MétaCan
Menu
Back to cohort
Record W2159428678 · doi:10.1002/rcm.2119

Direct analysis of laser capture microdissected endometrial carcinoma and epithelium by matrix‐assisted laser desorption/ionization mass spectrometry

2005· article· en· W2159428678 on OpenAlexaff
Jingzhong Guo, Terence J. Colgan, Leroi V. DeSouza, Mary Joe Rodrigues, Alexander D. Romaschin, K. W. Michael Siu

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2005
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsToronto General HospitalMount Sinai HospitalUniversity of TorontoYork University
Fundersnot available
KeywordsChemistryMass spectrometryDesorptionLaserMatrix-assisted laser desorption electrospray ionizationMass spectrometry imagingSurface-enhanced laser desorption/ionizationIonizationMatrix (chemical analysis)Matrix-assisted laser desorption/ionizationAnalytical Chemistry (journal)ChromatographySample preparation in mass spectrometryElectrospray ionizationIonPhysical chemistryAdsorptionOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

Direct analysis of laser capture microdissected malignant and normal endometrial epithelium using matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry (MS) was able to detect a number of proteins that are overexpressed in malignant epithelial cells. A total of 16 physiologic and malignant endometrial samples were laser capture microdissected, including four proliferative and four secretory endometria, and eight endometrioid adenocarcinomas. Two of these proteins, at 10,834 and 10,843 Da, likely correspond to calgranulin A and chaperonin 10, two proteins that had previously been identified in endometrioid adenocarcinoma in whole tissue homogenate by MS analysis. Direct analysis by MALDI-MS not only confirms that these proteins are overexpressed in endometrial carcinoma, but also localizes them to the epithelial cells, the expected cancer site.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.084
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.013
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.013
GPT teacher head0.269
Teacher spread0.256 · 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.

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

Citations26
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueRapid Communications in Mass SpectrometrySame topicMass Spectrometry Techniques and ApplicationsFrench-language works237,207