Advancing Cancer Immunotherapies with Nanotechnology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it