A growth-constrained environment drives tumor progression <i>in</i> <i>vivo</i>
Bibliographic record
Abstract
We recently have shown that selective growth of transplanted normal hepatocytes can be achieved in a setting of cell cycle block of endogenous parenchymal cells. Thus, massive proliferation of donor-derived normal hepatocytes was observed in the liver of rats previously given retrorsine (RS), a naturally occurring alkaloid that blocks proliferation of resident liver cells. In the present study, the fate of nodular hepatocytes transplanted into RS-treated or normal syngeneic recipients was followed. The dipeptidyl peptidase type IV-deficient (DPPIV(-)) rat model for hepatocyte transplantation was used to distinguish donor-derived cells from recipient cells. Hepatocyte nodules were chemically induced in Fischer 344, DPPIV(+) rats; livers were then perfused and larger (>5 mm) nodules were separated from surrounding tissue. Cells isolated from either tissue were then injected into normal or RS-treated DPPIV(-) recipients. One month after transplantation, grossly visible nodules (2--3 mm) were seen in RS-treated recipients transplanted with nodular cells. They grew rapidly, occupying 80--90% of the host liver at 2 months, and progressed to hepatocellular carcinoma within 4 months. By contrast, no liver nodules developed within 6 months when nodular hepatocytes were injected into the liver of recipients not exposed to RS, although small clusters of donor-derived cells were present in these animals. Taken together, these results directly point to a fundamental role played by the host environment in modulating the growth and the progression rate of altered cells during carcinogenesis. In particular, they indicate that conditions associated with growth constraint of the host tissue can drive tumor progression in vivo.
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 | 0.001 | 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 | 0.001 |
| 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".