Elevated CD54 Expression Renders CD4+ T Cells Susceptible to Natural Killer Cell-Mediated Killing
Bibliographic record
Abstract
BACKGROUND: Natural killer (NK) cells are an important type of effector cell in the innate immune response, and also have a role in regulation of the adaptive immune response. Several studies have indicated that NK cells may influence CD4+ T cells during HIV infection. METHODS: In total, 51 HIV-infected individuals and 15 healthy controls participated in this study. We performed the flow cytometry assays and real-time PCR for the phenotypic analysis and the functional assays of NK cell-mediated deletion of CD4+ T cells, phosphorylation of nuclear factor-κB (NF-κB/p65) and the intervention of metformin. RESULTS: Here we detected high CD54 expression on CD4+ T cells in HIV-infected individuals, and demonstrate that upregulated CD54 is associated with disease progression in individuals infected with HIV. We also show that CD54 expression leads to the deletion of CD4+ T cells by NK cells in vitro, and that this is modulated by NF-κB/p65 signaling. Further, we demonstrate that metformin can suppress CD54 expression on CD4+ T cells by inhibiting NF-κB/p65 phosphorylation. CONCLUSIONS: Our data suggest that further studies to evaluate the potential role of metformin as adjunctive therapy to reconstitute immune function in HIV-infected individuals are warranted.
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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) | 0.003 | 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".