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Record W4390879246 · doi:10.1002/adhm.202302436

Cancer Metastasis‐on‐a‐Chip for Modeling Metastatic Cascade and Drug Screening

2024· review· en· W4390879246 on OpenAlexfundno aff
Anastasia Brooks, Yali Zhang, Jiezhong Chen, Chun‐Xia Zhao

Bibliographic record

VenueAdvanced Healthcare Materials · 2024
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilAustralian Research CouncilMedical Research CouncilUniversity of AdelaideUniversity of QueenslandGallipoli Medical Research FoundationUniversity of Alberta
KeywordsIntravasationMetastasisCancerTumor microenvironmentMicrofluidicsDrug developmentNanotechnologyDrugComputer scienceCancer researchMedicineMaterials sciencePharmacologyInternal medicine

Abstract

fetched live from OpenAlex

Microfluidic chips are valuable tools for studying intricate cellular and cell-microenvironment interactions. Traditional in vitro cancer models lack accuracy in mimicking the complexities of in vivo tumor microenvironment. However, cancer-metastasis-on-a-chip (CMoC) models combine the advantages of 3D cultures and microfluidic technology, serving as powerful platforms for exploring cancer mechanisms and facilitating drug screening. These chips are able to compartmentalize the metastatic cascade, deepening the understanding of its underlying mechanisms. This article provides an overview of current CMoC models, focusing on distinctive models that simulate invasion, intravasation, circulation, extravasation, and colonization, and their applications in drug screening. Furthermore, challenges faced by CMoC and microfluidic technologies are discussed, while exploring promising future directions in cancer research. The ongoing development and integration of these models into cancer studies are expected to drive transformative advancements in the field.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.145
GPT teacher head0.451
Teacher spread0.306 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations34
Published2024
Admission routes1
Has abstractyes

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