Effect of Caloric Restriction on Hepatic Sinusoidal System and Stellate Cells in Mice
Bibliographic record
Abstract
Aging associated changes in liver include reduced hepatic blood flow, increased number of stellate cells, and collagen deposits in perisinusoidal space. We tested the possibility of mitigating these changes with caloric restriction. Two-month-old mice were subjected to 30 percent caloric restriction for 12 months and then examined for the effect of caloric restriction on the sinusoidal network, collagen deposition, and the number of stellate cells. Using intravital fluorescence microscopy, assessments were made on sinusoidal diameter, density, volumetric flow, perfusion index, and autofluorescence of vitamin A that was primarily stored with lipid droplets in stellate cells. A significant effect was observed in the vitamin A autofluorescence of stellate cells; stellate cell associated fluorescence was diminished in terms of number and size of fluorescent spots. Caloric restriction reduced collagen deposits in liver sections and lowered the gene expression of α 1-(I) collagen but not α -smooth muscle actin. No differences were detected in sinusoidal dimension measurements. Our results showed that caloric restriction was effective in ameliorating the increase in stellate cells and the mild fibrosis in old mice. However, caloric restriction had no impact on stellate cell activity level as indicated by the unaffected α -smooth muscle actin expression.
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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 | 0.000 |
| 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".