MétaCan
Menu
Back to cohort
Record W2794511584 · doi:10.6026/97320630014048

Hypothetical endogenous SIV-like antigens in Mauritian cynomolgus macaques

2018· article· en· W2794511584 on OpenAlexafffund
Hongzhao Li, Lin Li, Lewis R. Liu, Robert W. Omange, Nikki Toledo, Mohammad Abul Kashem, Hai Yan, Binhua Liang, Francis A. Plummer, Ma Luo

Bibliographic record

VenueBioinformation · 2018
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsPublic Health Agency of CanadaUniversity of Manitoba
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsSimian immunodeficiency virusVirologyAntigenAntibodyBiologyEndogenyVaccinationMacaqueImmunologyRhesus macaqueHIV vaccineLentivirusVirusVaccine trialViral disease

Abstract

fetched live from OpenAlex

Simian immunodeficiency virus (SIV) infection of Mauritian cynomolgus macaques (MCMs) is an increasingly important nonhuman primate model for HIV vaccine research. We previously reported that in MCMs anti-SIV antibodies can be naturally developed without exogenous infection or vaccination, and that a vaccine targeting SIV protease cleavage sites (PCS) can cross-induce antibodies to non-PCS SIV antigens. We speculate that this is potentially caused by the existence of endogenous SIV-like antigens. External stimuli (such as environmental factors and vaccination) may induce expression of endogenous SIV-like antigens to elicit these antibodies. Database and mass spectrometry analyses were conducted to search for such antigens. We identified endogenous SIV-like DNA sequences in cynomolgus macaque genome and non-PCS peptide homologous to SIV Env protein in PBMCs of a PCS-vaccinated monkey. Our preliminary insights suggest that endogenous SIV-like antigens may be one of the possible reasons for the natural and cross-inducible SIV antibodies in MCMs.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.027
GPT teacher head0.256
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueBioinformationSame topicHIV Research and TreatmentFrench-language works237,207