Design of Hybrid MnO<sub>2</sub>‐Polymer‐Lipid Nanoparticles with Tunable Oxygen Generation Rates and Tumor Accumulation for Cancer Treatment
Bibliographic record
Abstract
Manganese dioxide (MnO2) nanoparticles (NPs) were discovered in previous work to be effective in improving tumor oxygenation (hypoxia) and reducing H2O2 and acidity in the tumor microenvironment (TME) via local injection. To develop MnO2 formulations useful for clinical application, hybrid NPs are designed with tailored hydrophobicity and structure suitable for intravenous injection, with good blood circulation, biocompatibility, high tumor accumulation, and programmable oxygen generation rate. Two different hybrid NPs are constructed by embedding polyelectrolyte‐MnO2 (PMD) in hydrophilic terpolymer/protein‐MnO2 (TMD) or hydrophobic polymer/lipid‐MnO2 (LMD) matrices. The in vitro reactivity of the MnO2 toward H2O2 is controlled by matrix material and NP structure and dependent on pH with up to two‐fold higher O2 generation rate at acidic (tumor) pH than at systemic pH. The hybrid NPs are found to be safe to cells in vitro and organs in vivo and effectively decrease tumor hypoxia and hypoxia‐inducible‐factor‐1alpha through local or systemic administration. Fast acting TMD reduces tumor hypoxia by 70% in 0.5 h by local injection. Slow acting LMD exhibits superior tumor accumulation and retention through the systemic administration and decreased hypoxia by 45%. These findings encourage a broader use of hybrid MD NPs to overcome TME factors for cancer treatment.
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 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".