Cold atmospheric plasma‐mediated tumor microenvironment remodeling for cancer treatment
Bibliographic record
Abstract
Abstract Cancer is still a serious clinical concern, and current therapy is ineffective due to the complex tumor microenvironment (TME). Thus, TME targeting has gained recognition as a significant therapeutic target in cancer therapy. Among these, cold atmospheric plasma (CAP) has been proposed as an emerging and novel cancer treatment owing to its unique characteristics of non‐invasive nature and selective tumor cell killing. The current investigation reveals CAP as an effective strategy for TME modulation and tumor eradication, emphasizing its potential to enhance antitumor responses. This review explores the therapeutic potential of CAP in cancer treatment, with a particular focus on its impact on the TME and the underlying mechanisms of tumor cell death. Initially, the review provides a comprehensive overview of the TME and underscores its critical role in cancer progression and treatment responsiveness. It then examines the efficacy of CAP across various in vitro and in vivo tumor models, highlighting its modulatory effects on key components of the TME, including immune cells, stromal cells, and cancer physiological hallmarks such as immune suppression, hypoxia, acidosis, angiogenesis, and metabolism. Furthermore, the review synthesizes evidence on the diverse mechanisms of CAP‐induced tumor cell death, including apoptosis, pyroptosis, ferroptosis, autophagy, and necrosis. Together, findings from a wide range of experimental studies demonstrate the promise of CAP as a selective and safe antitumor agent, capable of reprogramming the TME and inducing numerous forms of cancer cell death. In addition, the review addresses current challenges and future directions for CAP and stresses the necessity of protocol standardization, large‐scale experimental validation, and rigorous safety evaluation before clinical implementation. Finally, the review anticipates CAP as a revolutionary tool in cancer care, offering hope for improved therapeutic efficacy and a paradigm shift in cancer treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".