IL-2–Engineered nano-APC Effectively Activates Viral Antigen-Mediated T Cell Responses from Chronic Hepatitis B Virus-Infected Patients
Bibliographic record
Abstract
Impaired function of virus-specific T cells resulting from virus persistence is one of the major mechanisms underlying the development of chronic hepatitis B viral infection. Previously, we found that IL-2 can restore the effector function of T cells rendered tolerant by Ag persistence. However, systemic administration of IL-2 induces organ pathology and expansion of T regulatory cells. In this study, we show that nano-APC with engineered HLA alleles and IL-2 deliver peptide-MHC complexes, costimulatory molecules, and IL-2 to Ag-responding T cells, resulting in enhanced expression of CD25 and activation of TCR signaling pathways, while suppressing PD-1 expression on viral-responding CD8 T cells from chronic hepatitis B virus patients. The enhanced activation of CD4 and CD8 T cells induced by IL-2-nano-APC was Ag dependent and IL-2-nano-APC did not affect T regulatory cells. At a size of 500 nm, the nano-APC effectively induce immune synapse formation on Ag-specific T cells and accumulate as free particles in the lymphoid organs. These attributes of IL-2-nano-APC or other bioadjuvant-engineered nano-APC have profound implications for their use as a therapeutic strategy in the treatment of chronic hepatitis B virus infection or other chronic viral diseases.
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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 | 0.001 |
| 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".