Ex vivo mechanical and microstructural evaluation of a changing hepatic microstructure
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Recent advancements in non-invasive imaging of the liver hold significant potential for disease diagnosis and monitoring. However, the influence of different microstructural features on clinically derived mechanical biomarkers in the liver are not fully elucidated. The aim of this study was to investigate the influence of microstructural changes within liver tissue on mechanics. METHODS: The impact of storage and preservation, as well as bulk microstructural changes via enzymatic treatments were evaluated by mechanical testing and histological processing on porcine liver. RESULTS: We found that the preservation method chosen for ex vivo liver tissue significantly influences the compressive material properties of the tissue, while not impacting those in tension. Additionally, we found that enzymatic treatments via collagenase alter the microstructure and mechanics, again more significantly in compression, of liver tissue. CONCLUSION: This work lays foundational insights for future studies which aim to develop ex vivo liver models to better understand the changing microstructure in the liver and its influence on mechanics, ultimately improving our understanding of clinically derived biomarkers.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".