Human Leukocyte Antigen (HLA) and Immune Regulation: How Do Classical and Non-Classical HLA Alleles Modulate Immune Response to Human Immunodeficiency Virus and Hepatitis C Virus Infections?
Bibliographic record
Abstract
The genetic factors associated with susceptibility or resistance to viral infections are likely to involve a sophisticated array of immune-response. These genetic elements may modulate other biological factors that account for significant influence on the gene expression and/or protein function in the host. Among them, the role of the Major Histocompatibility Complex (MHC) in viral pathogenesis in particular HIV and HCV, is very well documented. We recently added a novel insight into the field by identifying the molecular mechanism associated with the protective role of HLA-B27/B57 CD8+ T cells in the context of HIV-1 infection and why these alleles act as a double-edged sword protecting against viral infections but predisposing the host to autoimmune diseases. The focus of this review will be reexamining the role of classical and non-classical HLA-alleles including class Ia (HLA-A, -B, -C), class Ib (HLA-E, -F, -G, -H) and class II (HLA-DR, -DQ, -DM, and -DP) in immune regulation and viral pathogenesis (e.g. HIV and HCV). To our knowledge this is the very first review of its kind to comprehensively analyze the role of these molecules in immune regulation associated with chronic viral infections.
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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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".