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Record W4390646028 · doi:10.1038/s42003-023-05719-9

A biphenotypic lymphocyte subset displays both T- and B-cell functionalities

2024· article· en· W4390646028 on OpenAlexaff
Y Zhang, Cuiyuan Guo, Yigong Zhou, Wenhong Zhang, Zhaoqin Zhu, Wanhai Wang, Yanmin Wan

Bibliographic record

VenueCommunications Biology · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicT-cell and B-cell Immunology
Canadian institutionsMcGill University
FundersScience and Technology Commission of Shanghai MunicipalityNational Natural Science Foundation of China
KeywordsCD19ImmunologyImmune systemBiologyLymphocyteBone marrowImmunophenotypingB cellSpleenT cellAntigenPhenotypeAntibodyGeneGenetics

Abstract

fetched live from OpenAlex

Abstract T cell/B cell mixed phenotypic lymphocytes have been observed in different disease contexts, yet their presence and function in physiological conditions remain elusive. Here, we provide evidence for the existence of a lymphocyte subset endogenously expressing both T- and B-cell lineage markers in mice. The majority of these T/B phenotypic lymphocytes (CD3 + CD19 + ) show an origin of pro/pre B cells and distribute widely in mouse bone marrow, lymph nodes, spleen, and peripheral blood. Functional assays show that these biphenotypic lymphocytes can be activated through stimulating TCR or BCR signaling pathways. Moreover, we show that these cells actively participate both the humoral and cellular immune responses elicited by vaccination. Compared to conventional T cells, these biphenotypic lymphocytes can secrete a higher level of IL-2 but a lower level of TNF-α upon antigen specific stimulation. An equivalent lymphocyte subset is found in freshly isolated human PBMCs and exhibits similar functionality, albeit at a lower frequency than in mice.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0020.001

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 head0.256
Teacher spread0.233 · 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 designBench or experimental
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

Citations16
Published2024
Admission routes1
Has abstractyes

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