Potential applications of nanoparticles in cancer immunotherapy
Bibliographic record
Abstract
In recent years considerable progress has been made in the field of cancer immunotherapy whereby treatments that modulate the body's own immune system are used to combat cancer. This has the potential to not only elicit strong anti-cancer immune responses which can break pre-existing tolerance and help promote tumor regression, but could also induce immunological memory which may help prevent tumor recurrence. In order to ensure effective delivery of immunotherapeutic agents, such as vaccines, checkpoint inhibitors, chemotherapeutic agents and nucleic acids, a safe and effective delivery system is often required. One such approach is the use of multifunctional nanoparticles (NPs), such as liposomes, polymers, micelles, dendrimers, inorganic NPs, and hybrid NPs, which have the potential to combine the delivery of a diverse range of therapeutic immunomodulators thereby increasing the efficacy of tumor cell killing. This review focuses on recent progress in NP-mediated immunotherapy for the treatment of cancer.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".