Comparative Proliferation Capacity of Gag-C-Specific Naive and Memory CD4+ and CD8+ T Lymphocytes in Rapid, Viremic Slow, and Slow Progressors During Human Immunodeficiency Virus Infection
Bibliographic record
Abstract
The exact cause of altered dynamics in T cells compartment during HIV infection remains elusive to date. In this longitudinal study, the proliferation frequency of different T cell subsets was investigated in untreated HIV-1-infected Indian individuals stratified as rapid (R), viremic slow (VS), slow (S) progressors, and healthy controls. Ten healthy and 20 treatment-naive HIV-1-infected individuals were enrolled. Expression of Ki67 nuclear antigen was examined on HIV-specific T cell subsets in peripheral blood lymphocytes. Upon stimulation with HIV-1 Gag-C peptide pools, effector memory (EM) CD4 T cells (R vs. S, EM CD4, p < 0.05) of R progressors proliferated significantly compared with those of S progressors at baseline. However, central memory (CM) CD8 T cell subsets proliferated significantly in VS and S progressors compared with those in R progressors, wherein highest proliferation frequency of EM CD8 T cells was observed. At follow-up visit, the proliferation frequency of naive CD8 T cells was significantly higher in R progressors than S progressors (R vs. S naive CD8, p < 0.05). The findings suggest altered dynamics of different CD4+ and CD8+ T cell subsets in R, VS, and S progressors. The increase in CM T cell proliferation in VS and S progressors could be attributed to slower progression of the HIV infection. Hence, treatment strategies must be focused on restoring the homeostatic balance to restore T cell functionality.
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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) | 0.001 | 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".