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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".