Synergy between Non-Thermal Plasma with Radiation Therapy and Olaparib in a Panel of Breast Cancer Cell Lines
Bibliographic record
Abstract
Cancer therapy has evolved to a more targeted approach and often involves drug combinations to achieve better response rates. Non-thermal plasma (NTP), a technology rapidly expanding its application in the medical field, is a near room temperature ionized gas capable of producing reactive species, and can induce cancer cell death both in vitro and in vivo. Here, we used proliferation assay to characterize the plasma sensitivity of fourteen breast cancer cell lines. These assays showed that all tested cell lines were sensitive to NTP. In addition, a good correlation was found comparing cell sensitivity to NTP and radiation therapy (RT), where cells that were sensitive to RT were also sensitive to plasma. Moreover, in some breast cancer cell lines, NTP and RT have a synergistic effect. Adding a dose of PARP-inhibitor olaparib to NTP treatment always increases the efficacy of the treatment. Olaparib also exhibits a synergistic effect with NTP, especially in triple negative breast cancer cells. Results presented here help elucidate the position of plasma use as a potential breast cancer treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| 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".