Live and Let Live: The Remarkable Story of HEK293 Cells
Bibliographic record
Abstract
Live and Let Live: The Remarkable Story of HEK293 CellsHow a single cell line lit the spark that turned gene therapies from theoretical possibility to therapeutic reality All scientists dream of making an impact.Few can look back and identify a single experiment that led to the creation of an entire scientific field.When Frank Graham set out to transform human embryonic kidney (HEK) cells with adenoviral DNA in early 1973, he was trying to understand why some adenoviruses caused cancer and some didn't.He wasn't trying to set in motion the creation of a new field of medical research, with a multibillion-dollar market value.But that's exactly what he did.Frank had left Canada three years earlier to begin his postdoctoral research with Professor Alex van der Eb at the University of Leiden in the Netherlands.Until then, he hadn't been particularly interested in adenoviruses, but finding a postdoctoral supervisor working in that area changed his mind.''I decided to investigate the correlation between low GC content in adenoviral DNA and oncogenicity,'' Graham recalls.''At that time, nobody knew much about how or why cancers started, so using oncogenic viruses to transform normal cells into cancerous cells in culture seemed a good in vitro model for tumour induction.Perhaps it sounds naı ¨ve now, but given the little we understood back then, seeking to understand what made
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".