RSL3-loaded nanoparticles amplify the therapeutic potential of cold atmospheric plasma
Bibliographic record
Abstract
Cold atmospheric plasma (CAP) has exhibited exciting potential for cancer treatment. Reactive oxygen and nitrogen species (RONS), the primary constituents in CAP, contribute to cancer cell death by elevating oxidative stress in cells. However, several intrinsic cellular antioxidant defense systems exist, such as the glutathione peroxidase 4 (GPX4) enzyme, which dampens the cell-killing efficacy of CAP. RAS-selective lethal 3 (RSL3), also known as a ferroptosis inducer, is a synthetic GPX4 inhibitor. Therefore, we hypothesized that RSL3 can amplify CAP-induced cell death by inhibition of GPX4. In this study, we showed that RSL3 loaded in poly (ethylene glycol)-block-poly(lactide-co-glycolide) (PLGA-PEG) nanoparticles can enhance CAP-induced cell deaths in 4T1 tumor cells. Furthermore, the combination of CAP and RSL3 also promoted cancer immunogenic cell death (ICD), induced dendritic cell (DC) maturation, and macrophage polarization, initiating tumor-specific T-cell mediated immune responses against tumors. For in vivo application, RSL3@NP was co-delivered with CAP via injectable Pluronic hydrogel. In 4T1-bearing mice, hydrogel-mediated delivery of CAP and RSL3-loaded nanoparticles can effectively elicit potent anti-tumor immune responses and inhibit tumor growth.
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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 | 0.000 |
| 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".