Liver Fibrosis and Altered Matrix Synthesis
Bibliographic record
Abstract
Liver fibrosis represents the uniform response of liver to toxic, infectious or metabolic agents. The process leading to liver fibrosis resembles the process of wound healing, including the three phases following tissue injury: inflammation, synthesis of collagenous and noncollagenous extracellular matrix components, and tissue remodelling (scar formation). While a single liver tissue injury can be followed by an almost complete restitution ad integrum, the persistence of the original damaging noxa results in tissue damage. During the establishment of liver fibrosis, the basement membrane components collagen type IV, entactin and laminin increase and form a basement membrane-like structure within the space of Disse. The number of endothelial fenestrae of the sinusoids decreases. These changes of the sinusoids are called 'capillarization' because the altered structure of the sinusoids resembles that of capillaries. At the cellular level, origin of liver fibrogenesis is initiated by the damage of hepatocytes, resulting in the recruitment of inflammatory cells and platelets, and activation of Kupffer cells, with subsequent release of cytokines and growth factors. The hepatic stellate cells seem to be the primary target cells for these inflammatory stimuli, because during fibrogenesis, they undergo an activation process to a myofibroblast-like cell, which represents the major matrix-producing cell. Based on this pathophysiological mechanism, therapeutic methods are developed to inhibit matrix synthesis or stimulate matrix degradation. A number of substances are currently being tested that either neutralize fibrogenic stimuli and prevent the activation of hepatic stellate cells, or directly modulate the matrix metabolism. However, until now, the elimination of the hepatotoxins has been the sole therapeutic concept available for the treatment of liver fibrogenesis in humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".