Strategy of “Controllable Ions Interference” for Boosting MRI-Guided Ferroptosis Therapy of Tumors
Bibliographic record
Abstract
Chemotherapy for oral squamous cell carcinoma (OSCC) is often marred by the development of multidrug resistance and systemic adverse effects. Metal ion interference therapy (MIIT) has risen as an innovative strategy to disrupt the intracellular metal ion equilibrium in tumor cells, potentially overcoming drug resistance. However, the effectiveness of cancer treatment that relies on delivering single metal ions to tumor site is often constrained. To address this, we have developed a therapeutic nanoplatform employing hollow mesoporous manganese dioxide nanoparticles (HMON) which harness the chelating properties of tannic acid to control the loading and release of Zn 2+ and Pt 2+, i.e., Zn@CDDP@HMON. In acidic tumor microenvironment, Zn 2+ and Pt 2+ ions strategically released from nanoplatform can inhibit mitochondrial respiration and activate NADPH oxidases (NOXs), respectively, increasing superoxide anion (O 2 • – ) and hydrogen peroxide production (H 2 O 2 ). The released Mn 4+ consumes intracellular glutathione (GSH) to generate Mn 2+, which reacts with H 2 O 2 in a Fenton-like reaction, producing hydroxyl radicals (•OH) and inducing lipid peroxidation (LPO). The depletion of GSH also inhibits GPX4 activity, sensitizing tumor cells to ferroptosis. Furthermore, the reduced Mn 2+ facilitates T 1 -MRI imaging, allowing for real-time monitoring of nanoplatform distribution and accumulation in tumors.
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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) | 0.001 | 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".