Bibliographic record
Abstract
Hepatocytes in adults are in a nonproliferative state but they have high capacity to regenerate within few hours after an injury. After partial hepatectomy or chemical injury, hepatocytes undergo a synchronized multistep process consisting of priming/initiation, proliferation, and termination. These distinct steps are essential for restoring the structure and functions of liver. The mechanisms involved in each of these steps of regeneration are well documented from various laboratories and are described in several reviews. We briefly describe these steps and the involvement of various cytokines and growth factors for cell regeneration in this short review. Liver cell regeneration may also involve stem cell proliferation. The regenerating cells require large amounts of zinc within a short time, and this requirement is met by induction of a zinc and copper binding protein, metallothionein (MT), during the priming step, soon after an injury. There are several reports on the transfer of zinc from MT to various metalloenzymes and transcription factors. Genetically modified mouse models have been used to study the involvement of interleukin (IL)-6 and tumor necrosis factor (TNF)-alpha in cell regeneration. The use of an MT-knockout mouse has enabled us to investigate the specific role of MT in liver regeneration after partial hepatectomy, chemical injury, and fibrosis. Several studies have suggested a defective liver regeneration after an injury in MT-knockout mice. There is cumulative evidence that indicates an essential role for MT in liver cell regeneration.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".