Spatio-temporal mathematical model describing the interplay between biomechanics and cell kinetics during fibrotic scar formation
Bibliographic record
Abstract
Liver fibrosis is characterized by the accumulation of overexpressed extracellular matrix (ECM) proteins as a result of exposure of tissue to repeated damage. There are distinct patterns of fibrosis such as collagen septa (from tissue sections called “fibrotic walls”) connecting two central veins due to toxic injury. In the past decade, some computational models using either rule-based models 2D or partial differential equations of liver fibrosis to study the cellular and molecular mechanisms. Within a 3D single-cell-based model resolving tissue microarchitecture, we now incorporate the collagen fiber mechanics to address fibrosis formation. The same model approach already simulated regeneration after acute liver damage hence fibrosis formation is a further step towards a digital liver twin. The pattern-characterizing parameters in this study were obtained through image analysis of images from animal experiments that were compared to human histopathology. We explored alternative model mechanisms and parameters for a detailed in silico study of possible mechanism on the formation of characteristic fibrotic walls in liver fibrosis. Publication History Article published online: 18 January 2023 © 2023. Thieme. All rights reserved. Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".