Drilling dendritic cell activation: Engineering interfacial mechano-biochemical cues for enhanced immunotherapy
Bibliographic record
Abstract
A key challenge in immunotherapy is enhancing immune responses without introducing new molecular entities that trigger regulatory hurdles. While the size, shape, and composition of approved adjuvants have been optimized, their mechanical properties remain underexplored. Here, we repurpose approved aluminum-based adjuvants (alum) by engineering alum-stabilized Pickering emulsions (ASPEs) to synergize mechanical (PIEZO1) and biochemical (TLR4) cues. ASPEs, featuring interfacial alum with optimal rigidity, were heralded to promote an enlarged contact area with dendritic cells (DCs) during endocytosis, transmitting localized stress that activates PIEZO1-mediated calcium/mitogen-activated protein kinase (MAPK) signaling. This enhances antigen cross-presentation and Th1 immunity. Co-delivering a TLR4 agonist (monophosphoryl lipid A [MPLA]) further boosted immunogenicity in a varicella-zoster virus vaccine among aged mice, outperforming alum+MPLA (AS04). In antigen-pulsed DC therapy combined with PD-1 blockade, ASPE-M-treated DCs achieved a 2.11-fold greater tumor suppression compared with tumor lysate-M-based clinical approaches. These findings demonstrate how tuning the interfacial mechanics of approved materials can unlock mechano-immunotherapy with translational potential.
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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".