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Record W2808504040 · doi:10.1039/c8lc00310f

Dynamic CTC phenotypes in metastatic prostate cancer models visualized using magnetic ranking cytometry

2018· article· en· W2808504040 on OpenAlexafffund
Leyla Kermanshah, Mahla Poudineh, Sharif Ahmed, Luan N. Nguyen, Sanjana Srikant, Rhema Makonnen, Fernando Pena Cantu, Michael Corrigan, Shana O. Kelley

Bibliographic record

VenueLab on a Chip · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversity of TorontoToronto Public Health
FundersNational Cancer InstituteNatural Sciences and Engineering Research Council of CanadaCanadian Cancer Society Research InstituteCanadian Institutes of Health ResearchConnaught FundOntario Research Foundation
KeywordsProstate cancerPhenotypeFlow cytometryProstateRanking (information retrieval)CytometryCancerCancer researchPathologyMedicineComputational biologyOncologyBiologyInternal medicineComputer scienceImmunologyArtificial intelligenceGeneticsGene

Abstract

fetched live from OpenAlex

Tumors can shed thousands of cells into the circulation daily. These circulating tumor cells (CTCs) are heterogeneous, and their phenotypes change dynamically. Real-time monitoring of CTC phenotypes is crucial to elucidate the role of CTCs in the metastatic cascade. Here, we monitor phenotypic changes in CTCs in mice xenografted with tumors with varying aggressiveness during cancer progression and a course of chemotherapy to study the metastatic potential of CTCs and changes in the properties of these cells in response to treatment. A new device that enables magnetic ranking cytometry (MagRC) is employed to profile the phenotypic properties of CTCs. Overall, CTCs from metastatic xenografts in mice display dynamic and heterogeneous profiles while non-metastatic models had static profiles. Decreased heterogeneity followed by a reduction in metastasis incidence was observed after a course of chemotherapy administered to highly metastatic xenografts. Phenotypic profiling of CTCs could be employed to monitor disease progression and predict therapeutic responses.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.788
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.361
Teacher spread0.322 · 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 designSimulation or modeling
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

Citations31
Published2018
Admission routes2
Has abstractyes

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