Screening for supra-additive effects of cytotoxic drugs and gamma irradiation in an in vitro model for hepatocellular carcinoma
Bibliographic record
Abstract
Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world. A wide variety of treatment modalities is available for palliative therapy of HCC, although there is no strong evidence that these treatments can have a significant impact on survival. The aim of this work was to screen cytotoxic drugs relevant in the treatment of HCC for enhancement of the effect of irradiation in an in vitro model. As the majority of patients presenting with HCC suffer reduced liver function, attention was paid to low-dose effects of the cytotoxic drugs tested. To reflect this situation in vivo, multicellular tumor aggregates or "spheroids" of HepG2 cells were cultured and exposed to gamma irradiation alone or in combination with cisplatin for 4 h, gemcitabin for 4 or 24 h, or 5-fluorouracil for 4 h. In one experiment, the spheroids were cultured for 4 weeks in multiwell plates that allowed adhesion. Measurement of two-dimensional spheroid outgrowth was made every week for each spheroid. This kind of growth depends on the proliferation and motility of the cells that form the spheroid. In a second experiment, toxicity was evaluated by comparative growth curves by means of a three-dimensional growth assay and by histology. Supra-additive effects lasting for 4 weeks were observed for all drugs tested in combination with a gamma irradiation of 10 Gy.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".