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Record W2129585389 · doi:10.1373/clinchem.2009.136283

Nipple Aspirate Fluid Proteome of Healthy Females and Patients with Breast Cancer

2010· article· en· W2129585389 on OpenAlexaff
Maria Pavlou, Vathany Kulasingam, Edward R. Sauter, Beth Kliethermes, Eleftherios P. Diamandis

Bibliographic record

VenueClinical Chemistry · 2010
Typearticle
Languageen
FieldChemistry
TopicAdvanced Proteomics Techniques and Applications
Canadian institutionsUniversity of TorontoUniversity Health NetworkMount Sinai Hospital
Fundersnot available
KeywordsProteomeBreast cancerCancerBlood proteinsProteomicsBiologyMembrane proteinChemistryBioinformaticsMedicineBiochemistryInternal medicineGeneMembrane

Abstract

fetched live from OpenAlex

BACKGROUND: The ductal/alveolar system of the female breast constantly secretes and reabsorbs fluid in nonpregnant/nonlactating women. This fluid, referred to as nipple aspirate fluid (NAF), can be obtained by a noninvasive procedure and it is part of the microenvironment where more than 95% of breast cancers arise. METHODS: Using an Orbitrap mass analyzer coupled to a linear ion trap, we performed an in-depth proteomic analysis of NAF samples obtained from 3 healthy individuals and 3 patients with breast cancer. Multiple fractionation methods such as size-exclusion and anion-exchange chromatography were applied for protein separation before mass spectrometric analysis. RESULTS: We identified more than 800 unique proteins in total, generating the most extensive NAF proteome thus far. Using gene ontology, we classified the identified proteins by their subcellular localization and found that more than 50% were extracellular or plasma membrane proteins. By searching against the Plasma Proteome Database, we confirmed that 40% of the proteins were also found in the plasma. Unigene database searching for transcripts of the proteins not found in the plasma revealed that the vast majority were expressed in the mammary gland. CONCLUSIONS: Our extensive proteome database for NAF may be helpful in the identification of novel cancer biomarkers.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.490

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 head0.022
GPT teacher head0.340
Teacher spread0.319 · 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

Citations44
Published2010
Admission routes1
Has abstractyes

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