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Record W2140445067 · doi:10.1158/1535-7163.mct-06-0593

A novel three-dimensional model to quantify metastatic melanoma invasion

2007· article· en· W2140445067 on OpenAlexaff
Cyrus M. Ghajar, V. Suresh, Shelly R. Peyton, Christopher B. Raub, Frank L. Meyskens, Steven C. George, Andrew J. Putnam

Bibliographic record

VenueMolecular Cancer Therapeutics · 2007
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsImmunovaccine (Canada)
FundersNational Institutes of HealthNational Heart, Lung, and Blood InstituteAchievement Rewards for College Scientists Foundation
KeywordsIn vivoMetastasisMelanomaCancer researchMetastatic melanomaCell cultureIn vitroBiologyCellCancerMatrix (chemical analysis)ChemistryBiotechnologyGenetics

Abstract

fetched live from OpenAlex

Although attempts to develop any viable chemotherapeutic approaches to combat metastatic cancers have largely failed, potential genetic targets to halt metastatic progression continue to be identified. As drugs are developed to address these targets, there is a need for high-throughput systems that accurately reproduce in vivo microenvironments to gauge their efficacy. Accordingly, we have developed a three-dimensional in vitro culture system representative of the environment present upon secondary metastasis to quantitatively measure tumor cell invasion in this setting three-dimensionally. Culturing melanomas of different metastatic capacities within the system showed that each cell type invades the matrix in a manner commensurate to its known metastatic potential in vivo. Moreover, the developed quantitative schemes were put to use to characterize the effect of microenvironmental influences (i.e., matrix components, interstitial cell presence) on planar and vertical melanoma invasion. We propose this novel, quantitative system as a useful tool to assess the effects of pharmacologic and/or microenvironmental influences on tumor cell invasion at a metastatic site.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.568
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.083
GPT teacher head0.343
Teacher spread0.260 · 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

Citations25
Published2007
Admission routes1
Has abstractyes

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