Multifunctional Albumin–MnO<sub>2</sub> Nanoparticles Modulate Solid Tumor Microenvironment by Attenuating Hypoxia, Acidosis, Vascular Endothelial Growth Factor and Enhance Radiation Response
Bibliographic record
Abstract
Insufficient oxygenation (hypoxia), acidic pH (acidosis), and elevated levels of reactive oxygen species (ROS), such as H2O2, are characteristic abnormalities of the tumor microenvironment (TME). These abnormalities promote tumor aggressiveness, metastasis, and resistance to therapies. To date, there is no treatment available for comprehensive modulation of the TME. Approaches so far have been limited to regulating hypoxia, acidosis, or ROS individually, without accounting for their interdependent effects on tumor progression and response to treatments. Hence we have engineered multifunctional and colloidally stable bioinorganic nanoparticles composed of polyelectrolyte-albumin complex and MnO2 nanoparticles (A-MnO2 NPs) and utilized the reactivity of MnO2 toward peroxides for regulation of the TME with simultaneous oxygen generation and pH increase. In vitro studies showed that these NPs can generate oxygen by reacting with H2O2 produced by cancer cells under hypoxic conditions. A-MnO2 NPs simultaneously increased tumor oxygenation by 45% while increasing tumor pH from pH 6.7 to pH 7.2 by reacting with endogenous H2O2 produced within the tumor in a murine breast tumor model. Intratumoral treatment with NPs also led to the downregulation of two major regulators in tumor progression and aggressiveness, that is, hypoxia-inducible factor-1 alpha and vascular endothelial growth factor in the tumor. Combination treatment of the tumors with NPs and ionizing radiation significantly inhibited breast tumor growth, increased DNA double strand breaks and cancer cell death as compared to radiation therapy alone. These results suggest great potential of A-MnO2 NPs for modulation of the TME and enhancement of radiation response in the treatment of cancer.
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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) | 0.000 | 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 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".