Gene Expression Profiling Reveals the Mechanism and Pathophysiology of Mouse Liver Regeneration
Bibliographic record
Abstract
Comprehensive analysis of the changes in gene expression during liver regeneration was carried out by using an in-house microarray composed of 2,304 distinct mouse liver cDNA clones. Mice were subjected to partial two-thirds hepatectomy, and changes in mRNA levels were monitored up to 48 h. Of the 2,304 genes analyzed, 496 genes showed expression levels measurable at all time points after the partial hepatectomy. 317 genes were up- or down-regulated 2-fold or more at least at one time point during liver regeneration and were classified into eight clusters based on their expression patterns. With a more stringent cut-off value of +/-2 S.D., 68 genes were listed and were classified into five clusters. In these two analyses with different clustering criteria, functionally categorized genes showed similar cluster distributions. Genes involved in protein synthesis and posttranslational processing were significantly enriched in the cluster characterized by rapid gene activation and subsequent persistence. This suggests the importance of modulating the efficiency of protein supply and/or altering the composition of protein population from the early phase of hepatocyte proliferation. Genes for two major liver functions, i.e. plasma protein secretion and intermediate metabolism were enriched in distinct clusters exhibiting the features of gradual gene activation and sustained repression, respectively. Therefore, these genes are differentially regulated during the regeneration, possibly leading to changes in the flow of amino acids and energy from enzyme proteins to plasma proteins in their synthesis. Thus, clustering analysis of expression patterns of functionally classified genes gave insights into mechanism and pathophysiology of liver 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".