Harnessing Autophagy to Overcome Antigen-Specific T-Cell Dysfunction: Implication for People Living with HIV-1
Bibliographic record
Abstract
Like other chronic viral infections, HIV-1 persistence inhibits the development of antigen-specific memory T-cells, resulting in the exhaustion of the immune response and chronic inflammation. Autophagy is a major lysosome-dependent mechanism of intracellular large-target degradation such as lipid and protein aggregates, damaged organelles, and intracellular pathogens. Although it is known that autophagy may target HIV-1 for elimination, knowledge of its function as a metabolic contributor in such viral infection is only in its infancy. Recent data show that elite controllers (EC), who are HIV-1-infected subjects with natural and long-term antigen (Ag)-specific T-cell protection against the virus, are characterized by distinct metabolic autophagy-dependent features in their T-cells compared to other people living with HIV-1 (PLWH). Despite durable viral control with antiretroviral therapy (ART), HIV-1-specific immune dysfunction does not normalize in non-controller PLWH. Therefore, the hypothesis of inducing autophagy to strengthen their Ag-specific T-cell immunity against HIV-1 starts to be an enticing concept. The aim of this review is to critically analyze promises and potential limitations of pharmacological and dietary interventions to activate autophagy in an attempt to rescue Ag-specific T-cell protection among PLWH.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".