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Record W2127549063 · doi:10.1128/jvi.05418-11

Identification of Human Endogenous Retrovirus-Specific T Cell Responses in Vertically HIV-1-Infected Subjects

2011· article· en· W2127549063 on OpenAlexaff
Ravi Tandon, Devi SenGupta, Lishomwa C. Ndhlovu, Raphaella G. S. Vieira, R. Brad Jones, Vanessa A. York, Vinícius Vieira, Elizabeth R. Sharp, Andrew Wiznia, Mario Ostrowski, Michael Rosenberg, Douglas F. Nixon

Bibliographic record

VenueJournal of Virology · 2011
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of Toronto
FundersNational Center for Research ResourcesFogarty International CenterUniversity of California, San FranciscoCenter for AIDS Research, University of WashingtonRagon Institute of MGH, MIT and HarvardNational Institutes of HealthNational Institute of Allergy and Infectious DiseasesPfizer
KeywordsBiologyVirologyRetrovirusEndogenyViral replicationViral loadEndogenous retrovirusT cellImmunologyImmunityCellLentivirusVirusViral diseaseImmune systemGeneticsGeneGenome

Abstract

fetched live from OpenAlex

Human endogenous retrovirus (HERV)-specific T cell responses in HIV-1-infected adults have been reported. Whether HERV-specific immunity exists in vertically HIV-1-infected children is unknown. We performed a cross-sectional analysis of HERV-specific T cell responses in 42 vertically HIV-1-infected children. HERV (-H, -K, and -L family)-specific T cell responses were identified in 26 of 42 subjects, with the greatest magnitude observed for the responses to HERV-L. These HERV-specific T cell responses were inversely correlated with the HIV-1 plasma viral load and positively correlated with CD4(+) T cell counts. These data indicate that HERV-specific T cells may participate in controlling HIV-1 replication and that certain highly conserved HERV-derived proteins may serve as promising therapeutic vaccine targets in HIV-1-infected children.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.045
GPT teacher head0.269
Teacher spread0.224 · 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

Citations36
Published2011
Admission routes1
Has abstractyes

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