Hybrid Manganese Dioxide Nanoparticles Potentiate Radiation Therapy by Modulating Tumor Hypoxia
Bibliographic record
Abstract
Abstract Hypoxia in the tumor microenvironment (TME) mediates resistance to radiotherapy and contributes to poor prognosis in patients receiving radiotherapy. Here we report the design of clinically suitable formulations of hybrid manganese dioxide (MnO2) nanoparticles (MDNP) using biocompatible materials to reoxygenate the TME by reacting with endogenous H2O2. MDNP containing hydrophilic terpolymer-protein-MnO2 or hydrophobic polymer-lipid-MnO2 provided different oxygen generation rates in the TME relevant to different clinical settings. In highly hypoxic murine or human xenograft breast tumor models, we found that administering either MDNP formulation before radiotherapy modulated tumor hypoxia and increased radiotherapy efficacy, acting to reduce tumor growth, VEGF expression, and vascular density. MDNP treatment also increased apoptosis and DNA double strand breaks, increasing median host survival 3- to 5-fold. Notably, in the murine model, approximately 40% of tumor-bearing mice were tumor-free after a single treatment with MDNPs plus radiotherapy at a 2.5-fold lower dose than required to achieve the same curative treatment without MDNPs. Overall, our findings offer a preclinical proof of concept for the use of MDNP formulations as effective radiotherapy adjuvants. Cancer Res; 76(22); 6643–56. ©2016 AACR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".