Global transcriptomic characterization of T cells in individuals with chronic HIV-1 infection
Bibliographic record
Abstract
Abstract To obtain a comprehensive scenario of T cell profiles and synergistic immune responses, we performed single-cell RNA sequencing (scRNA-seq) on the peripheral T cells of 14 individuals with chronic human immunodeficiency virus 1 (HIV-1) infection, including nine treatment-naive (TP) and eight antiretroviral therapy (ART) participants (of whom three were paired with TP cases), and compared the results with four healthy donors (HD). Through analyzing the transcriptional profiles of CD4 + and CD8 + T cells, coupled with assembled T cell receptor sequences, we observed the significant loss of naive T cells, prolonged inflammation, and increased response to interferon-α in TP individuals, which could be partially restored by ART. Interestingly, we revealed that CD4 + and CD8 + Effector-GNLY clusters were expanded in TP cases, and persistently increased in ART individuals where they were typically correlated with poor immune restoration. This transcriptional dataset enables a deeper understanding of the pathogenesis of HIV-1 infection and is also a rich resource for developing novel immune targeted therapeutic strategies.
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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 | 0.001 | 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".