MOF-derived intelligent arenobufagin nanocomposites with glucose metabolism inhibition for enhanced bioenergetic therapy and integrated photothermal-chemodynamic-chemotherapy
Bibliographic record
Abstract
Bioenergetic therapy based on tumor glucose metabolism is emerging as a promising therapeutic modality. To overcome the poor bioavailability and toxicity of arenobufagin (ArBu), a MOF-derived intelligent nanosystem, ZIAMH, was designed to facilitate energy deprivation by simultaneous interventions of glycolysis, OXPHOS and TCA cycle. Herein, zeolitic imidazolate framework-8 was loaded with ArBu and indocyanine green, encapsulated within metal-phenolic networks for chemodynamic therapy and hyaluronic acid modification for tumor targeting. ZIAMH nanoparticles can release ArBu in the tumor microenvironment for chemtherapy, and ICG enables photothermal therapy under near-infrared laser irradiation. In vitro and in vivo mechanism studies revealed that the ZIAMH nanoplatform downregulated glucose metabolism related genes, resulting in the reduction of energy substances and metabolites in tumors. Additionally, it significantly promoted cell apoptosis by upregulating pro-apoptotic proteins such as Bax, Bax/Bcl-2, cytochrome C. Animal studies have shown that the tumor inhibition efficiency of ZIAMH nanomedicines was three fold higher than that of free drugs. Therefore, this study provides a new strategy for glucose metabolism-mediated bioenergetic therapy and PTT/CDT/CT combined therapy for 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) | 0.000 | 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".