Inhibition of mTOR enhances radiosensitivity of lung cancer cells and protects normal lung cells against radiation
Bibliographic record
Abstract
Radiotherapy has been used for a long time as a standard therapy for cancer; however, there have been no recent research breakthroughs. Radioresistance and various side-effects lead to the unexpected outcomes of radiation therapy. Specific and accurate targeting as well as reduction of radioresistance have been major challenges for irradiation therapy. Recent studies have shown that rapamycin shows promise for inhibiting tumorigenesis by suppressing mammalian target of rapamycin (mTOR). We found that the combination of rapamycin with irradiation significantly diminished cell viability and colony formation, and increased cell apoptosis, as compared with irradiation alone in lung cancer cell line A549, suggesting that rapamycin can enhance the effectiveness of radiation therapy by sensitizing cancer cells to irradiation. Importantly, we observed that the adverse effects of irradiation on a healthy lung cell line (WI-38) were also offset. No enhanced protein expression of mTOR signaling was observed in WI-38 cells, which is normally elevated in lung cancer cells. Moreover, DNA damage was significantly less with the combination therapy than with irradiation therapy alone. Our data suggest that the incorporation of rapamycin during radiation therapy could be a potent way to improve the sensitivity and effectiveness of radiation therapy as well as to protect normal cells from being damaged by irradiation.
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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 | 0.000 |
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".