Biochemical reprogramming of tumors for active modulation of receptor-mediated nanomaterial delivery
Bibliographic record
Abstract
Here we report that reactive oxygen species (ROS) can reprogram cancer cells to increase the expression of specific receptors and modulate the delivery of nanomaterials. Gold and γ-polyglutamic acid (γ-PGA) hybrid nanoparticles (PGANP) were prepared via a facile single-step process. Gold nanoclusters in PGANP were dispersed within the tangled γ-PGA matrix of the nanoparticles. The condensed assembly of gold nanoclusters in γ-PGA matrix enabled the interparticle plasmon coupling effect, which lacks in single gold nanoparticles. Compared with gold nanoparticles of the similar sizes, PGANP showed significantly higher absorbance at near infrared (NIR) wavelength and light-to-heat converting ratios, resulting in greater temperature increase upon NIR light irradiation. Pretreatment of HeLa cancer cells with methylene blue (MB) generated reactive oxygen species. The ROS reprogrammed the cancer cells to express higher cell membrane levels of gamma glutamyl transferase (GGT), which is known to bind to γ-PGA of PGANP. MB pretreatment significantly enhanced delivery of PGANP to cancer cells. Cancer cells internalized PGANP to a greater extent and, were highly susceptible to irradiation with NIR light, which reduced cell viability to near zero. In vivo, MB pretreatment of HeLa xenograft mice increased the expression of GGT in tumor tissues. In mice pretreated with MB and exposed to NIR irradiation, PGANP treatment resulted in complete tumor ablation. The strategy of actively reprogramming tumor membrane levels of target receptors could be widely applied to overcome the heterogeneity of cancer cells. Although we used interparticle plasmon coupling effect-based PGANP for proving the concept of receptor-modulated delivery, this strategy could be broadly applicable to the active modulation of the receptor-mediated delivery of anticancer nanomaterials.
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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".