Genes that affect both cell growth and polarity mediate stem cell quiescence
Bibliographic record
Abstract
Stem cells possess the capacity to expand and self-renew and do so by dividing in either a symmetrical or an asymmetrical manner. Under particular circumstances, some stem cell populations can undergo prolonged cell cycle arrest or quiescence, until they are triggered to divide by a given stimulus. In cancer treatment, these populations represent a significant roadblock to efficient therapies as their non-dividing state renders them refractory to most commonly used cytotoxic interventions. In certain organisms, germline stem cells undergo quiescence if animals experience inappropriate growth conditions, and recent studies have determined that the level of insulin signaling is key in the regulation of their proliferation rate, and that it functions through at least two tumor suppressor genes, PTEN and LKB1. These gene products regulate both growth and polarity in diverse cellular contexts, while it remains unclear how they can modulate cell division and prevent tumorigenesis through each of these functions, and whether indeed these functions are separable. We hope that understanding how these tumor suppressor genes impinge on quiescent stem cell populations could provide us with a means of designing more effective therapies to reduce the frequency of stem cell-derived tumor growth that occurs following treatment.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".