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Record W4361001720 · doi:10.1128/jvi.01670-22

HIV-1 DNA and Immune Activation Levels Differ for Long-Lived T-Cells in Lymph Nodes, Compared with Peripheral Blood, during Antiretroviral Therapy

2023· article· en· W4361001720 on OpenAlexaff
Christina Mallarino-Haeger, María Pino, Elise G. Viox, Amélie Pagliuzza, Colin T. King, Kevin Nguyen, Justin Harper, Sol Aldrete, Barbara Cervasi, Keith A. Delman, Michael Lowe, Nicolas Chomont, Vincent C. Marconi, Mirko Paiardini

Bibliographic record

VenueJournal of Virology · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersNational Institute of Allergy and Infectious DiseasesEli Lilly and CompanyEmory UniversityCenter for AIDS Research, Emory UniversityNational Institutes of HealthNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteGilead Sciences
KeywordsTIGITBiologyCD8Memory T cellImmunologyCD38Immune systemCytotoxic T cellT cellLymphCell biologyMedicinePathologyIn vitroStem cellGeneticsCD34

Abstract

fetched live from OpenAlex

This study provides new insights into the contributions of different CD4 + and CD8 + T-cell subsets to the anatomic differences between LN and blood in individuals with HIV who have optimal versus suboptimal CD4 + T-cell recovery. To our knowledge, this is the first study comparing paired LN and blood CD4 + and CD8 + T-cell differentiation subsets, as well as those subsets in immunological responders versus immunological suboptimal responders.

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 score0.002
Threshold uncertainty score0.006

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.267
Teacher spread0.243 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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