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Record W2018236302 · doi:10.1593/tlo.12361

Three-Dimensional Telomeric Analysis of Isolated Circulating Tumor Cells (CTCs) Defines CTC Subpopulations

2013· article· en· W2018236302 on OpenAlexafffund
Julius Adebayo Awe, Mark Xu, Janine Wechsler, Naoual Benali-Furet, Yvon E. Cayre, Jeff Saranchuk, Darrel Drachenberg, Sabine Mai

Bibliographic record

VenueTranslational Oncology · 2013
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversity of ManitobaCancerCare ManitobaResearch Manitoba
FundersCanadian Institutes of Health ResearchCarl Zeiss Meditec AGUniversity of ManitobaMitacsCancerCare Manitoba Foundation
KeywordsCirculating tumor cellTelomereCancer researchBreast cancerMelanomaCancerColorectal cancerProstate cancerMedicineBiologyPathologyInternal medicineMetastasisDNAGenetics

Abstract

fetched live from OpenAlex

Circulating tumor cells (CTCs) have been identified with the potential to serve as suitable biomarkers for tumor stage and progression, but the availability of effective isolation technique(s) coupled with detailed molecular characterization have been the challenges encountered in making CTCs clinically relevant. For the first time, we combined isolation of CTCs using the ScreenCell filtration technique with quantitative analysis of CTC telomeres by TeloView. This resulted in the identification and molecular characterization of different subpopulations of CTCs in the same patient. Three-dimensional (3D) telomeric analysis was carried out on isolated CTCs of 19 patients that consisted of four different tumor types, namely, prostate, colon, breast, melanoma, and one lung cancer cell line. With telomeric analysis of the filter-isolated CTCs, the level of chromosomal instability (CIN) of the CTCs can be determined. Our study shows that subpopulations of CTCs can be identified on the basis of their 3D telomeric properties.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.031
GPT teacher head0.303
Teacher spread0.272 · 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

Citations32
Published2013
Admission routes2
Has abstractyes

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