Advancing Cancer Immunotherapies with Nanotechnology
Bibliographic record
Abstract
Abstract Cancer immunotherapies can elicit long term, durable responses in only a fraction of patients. As such, there is a need to increase the number of patients who can benefit from cancer immunotherapies. By virtue of their versatility and nanoscale, nanoparticles have unique properties that can be exploited to enhance the efficacy of cancer immunotherapies. This review first outlines key concepts in nanotechnology and immunotherapy. Then, it highlights nanotechnology‐mediated improvements to the efficacy of immune checkpoint inhibitors, cancer vaccines, and adoptive cellular therapies. Next, the insights derived from nanoparticle‐mediated imaging of immune cells in both preclinical and clinical studies are reviewed. Afterwards, the roles of nanotechnology in combination therapies to augment antitumoral immunity are summarized. Finally, the challenges facing this emerging field combining nanotechnology with immunotherapies are discussed. Given the exciting, novel approaches that can arise from nanotechnology, there is great potential for nanotechnology to advance immunotherapies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".