CD4:CD8 ratio as a frontier marker for clinical outcome, immune dysfunction and viral reservoir size in virologically suppressed HIV‐positive patients
Bibliographic record
Abstract
INTRODUCTION: Absolute CD4 T cell count and plasma viral load have been established as predictors of HIV disease progression, and CD4 T cell count is used as an indicator for initiation of antiretroviral therapy. Following long-term therapy, patients generally present with significant CD4 T cell recovery contrasting with persistently elevated CD8 T cell counts, which leads to a partial restoration of CD4:CD8 ratio. This review focuses on the relevance of the CD4:CD8 ratio on clinical outcomes, immune dysfunction and HIV reservoir size in long-term treated patients. METHOD: We conducted a comprehensive literature review of publications in English language using major electronic databases. Our search was focused on factors contributing to CD4:CD8 T cell ratio and clinical outcome in adult HIV-positive patients in the context of treated infection. DISCUSSION: Low CD4:CD8 ratio has been linked to ageing and acts as a predictor of mortality in the general population. This ratio may represent the combined effects of inflammation and immunological changes called "inflammaging." Although the mechanisms underlying partial correction of the CD4:CD8 ratio and persistently elevated CD8 T cell count in long-term treated patients remain poorly understood, it has been recently indicated that patients with optimal CD4 T cell recovery and low CD4:CD8 ratio still harbour increased immune activation, an immune senescent phenotype and have a higher risk of non-AIDS morbidity and mortality. This review reconsiders CD4:CD8 ratio in the light of advances in the understanding of immune dysfunction and examines its pathophysiological features and implications on clinical outcome and HIV reservoir size in long-term treated HIV-positive adults. CONCLUSION: The CD4:CD8 ratio can contribute to the immunological evaluation of treated patients in a long-term follow-up and may be applied for monitoring both immune dysfunction and viral reservoir size in immune-based clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".