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Record W1809364733 · doi:10.1002/0471142956.cy0927s46

Whole Blood Processing for Measurement of Signaling Proteins by Flow Cytometry

2008· article· en· W1809364733 on OpenAlexaff
Sue Chow, David W. Hedley, T. Vincent Shankey

Bibliographic record

VenueCurrent Protocols in Cytometry · 2008
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsPrincess Margaret Cancer CentreOntario Institute for Cancer Research
Fundersnot available
KeywordsFlow cytometrySignal transductionCell biologyPhosphorylationBiologyHaematopoiesisPhenotypeImmune systemSignaling proteinsCytometryComputational biologyImmunologyStem cellGeneticsGene

Abstract

fetched live from OpenAlex

Signal transduction pathways link external stimuli with cellular responses, which normally regulate cell proliferation, death, and differentiation. The study of signal transduction was revolutionized through the development of phospho-specific antibodies that recognize proteins only when they are phosphorylated at specific sites. As discussed by Nolan and co-workers (UNIT ), one of the unique features of flow cytometry is its ability to perform correlated measurements of multiple phosphorylation states at the single cell level. This provides insight into the complexity of signaling networks that is not obtained by standard biochemical techniques. Furthermore, in combination with other phenotypic markers, flow cytometry can measure alterations in signaling pathways in subpopulations of cells. This clearly has wide potential for studying disorders of the hematopoietic and immune systems.

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)0.002
metaresearch head score (Gemma)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.029
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0290.019

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 head0.117
GPT teacher head0.396
Teacher spread0.278 · 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
GenreMethods

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
Published2008
Admission routes1
Has abstractyes

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