Human Leukocyte Antigen (HLA) Class I Down-Regulation by Human Immunodeficiency Virus Type 1 Negative Factor (HIV-1 Nef): What Might We Learn From Natural Sequence Variants?
Bibliographic record
Abstract
HIV-1 causes a chronic infection in humans that is characterized by high plasma viremia, progressive loss of CD4+ T lymphocytes, and severe immunodeficiency resulting in opportunistic disease and AIDS. Viral persistence is mediated in part by the ability of the Nef protein to down-regulate HLA molecules on the infected cell surface, thereby allowing HIV-1 to evade recognition by antiviral CD8+ T lymphocytes. Extensive research has been conducted on Nef to determine protein domains that are required for its immune evasion activities and to identify critical cellular co-factors, and our mechanistic understanding of this process is becoming more complete. This review highlights our current knowledge of Nef-mediated HLA class I down-regulation and places this work in the context of naturally occurring sequence variation in this protein. We argue that efforts to fully understand the critical role of Nef for HIV-1 pathogenesis will require greater analysis of patient-derived sequences to elucidate subtle differences in immune evasion activity that may alter clinical outcome.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".