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Record W2421656184 · doi:10.1007/978-1-61779-204-5_10

Identification of the Myosin Heavy Polypeptide 9 as a Downstream Effector of the Proprotein Convertases in the Human Colon Carcinoma HT-29 Cells

2011· article· en· W2421656184 on OpenAlexaff
Nathalie Scamuffa, Peter Metrakos, Fabien Calvo, Abdel‐Majid Khatib

Bibliographic record

VenueMethods in molecular biology · 2011
Typearticle
Languageen
FieldMedicine
TopicPeptidase Inhibition and Analysis
Canadian institutionsRoyal Victoria HospitalMcGill University
Fundersnot available
KeywordsProprotein ConvertasesEffectorCarcinogenesisMetastasisCancer researchColorectal cancerCancerCancer cellChemistryCell biologyBiologyBiochemistryLDL receptor

Abstract

fetched live from OpenAlex

In addition to the large spectrum of the protein precursors processed and activated by the proprotein convertases (PCs) that are crucial for the maintenance of the malignant phenotype of colon cancer cells such as matrix metalloproteases, adhesion molecules, growth factors, and growth factor receptors, the PCs also regulate the expression and the activity of other proteins that are not PC substrates and involved in the acquisition of the metastatic and tumorigenic potential of these tumor cells. The identification in colon cancer cells of such proteins is thereby crucial for the understanding of the cascade of molecular events regulated by the PCs leading to tumorigenesis and metastasis and thus may constitute potential candidates for new colon cancer-specific targets and/or biomarkers. Using the human colon cancer cells HT-29 and ProteinChip arrays analysis that apply the surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS), we identified the myosin heavy polypeptide 9 as new downstream effector of PCs in these cells. This protein was reported to be involved in the processes of malignant epithelial transformation and its role in colon cancer is unknown.

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 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 score0.027
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.355
Teacher spread0.331 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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