In Vivo Stimulation of Therapeutic Antigen‐Specific T Cells in an Artificial Lymph Node Matrix
Bibliographic record
Abstract
T cells are critical mediators of antigen-specific immune responses and are common targets for immunotherapy. Biomaterial scaffolds have previously been used to stimulate antigen-presenting cells to elicit antigen-specific immune responses; however, structural and molecular features that directly stimulate and expand naïve, endogenous, tumor-specific T cells in vivo have not been defined. Here, an artificial lymph node (aLN) matrix is created, which consists of an extracellular matrix hydrogel conjugated with peptide-loaded-MHC complex (Signal 1), the co-stimulatory signal anti-CD28 (Signal 2), and a tethered IL-2 (Signal 3), that can bypass challenges faced by other approaches to activate T cells in situ such as vaccines. This dynamic immune-stimulating platform enables direct, in vivo antigen-specific CD8+ T cell stimulation, as well as recruitment and coordination of host immune cells, providing an immuno-stimulatory microenvironment for antigen-specific T cell activation and expansion. Co-injecting the aLN with naïve, wild-type CD8+ T cells results in robust activation and expansion of tumor-targeted T cells that kill target cells and slow tumor growth in several distal tumor models. The aLN platform induces potent in vivo antigen-specific CD8+ T cell stimulation without the need for ex vivo priming or expansion and enables in situ manipulation of antigen-specific responses for immunotherapies.
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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".