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A lamprey monoclonal VLR antibody against human CD5 (65.34)

2011· article· en· W187387968 on OpenAlexaff
Cuiling Yu, Götz R. A. Ehrhardt, Max D. Cooper

Bibliographic record

VenueThe Journal of Immunology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Genetics and Reproduction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMonoclonal antibodyAntigenMolecular biologyCD5BiologyAntibodyEpitopeclone (Java method)TransfectionPopulationCell cultureFlow cytometryImmunologyMedicineGeneticsGene

Abstract

fetched live from OpenAlex

Abstract The recently identified VLRB antibodies of the agnathan sea lamprey use highly variable leucine-rich repeats (LRRs) that assume a solenoid shape to bind antigens. Solved structures of monoclonal VLRB antibodies complexed to the H-trisaccharide or HEL show that antigens bind to variable residues in the beta-sheets located on the concave surface of specific VLRB antibodies. In this study we screened clones of a lamprey VLR cDNA expression library prepared from animals immunized with human CD4+ T cells to identify a monoclonal VLRB antibody (T32) that bound to human T lymphocytes, but not mouse T cells. Modulation of the expression levels of the TCR/CD3 complex by mouse antibodies did not affect VLR T32 binding. Staining of T cells, tonsilar B cells and malignant B-CLL cells with VLRB T32 and a mouse anti-CD5 mAb (clone UCHT2) demonstrated a diagonal co-staining pattern for each population of the test cells. Transfection of the CD5-negative OCI-Ly3 cell line with a human CD5 cDNA construct resulted in VLR T32 binding of transfected but not of untransfected cells. Staining of CD5+ cells by VLR T32 was not blocked by pre-incubation with the mouse UCHT2 anti-CD5 mAb, which suggests that they recognizes different CD5 epitopes. We conclude that lamprey monoclonal VLR antibodies offer useful alternatives to conventional antibodies for biomedical purposes.

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 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 score0.111
Threshold uncertainty score0.298

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)0.0000.000

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 head0.020
GPT teacher head0.263
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 teacher head, 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

Citations0
Published2011
Admission routes1
Has abstractyes

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