Formation of Benign Tumours by Stem Cell Misregulation
Bibliographic record
Abstract
Our tissues usually have just the right number of cells to optimally fulfil their function. Not enough cells within a tissue can lead to dysfunction, while too many cells result in a tumour. Yet, how this homeostatic balance is maintained remains poorly defined. Most differentiated cells within tissues have a finite lifespan and need to be replaced at a corresponding pace to maintain tissue homeostasis. These new differentiated cells are generated by proliferation of the stem/progenitor cells that serve the tissue. Work in simple invertebrates clearly suggests stem cells respond to at least two types of signals: niche signaling and growth factors. Niche signals promote the undifferentiated state by preventing differentiation, and thus allow for stem cell self-renewal. Growth factor sources comprise a systemic input reflecting the animal’s nutritional status, and a localized, homeostatic feedback from the tissue that the stem cells serve. That homeostatic signal couples stem cell proliferation rates to the tissue’s need for new differentiated cells. Evidence from simple organisms suggests two types of benign tumours can arise from deregulation of either niche or homeostatic signaling. Namely, constitutive niche signaling promotes the formation of undifferentiated “stem cell” tumours, while defective homeostatic signaling leads to the formation of differentiated tumours. We propose that these principles may be conserved and underlie benign tumour formation in humans, while benign tumours can evolve into cancer.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".