Nanostructured Peptidotoxins as Natural Pro-Oxidants Induced Cancer Cell Death via Amplification of Oxidative Stress
Bibliographic record
Abstract
Melittin (Mel), one of the host defense peptides derived from the venom of honeybees, demonstrates substantial anticancer properties, which is attributed to augmenting reactive oxygen species (ROS) generation. However, little has been reported on its pro-oxidation capacity in cancer oxidation therapy. In this study, an ROS amplifying nanodevice was fabricated through direct complexation of two natural pro-oxidants, Mel and condensed epigallocatechin gallate (pEGCG). The obtained nanocomplex (NC) was further covered with phenylboronic acid derivatized hyaluronic acid (pHA) through the ROS-responsive boronate ester coordination bond to produce pHA-NC. Upon undergoing receptor-mediated endocytosis into cancer cells, the inner cores of pHA-NC will be partially uncovered once pHA corona is degraded by hyaluronidase and will then escape from the lysosome by virtue of cytolytic Mel. The elevated ROS level in the tumor cytoplasm can disrupt the boronate ester bond to facilitate drug release. Both Mel and pEGCG could synergistically amplify oxidative stress and prolong ROS retention in cancer cells, leading to enhanced anticancer efficacy. This ROS cascade amplifier based on selective coordination bond and inherent pro-oxidation properties of natural ingredients could detect and elevate intracellular ROS signals, potentiating to move the tumor away from its homeostasis and make the tumor vulnerable. Compared to previously reported chemosynthetic pro-oxidants, the ROS self-sufficient system, fully composed of natural medicine, from this study provides a new insight in developing cancer oxidation therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".