The basis of liver regeneration: A systems biology approach
Bibliographic record
Abstract
INTRODUCTION: Liver regeneration is a normal response to liver injury. The aim of this study was to determine the molecular basis of liver regeneration, through an integrative analysis of high-throughput gene expression datasets. METHODS: We identified and curated datasets pertaining to liver regeneration from the Gene Expression Omnibus, where regenerating liver tissue was compared to healthy liver samples. The key dysregulated genes and pathways were identified using Ingenuity Pathway Analysis software. There were three eligible datasets in total. RESULTS: In the early phase after hepatectomy, inflammatory pathways such as Nrf2 oxidative stress-mediated response and cytokine signaling were significantly upregulated. At peak regeneration, we discovered that cell cycle genes were predominantly expressed to promote cell proliferation. Using the Betweenness centrality algorithm, we discovered that Jun is the key central gene in liver regeneration. Calcineurin inhibitors may inhibit liver regeneration, based on predictive modeling. CONCLUSION: There is a paucity of human literature in defining the molecular mechanisms of liver regeneration along a time continuum. Nonetheless, using an integrative computational analysis approach to the available high-throughput data, we determine that the oxidative stress response and cytokine signaling are key early after hepatectomy, whereas cell cycle control is important at peak regeneration. The transcription factor Jun is central to liver regeneration and a potential therapeutic target. Future studies of regeneration in humans along a time continuum are needed to better define the underlying mechanisms, and ultimately enhance care of patients with acute and chronic liver failure while awaiting transplant.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".