Theranostic potential of manganese dioxide nanoparticles for targeting tumor hypoxia during MR-guided radiotherapy
Bibliographic record
Abstract
Purpose Magnetic resonance (MR)-guided radiotherapy (MRgRT) is an attractive treatment option for many patients with cancer, allowing higher radiation doses to be safely delivered. However, even with more precise tumor targeting and higher doses, hypoxia remains an important clinical challenge, making tumors more radioresistant and detracting from the benefits of dose escalation. Nanoparticles loaded with manganese dioxide (MnO2) have been developed as theranostic agents to improve MRgRT. We review the MR-enhancing and oxygen-generating properties of MnO2 nanoparticles, the evidence that MnO2 nanoparticles can improve tumor response to RT, and the opportunities for further research to support translation into the clinic.Conclusion The theranostic potential of MnO2 nanoparticles lies in the dual functionality of providing tumor-specific MR enhancement for RT planning and image guidance, while also generating oxygen in the tumor microenvironment (TME) to overcome hypoxia-induced radioresistance. Several preclinical studies have demonstrated lower levels of hypoxia and improved tumor response when RT is combined with MnO2 nanoparticles. In addition, MnO2 nanoparticles have been reported to deplete intracellular antioxidants and create an immunogenic, less immunosuppressive TME, which may also enhance radiotherapy efficacy. These encouraging findings support further clinical evaluation in patients receiving MRgRT.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".