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Record W183848026 · doi:10.1007/978-1-61779-797-2_1

Assessing Cancer Cell Migration and Metastatic Growth In Vivo in the Chick Embryo Using Fluorescence Intravital Imaging

2012· article· en· W183848026 on OpenAlexafffund
Hon S. Leong, Ann F. Chambers, John D. Lewis

Bibliographic record

VenueMethods in molecular biology · 2012
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsIn vivoChorioallantoic membraneMetastasisCell migrationCancer cellBiologyCancerLive cell imagingMotilityCell biologyCellEmbryoIn vitroEmbryonic stem cellPathologyCancer researchMedicineBiochemistryGenetics

Abstract

fetched live from OpenAlex

Cell migration and metastasis are key features of aggressive tumors. These processes can be difficult to study, as they often occur deep within the body of a cancer patient or an experimental animal. In vitro assays are able to model some aspects of these processes, and a number of assays have been developed to assess cancer cell motility, migration, and invasion. However, in vitro assays have inherent limitations that may miss important aspects of these processes as they occur in vivo. The chick embryo provides a powerful model for studying these processes in vivo, facilitated by the external and accessible nature of the chorioallantoic membrane (CAM), a well-vascularized tissue that surrounds the embryo. When coupled with multiple fluorescent approaches to labeling both cancer cells and the embryonic vasculature, along with image analysis tools, the chick CAM model offers cost-effective, rapid assays for studying cancer cell migration and metastasis in a physiologically-relevant, in vivo setting. Here, we present recent developments of detailed procedures for using shell-less chick embryos, coupled with fluorescent labeling of cancer cells and/or chick vasculature, to study cancer cell migration and metastasis in vivo.

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)0.002
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 score0.253
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.040
GPT teacher head0.426
Teacher spread0.385 · 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

Citations36
Published2012
Admission routes2
Has abstractyes

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