QUASI analysis of host immune responses to Gag polyproteins of human immunodeficiency virus type 1 by a systematic bioinformatics approachThis paper is one of a selection of papers published in this special issue entitled “Second International Symposium on Recent Advances in Basic, Clinical, and Social Medicine” and has undergone the Journal's usual peer review process.
Bibliographic record
Abstract
There is a consensus that Gag-specific cytotoxic T lymphocyte (CTL) response plays a key role in the immune control of human immunodeficiency virus type 1 (HIV-1) infection. In this study, we analyzed all currently available gag sequences in the Los Alamos HIV sequence database and identified positive selection (PS) sites likely restricted by the host immune responses. We found that between 23.4% and 47.4% of PS sites were shared by clades A, B, and C of Gag, indicating similar positive selection pressure on Gag in different subtypes of HIV-1. Furthermore, a significant correlation was observed between the combined CTL and antibody responses and PS sites. The Gag regions of free from PS contained 9 CTL epitopes restricted by 11 HLA class I alleles associated with disease progression to acquired immune deficiency syndrome (AIDS). These analyses provide information important for the identification of cross-clade epitopes and development of a global HIV-1 vaccine.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".