Ablation of Gap Junction Protein Improves the Efficiency of Nanozyme‐Mediated Catalytic/Starvation/Mild‐Temperature Photothermal Therapy
Bibliographic record
Abstract
Reactive oxygen species (ROS)-mediated tumor catalytic therapy is typically hindered by gap junction proteins that form cell-to-cell channels to remove cytotoxic ROS, thereby protecting tumor cells from oxidative damage. In this work, a multifunctional nanozyme, FePGOGA, is designed and prepared by Fe(III)-mediated oxidative polymerization (FeP), followed by glucose oxidase (GOx) and GAP19 peptides co-loading through electrostatic and π-π interactions. The FePGOGA nanozyme exhibits excellent cascade peroxidase- and glutathione-oxidase-like activities that efficiently catalyze hydrogen peroxide conversion to hydroxyl radicals and convert reduced glutathione to oxidized glutathione disulfide. The loaded GOx starves the tumors and aggravates tumor oxidative stress through glucose decomposition, while GAP19 peptides block the hemichannels by inducing degradation of Cx43, thus increasing the accumulation of intracellular ROS, and decreasing the transport of intracellular glucose. Furthermore, the ROS reacts with primary amines of heat shock proteins to destroy their structure and function, enabling tumor photothermal therapy at the widely sought-after mild temperature (mildPTT, ≤45 °C). In vivo experiments demonstrate the significant antitumor effectof FePGOGA on cal27 xenograft tumors under near-infrared light irradiation. This study demonstrates the successful ablation of gap junction proteins to overcome resistance to ROS-mediated therapy, providing a regulator to suppress tumor self-preservation during tumor starvation, catalytic therapy, and mildPTT.
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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".