Liver Aging and Pseudocapillarization in a Werner Syndrome Mouse Model
Bibliographic record
Abstract
Werner syndrome is a progeric syndrome characterized by premature atherosclerosis, diabetes, cancer, and death in humans. The knockout mouse model created by deletion of the RecQ helicase domain of the mouse Wrn homologue gene (Wrn(∆hel/∆hel)) is of great interest because it develops atherosclerosis and hypertriglyceridemia, conditions associated with aging liver and sinusoidal changes. Here, we show that Wrn(∆hel/∆hel) mice exhibit increased extracellular matrix, defenestration, decreased fenestration diameter, and changes in markers of liver sinusoidal endothelial cell inflammation, consistent with age-related pseudocapilliarization. In addition, hepatocytes are larger, have increased lipofuscin deposition, more frequent nuclear morphological anomalies, decreased mitochondria number, and increased mitochondrial diameter compared to wild-type mice. The Wrn(∆hel/∆hel) mice also have altered mitochondrial function and altered nuclei. Microarray data revealed that the Wrn(∆hel/∆hel) genotype does not affect the expression of many genes within the isolated hepatocytes or liver sinusoidal endothelial cells. This study reveals that Wrn(∆hel/∆hel) mice have accelerated typical age-related liver changes including pseudocapillarization. This confirms that pseudocapillarization of the liver sinusoid is a consistent feature of various aging models. Moreover, it implies that DNA repair may be implicated in normal aging changes in the liver.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".