CD127 Expression, Exhaustion Status and Antigen Specific Proliferation Predict Sustained Virologic Response to IFN in HCV/HIV Co-Infected Individuals
Bibliographic record
Abstract
Hepatitis C virus (HCV) infection is a major cause of morbidity and mortality in the HIV co-infected population. Interferon-alpha (IFN-α) remains a major component of anti-HCV therapy despite its deleterious effects on the immune system. Furthermore, IFN-α was recently shown to diminish the size of the latent HIV reservoir. The objectives of this study were to monitor the impact of IFN-α on T cell phenotype and proliferation of HIV and HCV-specific T cells during IFN therapy, and to identify immune markers that can predict the response to IFN in HICV/HIV co-infected patients. We performed longitudinal analyses of T cell numbers, phenotype and function in co-infected patients undergoing IFN-α therapy with different outcomes including IFN-α non-responders (NR) (n = 9) and patients who achieved sustained virologic response (SVR) (n = 19). We examined the expression of activation (CD38, HLA-DR), functional (CD127) and exhaustion markers (PD1, Tim-3, CD160 and CD244) on total CD4 and CD8 T cells before, during and after therapy. In addition, we examined the HIV- and HCV-specific proliferative responses against HIV-p24 and HCV-NS3 proteins. Frequencies of CD127+ CD4 T cells were higher in SVR than in NR patients at baseline. An increase in CD127 expression on CD8 T cells was observed after IFN-α therapy in all patients. In addition, CD8 T cells from NR patients expressed a higher exhaustion status at baseline. Finally, SVR patients exhibited higher proliferative response against both HIV and HCV antigens at baseline. Altogether, SVR correlated with higher expression of CD127, lower T cell exhaustion status and better HIV and HCV proliferative responses at baseline. Such factors might be used as non-invasive methods to predict the success of IFN-based therapies in co-infected individuals.
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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 | 0.001 |
| 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".