Targeted Magnetic Resonance Imaging and Modulation of Hypoxia with Multifunctional Hyaluronic Acid‐MnO<sub>2</sub> Nanoparticles in Glioma
Bibliographic record
Abstract
Abstract Manganese dioxide (MnO 2 )‐based nanoparticles are a promising tumor microenvironment‐responsive nanotheranostic carrier for targeted magnetic resonance imaging (MRI) and for alleviating tumor hypoxia. However, the complexity and potential toxicity of the present common synthesis methods limit their clinical application. Herein, multifunctional hyaluronic acid‐MnO 2 nanoparticles (HA‐MnO 2 NPs) are synthesized in a simple way by directly mixing sodium permanganate with HA aqueous solutions, which serve as both a reducing agent and a surface‐coating material. The obtained HA‐MnO 2 NPs show an improved water‐dispersibility, fine colloidal stability, low toxicity, and responsiveness to the tumor microenvironment (high H 2 O 2 and high glutathione, low pH). After intravenous injection, HA‐MnO 2 NPs exhibit a high imaging sensitivity for detecting rat intracranial glioma with MRI for a prolonged period of up to 3 d. These nanoparticles also effectively alleviate the tumor hypoxia in a rat model of intracranial glioma. The downregulation of VEGF and HIF‐1α expression in intracranial glioma validates the sustained attenuation effect of HA‐MnO 2 NPs on tumor hypoxia. These results show that HA‐MnO 2 NPs can be used for sensitive, targeted MRI detection of gliomas and simultaneous attenuation of tumor hypoxia.
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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".