Cold atmospheric plasma combined with nanoparticles in cancer therapy
Bibliographic record
Abstract
Abstract This review explores the application of cold atmospheric plasma (CAP) and nanoparticles (NPs) in cancer therapy, highlighting their potential to enhance treatment efficacy and minimize side effects. CAP generates reactive oxygen and nitrogen species that selectively induce apoptosis in cancer cells, while NPs improve drug delivery, enhance targeting precision, and reduce adverse effects on healthy tissues. By summarizing various types of NPs, including gold, silver, magnetic, and other NPs, we evaluate their individual and combined effects with CAP across different cancer models. Our findings suggest that combined CAP‐NPs significantly enhance therapeutic outcomes by increasing cancer cell sensitivity and minimizing damage to surrounding tissues. This synergistic approach not only aligns with previous research on CAP's selective toxicity but also reveals new possibilities for optimizing cancer treatment through targeted NP delivery. Further clinical research is needed to establish the safety and efficacy of this combination, paving the way for novel, patient‐specific treatment strategies with improved outcomes.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".