Recent Advances in Plasma-Based Cancer Treatments: Approaching Clinical Translation through an Intracellular View
Bibliographic record
Abstract
Plasma medicine is a multidisciplinary field of research which is combining plasma physics and chemistry with biology and clinical medicine to launch a new cancer treatment modality. It mainly relies on utilizing low temperature plasmas in atmospheric pressure to generate and instill a cocktail of reactive species to selectively target malignant cells for inhibition the cell proliferation and tumor progression. Following a summarized review of primary in vitro and in vivo studies on the antitumor effects of low temperature plasmas, this article briefly outlines the plasma sources which have been developed for cancer therapeutic purposes. Intracellular mechanisms of action and significant pathways behind the anticancer effects of plasma and selectivity toward cancer cells are comprehensively discussed. A thorough understanding of involved mechanisms helps investigators to explicate many disputes including optimal plasma parameters to control the reactive species combination and concentration, transferring plasma to the tumors located in deep, and determining the optimal dose of plasma for specific outcomes in clinical translation. As a novel strategy for cancer therapy in clinical trials, designing low temperature plasma sources which meet the technical requirements of medical devices still needs to improve in efficacy and safety.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".