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Record W2103781122 · doi:10.1515/bc.2010.045

Functional proteomics of kallikrein-related peptidases in ovarian cancer ascites fluid

2010· article· en· W2103781122 on OpenAlexafffund
Κατερίνα Οικονομοπούλου, Ihor Batruch, Christopher R. Smith, Antoninus Soosaipillai, Eleftherios P. Diamandis, Morley D. Hollenberg

Bibliographic record

VenueBiological Chemistry · 2010
Typearticle
Languageen
FieldMedicine
TopicCoagulation, Bradykinin, Polyphosphates, and Angioedema
Canadian institutionsMount Sinai HospitalUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsKallikreinAscitesOvarian cancerProteomicsAscitic fluidChemistryCancerInternal medicineComputational biologyMedicineBiologyBiochemistryEnzymeGene

Abstract

fetched live from OpenAlex

Kallikrein-related peptidases (KLKs) are secreted serine proteinases with trypsin or chymotrypsin-like activity. Several family members, such as KLKs 6 and 10, are potential ovarian cancer biomarkers. Recently, using a newly developed assay for active KLK6, we found that only a very small proportion of immunoreactive KLK6 in tumor-derived clinical samples (malignant ascites fluid), in cerebrospinal fluid, and in cancer cell line supernatants is enzymatically active. We therefore hypothesized that a proportion of other immunoreactive KLKs in such samples could be present, but might be partly complexed to endogenous serine proteinase inhibitors. Using a combination of immunological isolation of the enzymes, activity-based probe analysis and proteomics, we identified active KLK10 in ovarian cancer ascites and we provide preliminary data that the activity of other KLKs present in these samples can be decreased by known proteinase inhibitors (e.g., alpha2-macroglobulin, alpha1-antitrypsin). Our data suggest that the enzymatic activity of ovarian cancer-released KLKs that are detected by regular immunoassays is low in vivo and very likely regulated by proteinase inhibitors.

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 categoriesInsufficient 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 score0.251
Threshold uncertainty score0.997

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)0.0030.000

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 head0.026
GPT teacher head0.263
Teacher spread0.237 · 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

Citations32
Published2010
Admission routes2
Has abstractyes

Explore more

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