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Record W2057117122 · doi:10.1186/1742-4690-9-s2-p102

Regulatory B cells are induced in untreated HIV-1 infection and suppress HIV-1 specific T cell responses

2012· article· en· W2057117122 on OpenAlexaff
Jun Liu, Wenbin Zhan, Chaelin Kim, E Lee, Jinghong Cao, Alexander Gregor, F Yue, Sanja Huibner, Sonya A. MacParland, Kiera Clayton, J. Schwartz, Haihan Song, E Bento, Colin Kovacs, Rupert Kaul, Mario Ostrowski

Bibliographic record

VenueRetrovirology · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsMaple Leaf Medical ClinicUniversity of Toronto
Fundersnot available
KeywordsRegulatory B cellsImmunologyInterleukin 10Flow cytometryCytokineMedicineImmune systemCD19Ex vivoPeripheral blood mononuclear cellBiologyIn vivoIn vitro

Abstract

fetched live from OpenAlex

Regulatory B cells (Breg), the B cells producing interleukin 10 (IL-10), have been identified in mice and humans. Mouse Breg can suppress innate and T cell responses and are implicated in pathogenesis of some autoimmune diseases and immune evasion of some pathogens. However, the role of Breg in humans is less clear. PBMC and gut biopsy samples were obtained from healthy donors and HIV infected individuals. Flow cytometry and Luminex were used to quantify cytokine production. Flow cytometry were used to analyze Breg's phenotype. Breg were elevated in both peripheral blood and gut tissue of untreated HIV-1 infected individuals and the elevation correlated with viral load in early HIV-1 infection. Breg from HIV-1 infected individuals were CD19 TIM-1 .Anti-retroviral therapy could reduce elevated Breg frequency. Treatment of B cells from healthy donors with microbial translocation products could differentiate them toward a Breg phenotype. Ex vivo Bregs from HIV-1 infected individuals suppressed cytokine production /degranulation of HIV-1 specific T cells that was in part IL-10 dependent. Our findings show that Bregs are induced early in HIV-1 infection, which may play a role in inhibiting effective HIV-1-specific T cell responses.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.003

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 head0.018
GPT teacher head0.240
Teacher spread0.223 · 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.

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

Citations0
Published2012
Admission routes1
Has abstractyes

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