Nanoparticles in tumor microenvironment remodeling and cancer immunotherapy
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
Cancer immunotherapy and vaccine development have significantly improved the fight against cancers. Despite these advancements, challenges remain, particularly in the clinical delivery of immunomodulatory compounds. The tumor microenvironment (TME), comprising macrophages, fibroblasts, and immune cells, plays a crucial role in immune response modulation. Nanoparticles, engineered to reshape the TME, have shown promising results in enhancing immunotherapy by facilitating targeted delivery and immune modulation. These nanoparticles can suppress fibroblast activation, promote M1 macrophage polarization, aid dendritic cell maturation, and encourage T cell infiltration. Biomimetic nanoparticles further enhance immunotherapy by increasing the internalization of immunomodulatory agents in immune cells such as dendritic cells. Moreover, exosomes, whether naturally secreted by cells in the body or bioengineered, have been explored to regulate the TME and immune-related cells to affect cancer immunotherapy. Stimuli-responsive nanocarriers, activated by pH, redox, and light conditions, exhibit the potential to accelerate immunotherapy. The co-application of nanoparticles with immune checkpoint inhibitors is an emerging strategy to boost anti-tumor immunity. With their ability to induce long-term immunity, nanoarchitectures are promising structures in vaccine development. This review underscores the critical role of nanoparticles in overcoming current challenges and driving the advancement of cancer immunotherapy and TME modification.
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