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Record W2113608219 · doi:10.1002/pmic.201000082

mTRAQ‐based quantification of potential endometrial carcinoma biomarkers from archived formalin‐fixed paraffin‐embedded tissues

2010· article· en· W2113608219 on OpenAlexaff
Leroi V. DeSouza, Olga Krakovska, Marlene Darfler, David B. Krizman, Alexander D. Romaschin, Terence J. Colgan, K. W. Michael Siu

Bibliographic record

VenuePROTEOMICS · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMolecular Biology Techniques and Applications
Canadian institutionsMount Sinai HospitalSt. Michael's HospitalUniversity of TorontoYork University
Fundersnot available
KeywordsCarcinomaLaser capture microdissectionPathologyEndometriumChemistryMolecular biologyBiologyCancer researchMedicineBiochemistryGeneGene expressionEndocrinology

Abstract

fetched live from OpenAlex

Formalin-fixed paraffin-embedded (FFPE) tissues are the primary and preferred medium for archiving patients' samples. Here we demonstrate relative quantifications of protein biomarkers in extracts of laser microdissected epithelial cells from FFPE endometrial carcinoma tissues versus those from normal proliferative endometria by means of targeted proteomic analyses using LC-multiple reaction monitoring (MRM) MS with MRM Tags for Relative and Absolute Quantitation (mTRAQ) labeling. Comparable results of differential expressions for pyruvate kinase isoform M2 (PK-M2) and polymeric Ig receptor were observed between analyses on laser microdissected epithelial cells from FFPE tissues and corresponding homogenates from frozen tissues of the same individuals that had previously been analyzed and reported. We also identified PK-M2 in the normal proliferative phase of the endometrium. Other biomarkers in addition to PK-M2 and polymeric Ig receptor were also observed but not consistently and/or were at levels below the threshold for quantification.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.265
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 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

Citations62
Published2010
Admission routes1
Has abstractyes

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