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Record W2523841505 · doi:10.1038/srep33968

Bioluminescence Microscopy as a Method to Measure Single Cell Androgen Receptor Activity Heterogeneous Responses to Antiandrogens

2016· article· en· W2523841505 on OpenAlexafffund
Pallavi Jain, Bertrand Neveu, Lauriane Vélot, Lily Wu, Yves Fradet, Frédéric Pouliot

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Biosensing Techniques and Applications
Canadian institutionsUniversité Laval
FundersCongressionally Directed Medical Research ProgramsFonds de Recherche du Québec - SantéMovember CanadaAstellas PharmaProstate Cancer CanadaDOD Prostate Cancer Research ProgramMovember FoundationCanadian Urological AssociationUniversité Laval
KeywordsAntiandrogensAndrogen receptorCellSingle-cell analysisPopulationCell biologyBioluminescenceChemistryReceptorBiologyProstate cancerInternal medicineMedicineCancerBiochemistry

Abstract

fetched live from OpenAlex

Cancer cell heterogeneity is well-documented. Therefore, techniques to monitor single cell heterogeneous responses to treatment are needed. We developed a highly translational and quantitative bioluminescence microscopy method to measure single cell androgen receptor (AR) activity modulation by antiandrogens from fluid biopsies. We showed that this assay can detect heterogeneous cellular response to drug treatment and that the sum of single cell AR activity can mirror the response in the whole cell population. This method may thus be used to monitor heterogeneous dynamic treatment responses in cancer cells.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.019
GPT teacher head0.310
Teacher spread0.291 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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