Supramolecular biomaterials for enhanced cancer immunotherapy
Bibliographic record
Abstract
Cancer immunotherapy has achieved promising clinical results. However, many limitations associated with current cancer immunotherapy still exist, including low response rates and severe adverse effects in patients. Engineering biomaterials for the delivery of immunotherapeutic reagents has been suggested to be an effective strategy to improve cancer immunotherapy. Among different biomaterials, supramolecular biomaterials with flexible and versatile structures and functions have exhibited unparalleled advantages in promoting cancer immunotherapy. In recent years, various supramolecular formulations have been extensively explored as immunotherapeutic delivery platforms due to their high cargo-loading capacity/feasibility, facile immunization function, and excellent biocompatibility, which make them possible candidates for modular and personalized cancer immunotherapy. These nanoarchitectures with unique topologies possess distinguishing advantages in cancer immunotherapy, incarnating a structure-property relationship. Based on extensive state-of-the-art research, this minireview highlights recent advances in supramolecular biomaterials for cancer immunotherapy and discusses the possible mechanisms underlying how supramolecular biomaterials promote the development of cancer immunotherapy together with their potential for clinical translation.
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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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".