Automated quantification of collagen proportionate area correlates with molecular and histological markers of fibrosis in CCl4-treated rats
Bibliographic record
Abstract
Liver fibrosis results from chronic liver injury and is characterized by excessive accumulation of extracellular matrix due to sustained wound-healing responses. Although histological evaluation remains the gold standard for fibrosis assessment, its subjectivity can limit reproducibility. In this study, we evaluated an automated image analysis software, MorphoQuant, for liver fibrosis quantification in a rat model of CCl4-induced liver injury. Male Wistar rats were treated with CCl4 or vehicle for six weeks, and fibrosis severity was assessed using both the conventional Ishak staging system and automated quantification of collagen proportionate area (CPA). Automated CPA strongly correlated with Ishak stage, liver index, and plasma aminotransferase levels. Additionally, CPA values were significantly associated with the expression of fibrosis-related genes and macrophage infiltration, highlighting the software's ability to assess both fibrosis progression and inflammatory responses. These findings support the use of MorphoQuant as a robust, reader-independent tool that enhance analytical consistency in preclinical models of liver fibrosis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".