Multivalent Carbohydrate Nanocomposites for Tumor Microenvironment Remodeling to Enhance Antitumor Immunity
Bibliographic record
Abstract
Current cancer immunotherapeutic strategies mainly focus on remodeling the tumor microenvironment (TME) to make it favorable for antitumor immunity. Increasing attention has been paid to developing innovative immunomodulatory adjuvants that can restore weakened antitumor immunity by conferring immunogenicity to inflamed tumor tissues. Here, a galactan-enriched nanocomposite (Gal-NC) is developed from native carbohydrate structures through an optimized enzymatic transformation for effective, stable, and biosafe innate immunomodulation. Gal-NC is characterized as a carbohydrate nanoadjuvant with a macrophage-targeting feature. It is composed of repeating galactan glycopatterns derived from heteropolysaccharide structures of plant origin. The galactan repeats of Gal-NC function as multivalent pattern-recognition sites for Toll-like receptor 4 (TLR4). Functionally, Gal-NC-mediated TLR activation induces the repolarization of tumor-associated macrophages (TAMs) toward immunostimulatory/tumoricidal M1-like phenotypes. Gal-NC increases the intratumoral population of cytotoxic T cells, the main effector cells of antitumor immunity, via re-educated TAMs. These TME alterations synergistically enhance the T-cell-mediated antitumor response induced by αPD-1 administration, suggesting that Gal-NC has potential value as an adjuvant for immune checkpoint blockade combination therapies. Thus, the Gal-NC model established herein suggests a glycoengineering strategy to design a carbohydrate-based nanocomposite for advanced cancer immunotherapies.
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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) | 0.000 | 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 | 0.002 |
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 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".