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Identification of Dominant Optimal HLA-B60- and HLA-B61-Restricted Cytotoxic T-Lymphocyte (CTL) Epitopes: Rapid Characterization of CTL Responses by Enzyme-Linked Immunospot Assay

2000· article· en· W2121372003 on OpenAlexaff
Marcus Altfeld, Alicja Trocha, Robert L. Eldridge, Eric Rosenberg, Mary N. Phillips, Marylyn M. Addo, Rafick‐Pierre Sékaly, Spyros A. Kalams, Sandra A. Burchett, Kenneth McIntosh, Bruce D. Walker, Philip Goulder

Bibliographic record

VenueJournal of Virology · 2000
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsMontreal Clinical Research Institute
FundersNational Institute of Allergy and Infectious Diseases
KeywordsCTL*ELISPOTEpitopeBiologyHuman leukocyte antigenCytotoxic T cellImmunologyVirologyImmune systemAntigenT cellCD8GeneticsIn vitro

Abstract

fetched live from OpenAlex

Human immunodeficiency virus type 1 (HIV-1)-specific cytotoxic T-lymphocyte (CTL) responses play a major role in the antiviral immune response, but the relative contribution of CTL responses restricted by different HLA class I molecules is less well defined. HLA-B60 or the related allele B61 is expressed in 10 to 20% of Caucasoid populations and is even more highly prevalent in Asian populations, but yet no CTL epitopes restricted by these alleles have been defined. Here we report the definition of five novel HLA-B60-restricted HIV-1-specific CTL epitopes, using peripheral blood mononuclear cells in enzyme-linked immunospot (Elispot) assays and using CTL clones and lines in cytolytic assays. The dominant HLA-B60-restricted epitope, Nef peptide KEKGGLEGL, was targeted by all eight subjects with B60 and also by both subjects with B61 studied. This study additionally establishes the utility of the Elispot assay as a more rapid and efficient method of defining novel CTL epitopes. This approach will help to define new CTL epitopes that may play an important role in the immune control of HIV-1.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.884

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.249
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations99
Published2000
Admission routes1
Has abstractyes

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