Development of a Cellular System to Identify Modulators of B-Raf Induced Senescence in Human Fibroblasts
Bibliographic record
Abstract
Background: B-Raf is one of the earliest and most common genetic mutations observed in many different types of cancers. A single mutation in B-Raf cannot cause full-blown cancer, but may cause an observed phenotype called oncogene induced senescence (OIS). This suggests the need for cooperation between B-Raf and other genes for successful tumorigenesis. Objective: We look to characterize Human Fibroblast cells that are able to senesce in response to elevated oncogenic expression of B-Raf. Methods: We introduced ectopic expression of inducible B-Raf into human fibroblast cells. We characterized the successfully infected cells based on their ability to induce senescence. Results: We isolated cells of clonal origin and we identified the clone most responsive to B-Raf expression. Conclusions and Future Research: Our methodology proved to be effective in creating a model of B-Raf expression that can be used to study OIS. The next step is to screen the cells to identify genes that enable the cells to evade senescence. These genes could prove to be valuable chemotherapeutic targets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".