Nanoparticle-based modulation of CD4+ T cell effector and helper functions enhances adoptive immunotherapy
Bibliographic record
Abstract
Abstract Helper (CD4 + ) T cells perform direct therapeutic functions and augment responses of cells such as cytotoxic (CD8 + ) T cells against a wide variety of diseases and pathogens. Nevertheless, inefficient synthetic technologies for expansion of antigen-specific CD4 + T cells hinders consistency and scalability of CD4 + T cell-based therapies, and complicates mechanistic studies. Here we describe a nanoparticle platform for ex vivo CD4 + T cell culture that mimics antigen presenting cells (APC) through display of major histocompatibility class II (MHC II) molecules. When combined with soluble co-stimulation signals, MHC II artificial APCs (aAPCs) expand cognate murine CD4 + T cells, including rare endogenous subsets, to induce potent effector functions in vitro and in vivo. Moreover, MHC II aAPCs provide help signals that enhance antitumor function of aAPC-activated CD8 + T cells in a mouse tumor model. Lastly, human leukocyte antigen class II-based aAPCs expand rare subsets of functional, antigen-specific human CD4 + T cells. Overall, MHC II aAPCs provide a promising approach for harnessing targeted CD4 + T cell responses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".