Sensitization of breast cancer cells to doxorubicin via stable cell line generation and overexpression of DFF40
Bibliographic record
Abstract
There are a number of reports demonstrating a relationship between the alterations in DFF40 expression and development of some cancers. Here, increased DFF40 expression in T-47D cells in the presence of doxorubicin was envisaged for therapeutic usage. The T-47D cells were transfected with an eukaryotic expression vector encoding the DFF40 cDNA. Following incubation with doxorubicin, propidium iodide (PI) staining was used for cell cycle distribution analysis. The rates of apoptosis were determined by annexin V/PI staining. Apoptosis was also evaluated using the DNA laddering analysis. The viability of DFF40-transfected cells incubated with doxorubicin was significantly decreased compared with control cells. However, there were no substantial changes in the cell cycle distribution of pIRES2-DFF40 cells incubated with doxorubicin compared to control cells. The expression of DFF40, without doxorubicin incubation, had also no significant effect on the cell cycle distribution. There was no DNA laddering in cells transfected with the empty pIRES2 vector when incubated with doxorubicin. In contrast, DNA laddering was observed in DFF40 transfected cells in the presence of doxorubicin after 48 h. Also, the expression of DFF40 and DFF45 was increased in DFF40 transfected cells in the presence of doxorubicin enhancing cell death. Collectively our results indicated that co-treatment of DFF40-transfected cells with doxorubicin can enhance the killing of these tumor cells via apoptosis. Thus, modulation of DFF40 level may be a beneficial strategy for treatment of chemo-resistant cancers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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".