A Tumor Vascular‐Targeted Interlocking Trimodal Nanosystem That Induces and Exploits Hypoxia
Bibliographic record
Abstract
Vascular-targeted photodynamic therapy (VTP) is a recently approved strategy for treating solid tumors. However, the exacerbated hypoxic stress makes tumor eradication challenging with such a single modality approach. Here, a new graphene oxide (GO)-based nanosystem for rationally designed, interlocking trimodal cancer therapy that enables VTP using photosensitizer verteporfin (VP) (1) with codelivery of banoxantrone dihydrochloride (AQ4N) (2), a hypoxia-activated prodrug (HAP), and HIF-1α siRNA (siHIF-1α) (3) is reported. The VTP-induced aggravated hypoxia is highly favorable for AQ4N activation into AQ4 (a topoisomerase II inhibitor) for chemotherapy. However, the hypoxia-induced HIF-1α acts as a "hidden brake," through downregulating CYP450 (the dominant HAP-activating reductases), to substantially hinder AQ4N activation. siHIF-1α is rationally adopted to suppress the HIF-1α expression upon hypoxia and further enhance AQ4N activation. This trimodal nanosystem significantly delays the growth of PC-3 tumors in vivo compared to the control nanoparticles carrying VP, AQ4N, or siHIF-1α alone or their pairwise combinations. This multimodal nanoparticle design presents, the first example exploiting VTP to actively induce hypoxia for enhanced HAP activation. It is also revealed that HAP activation is still insufficient under hypoxia due to the hidden downregulation of the HAP-activating reductases (CYP450), and this can be well overcome by GO nanoparticle-mediated siHIF-1α intervention.
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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".