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Record W3081226729 · doi:10.1371/journal.pone.0238234

Racial differences in α4β7 expression on CD4+ T cells of HIV-negative men and women who inject drugs

2020· article· en· W3081226729 on OpenAlexaff
Alyssa R. Martin, Aida Sivro, Zoë Packman, Eshan U. Patel, Lívia R. Góes, Lyle R. McKinnon, Jacquie Astemborski, Gregory D. Kirk, Shruti H. Mehta, Claudia Cicala, James Arthos, Andrew D. Redd, Thomas C. Quinn

Bibliographic record

VenuePLoS ONE · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of Manitoba
FundersNHLBI Division of Intramural ResearchJohns Hopkins UniversityNational Institute of Allergy and Infectious DiseasesNational Institute on Drug AbuseNational Institutes of HealthGilead Sciences
KeywordsHuman immunodeficiency virus (HIV)MedicineCohortFlow cytometryRacial differencesInternal medicineDemographyImmunologyEthnic group

Abstract

fetched live from OpenAlex

INTRODUCTION: We performed a cross-sectional study of HIV-uninfected men and women who inject drugs from the ALIVE cohort to examine if black men and women who inject drugs have higher levels of CD4+ T cells expressing the integrin heterodimer α4β7 compared to white men and women. MATERIALS AND METHODS: Flow cytometry was used to examine expression of α4β7 and other markers associated with different functional CD4+ T cell subsets in both men and women who inject drugs. RESULTS: Higher levels of α4β7, CCR5, and CCR6 were observed on CD4+ T cells from black participants compared with white participants. In a multivariable model, α4β7 expression differed by race, but not sex, age, or other factors. DISCUSSION: Black men and women express higher percentages of α4β7 expressing CD4+ T cells, which may play a role in HIV disease.

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 score0.003
Threshold uncertainty score0.011

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)0.0030.000

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 head0.031
GPT teacher head0.231
Teacher spread0.200 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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