups showed an increase in epididymal fat, in addition to higher food consumption. Inflammatory markers were increased in epididymal fat and in some brain structures. In the evaluation of oxidative damage, it was possible to observe significant increases in the hypothalamus, prefrontal cortex, and hippocampus. In the epididymal fat and in the prefrontal cortex, there was a decrease in the activity of the mitochondrial respiratory chain complexes and an increase in the striatum. Furthermore, short memory was impaired in the 10% and 20% groups but not long memory. In conclusion, excess fructose consumption can cause fat accumulation, inflammation, oxidative damage, and mitochondrial dysfunction, which can damage brain structures and consequently memory.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".