Injectable Polymeric Cytokine-Binding Nanowires Are Effective Tissue-Specific Immunomodulators
Bibliographic record
Abstract
Injectable nanomaterials that interact with the host immune system without surgical intervention present spatially anchored complements to cell transplantation and could offer improved pharmacokinetics compared to systemic cytokine therapy. Here we demonstrate fabrication of high aspect ratio polycaprolactone nanowires coupled with cytokine-binding antibodies that assemble into porous matrices when injected into the subcutaneous space. These structures are fabricated using a nanotemplating technique that allows for tunability of particle dimensions and utilize a straightforward maleimide conjugation chemistry to allow site-specific coupling to proteins. Nanowires are well tolerated in vivo and incite minimal inflammatory infiltrate. Nanowires conjugated with antibodies were designed to capture and potentiate endogenous interleukin-2 (IL-2), an important leukocyte activating cytokine. Together these nanowire-antibody matrices were capable of localizing endogenous IL-2 in the skin and activated targeted specific natural killer and T cell subsets, demonstrating both tissue- and cell-specific immune activation. These self-assembling nanowire matrices show promise as scaffolds to present engineered, local receptor-ligand interactions for cytokine-mediated disease.
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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".