Design and Encapsulation of Immunomodulators onto Gold Nanoparticles in Cancer Immunotherapy
Bibliographic record
Abstract
The aim of cancer immunotherapy is to reactivate autoimmune responses to combat cancer cells. To stimulate the immune system, immunomodulators, such as adjuvants, cytokines, vaccines, and checkpoint inhibitors, are extensively designed and studied. Immunomodulators have several drawbacks, such as drug instability, limited half-life, rapid drug clearance, and uncontrolled immune responses when used directly in cancer immunotherapy. Several strategies have been used to overcome these limitations. A simple and effective approach is the loading of immunomodulators onto gold-based nanoparticles (GNPs). As gold is highly biocompatible, GNPs can be administered intravenously, which aids in increasing cancer cell permeability and retention time. Various gold nanoplatforms, including nanospheres, nanoshells, nanorods, nanocages, and nanostars have been effectively used in cancer immunotherapy. Gold nanostars (GNS) are one of the most promising GNP platforms because of their unusual star-shaped geometry, which significantly increases light absorption and provides high photon-to-heat conversion efficiency due to the plasmonic effect. As a result, GNPs are a useful vehicle for delivering antigens and adjuvants that support the immune system in killing tumor cells by facilitating or activating cytotoxic T lymphocytes. This review represents recent progress in encapsulating immunomodulators into GNPs for utility in a cancer immunotherapeutic regimen.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".