MétaCan
Menu
← Back to cohort

Complex Peptide Biosensors for Detection of Intracellular Kinase Biomarkers

2012· article· en· W3173431435 on OpenAlexfundno aff
May C. Morris, Laëtitia Kurzawa, N. Chan Van, Morgan Pellerano

Bibliographic record

VenueThe FASEB Journal · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsnot available
FundersCNIB
KeywordsCyclin-dependent kinaseKinaseIntracellularCell biologyBiosensorPeptideIn vitroCyclinChemistryBiologyCell cycleBiochemistryCell

Abstract

fetched live from OpenAlex

Cyclin‐dependant kinases play a central role in coordinating cell cycle progression, and in sustaining proliferation of cancer cells, thereby constituting attractive pharmacological targets. We have developed a family of environmentally‐sensitive fluorescent peptide biosensors to probe the relative abundance and activities of CDK/cyclin kinases. CDKSENS and CDKACT sensors allow to probe CDK/Cyclin kinases in a sensitive and specific fashion in vitro. Complexation of these peptide biosensors with cell‐penetrating peptide carriers allows to form stable nanoparticles that can probe these kinases in living cells in a non‐invasive fashion (Kurzawa et al. PloS One 2011). This technology can be applied to monitor subtle alterations between healthy and cancer cell lines, to probe kinase levels and activities in tumour biopsies, and to monitor response to therapeutics in vitro and in vivo. Source of Funding: Grants to MCM “Chercheuse d'Avenir” Région Languedoc‐Roussillon and INCA

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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.273
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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB Journal→Same topicAdvanced biosensing and bioanalysis techniques→French-language works237,207→