Trehalose Activates Hepatic and Myocardial Autophagy and Has Anti-Inflammatory Effects in db/db Diabetic Mice
Bibliographic record
Abstract
Db/db mice (carrying a mutation in the gene encoding leptin receptor) show autophagy suppression. Our aim was to evaluate the effect of autophagy inducer trehalose on liver and heart autophagy in db/db mice and to study inflammation dysregulation and the suitability of chitinases’ expression levels as diabetes markers. Thirty-eight male db/db mice and C57/BL mice (control) were used. The db/db model manifested inflammation symptoms: overexpression of TNF-α in the spleen and underexpression of IL-10 in the liver and spleen (cytokine imbalance). Simultaneously, we revealed decreased expression of chitotriosidase (CHIT1) and acid mammalian chitinase (CHIA) in the liver of db/db mice. CHIA expression in db/db mice is significantly lower only in the spleen. Trehalose treatment significantly reduced blood glucose concentration and glycated hemoglobin. Treatment of db/db mice by trehalose was followed by increased autophagy induction in the heart and liver (increased autolysosomes volume density studied by morphometric electron-microscopic method). Trehalose exerted beneficial cardiac effects possibly via increased lipophagy (uptake of lipid droplets). The autophagy activation by trehalose had several positive effects on the heart and liver of db/db mice; therefore, lipophagy activation seems to be a promising therapy for diabetes.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".