Whole-Cell and Microcell Fusion for the Identification of Natural Regulators of Telomerase
Bibliographic record
Abstract
The ability of tumor cells to grow indefinitely contrasts sharply with the growth of normal cells. When normal human cells are grown in vitro , they undergo a limited number of divisions. This property, termed replicative senescence, is lost when cells become tumorigenic. However, whole-cell hybrids between normal and immortal cells are mortal, indicating that cellular senescence is dominant and normal cells contain genes that limit cell growth ( 1 , 2 ). These findings have been confirmed and expanded by microcell transfer studies in which individual chromosomes from donor cells are introduced into recipient cells. Depending on the recipient cells, transfer of at least seven different human chromosomes has been shown to induce cellular senescence ( 3 - 9 ). Thus, normal cells have developed many mechanisms to prevent unlimited cell growth. One of them is cellular suicide, or apoptosis, and is controlled (at least in part) by the proteins encoded by two tumor suppressor genes, namely p53 ( 10 ) and RB ( 11 ). Another apparent mechanism is the progressive shortening of the ends of chromosomes (telomeres), following each cycle of cellular replication, leading to a state of cellular senescence after the completion of a certain number of replicative cycles ( 12 ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".