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Record W2419591865 · doi:10.1385/1-59259-916-8:121

Identification of Clonotypic IgH VDJ Sequences in Multiple Myeloma

2005· article· en· W2419591865 on OpenAlexaff
Brian J. Taylor, Jitra Kriangkum, Erin Strachan, Juanita Wizniak, Linda M. Pilarski

Bibliographic record

VenueHumana Press eBooks · 2005
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiologyImmunoglobulin heavy chainPrimer (cosmetics)clone (Java method)Molecular biologyPolymerase chain reactionAntibodyGeneticsGeneChemistry

Abstract

fetched live from OpenAlex

In multiple myeloma (MM) the rearranged immunoglobulin heavy chain (IgH) variable, diversity, and joining (VDJ) DNA sequence of malignant plasma cells (PCs) serves as a marker for cells in the MM clone. This clonotypic sequence can be isolated from MM PCs by reverse transcriptase polymerase chain reaction (RT-PCR) with consensus primers that amplify the rearranged IgH repertoire. This chapter focuses on the key steps in determining patient-specific clonotypic sequences, including bulk RT-PCR using purified bone marrow mononuclear cell (BMMC) RNA, single-cell RT-PCR using RNA from PCs sorted by flow cytometry, IgH sequence alignments using IMGT or V BASE, and patient-specific primer design. In a test panel of several MM patient BMMCs, primers specific for the proposed sequence must amplify IgH from only the original patient. Furthermore, the proposed IgH sequence is not confirmed as clonotypic until these primers generate positive amplifications in the majority of single PCs from the original patient. This two-part test ensures that the proposed IgH sequence satisfies the definition of the clonotypic sequence as the most frequent, unique IgH sequence in an MM patient PC sample. With this patient-specific MM marker, a better understanding of transformed PCs and their B-lineage predecessors can be developed.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.416

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 head0.084
GPT teacher head0.337
Teacher spread0.253 · 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 designOther design
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

Citations11
Published2005
Admission routes1
Has abstractyes

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