Nanomedicines modulate the tumor immune microenvironment for cancer therapy
Bibliographic record
Abstract
INTRODUCTION: In recent years, the evolution of immunotherapy as a means to trigger a robust antitumor immune response has revolutionized cancer treatment. Despite its potential, the effectiveness of cancer immunotherapy is hindered by low response rates and significant systemic side effects. Nanotechnology emerges as a promising frontier in shaping the future of cancer immunotherapy. AREAS COVERED: This review elucidates the pivotal role of nanomedicine in reshaping the immune tumor microenvironment and explores innovative strategies pursued by diverse research groups to enhance the therapeutic efficacy of cancer immunotherapy. It discusses the hurdles encountered in cancer immunotherapy and the application of nanomedicine for small molecule immune modulators and nucleic acid therapeutics. It also highlights the advancements in DNA and mRNA vaccines facilitated by nanotechnology and outlines future trajectories in this evolving field. EXPERT OPINION: Collectively, the integration of nanomedicine into cancer immunotherapy stands as a promising avenue to tackle the intricacies of the immune tumor microenvironment. Innovations such as immune checkpoint inhibitors and cancer vaccines have shown promise. Future developments will likely optimize nanoparticle design through artificial intelligence and create biocompatible, multifunctional nanoparticles, promising more effective, personalized, and durable cancer treatments, potentially transforming the field in the foreseeable future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".