Artificial antigen-presenting cell system reveals CD28’s role in modulating T cell functions during human immunodeficiency virus infection
Bibliographic record
Abstract
T cell immune dysfunction is a prominent feature of chronic HIV infection. To evaluate non-specific dysfunction, a method involving both generic activation and T cell receptor (TCR) stimulation is necessary. We created a tunable artificial antigen-presenting cell (aAPC) system. This system consists of lipid bilayers on cytometry-compatible silica microbeads (5 μm). When only anti-CD3 is incorporated, T cell activation is limited. Introducing anti-CD28 agonists significantly elevates the cytokine expression and upregulation of activation-induced markers. CD28 co-stimulation modulates the response profile, preferentially promoting IL-2 expression relative to other cytokines. aAPCs-stimulated CD4 + and CD8 + T cells from untreated HIV-infected individuals exhibit altered effector functions and diminished CD28 dependence. These functions are skewed toward TNFα, IFNγ and CD107a, with reduced IL-2. Antiretroviral therapy partially normalizes this distorted profile in CD4 + T cells, but not in CD8 + T cells. Our findings show T cell intrinsic biases that may contribute to persistent systemic T cell dysfunction associated with HIV pathogenesis.
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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) | 0.000 | 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".