Bioresponsive and immunotherapeutic nanomaterials to remodel tumor microenvironment for enhanced immune checkpoint blockade
Bibliographic record
Abstract
Immune checkpoint blockade (ICB) therapy is a revolutionary approach to treat cancers, but still have limited clinical applications. Accumulating evidence pinpoints the immunosuppressive characteristics of the tumor microenvironment (TME) as one major obstacle. The TME, characterized by acidity, hypoxia and elevated ROS levels, exerts its detrimental effects on infiltrating anti-tumor immune cells. Here, we developed a TME-responsive and immunotherapeutic catalase-loaded calcium carbonate nanoparticles (termed as CAT@CaCO3 NPs) as the simple yet versatile multi-modulator for TME remodeling. CaCO3 NPs can consume protons in the acidic TME to normalize the TME pH. CAT catalyzed the decomposition of ROS and thus generated O2. The released Ca2+ led to Ca2+ overload in the tumor cells which then triggered the release of damage-associated molecular patterns (DAMP) signals to initiate anti-tumor immune responses, including tumor antigen presentation by dendritic cells. Meanwhile, CAT@CaCO3 NPs-induced immunosupportive TME also promoted the polarization of the M2 tumor-associated macrophages to the M1 phenotype, further enhancing tumor antigen presentation. Consequently, T cell-mediated anti-tumor responses were activated, the efficacy of which was further boosted by aPD-1 immune checkpoint blockade. Our study demonstrated that local treatment of CAT@CaCO3 NPs and aPD-1 combination can effectively evoke local and systemic anti-tumor immune responses, inhibiting the growth of treated tumors and distant diseases.
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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) | 0.000 | 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) | 0.001 | 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 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".