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Record W2972472006 · doi:10.1039/c9lc00352e

Emulating endothelial dysfunction by implementing an early atherosclerotic microenvironment within a microfluidic chip

2019· article· en· W2972472006 on OpenAlexaff
Yujin Shin, Seongjin Lim, Jin-Won Kim, Jessie S. Jeon, Hongki Yoo, Bomi Gweon

Bibliographic record

VenueLab on a Chip · 2019
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsKootenay Association for Science & Technology
FundersNational Research Foundation of Korea
KeywordsMicrofluidicsMicrofluidic chipChipBiomedical engineeringNanotechnologyLab-on-a-chipOrgan-on-a-chipEndothelial dysfunctionMaterials scienceEngineeringMedicineInternal medicineElectrical engineering

Abstract

fetched live from OpenAlex

Recent studies on endothelial dysfunction in relation to vascular diseases including atherosclerosis have highlighted the key contribution of the microenvironment of endothelial cells (ECs). By mimicking the microenvironment of early atherosclerotic lesions, here, we replicate the pathophysiological phenotype and function of ECs within microchannels. Considering the elevated deposition of fibronectin (FN) in early atherosclerotic plaques and the close correlation between the vascular stiffness and the progression of atherosclerosis, we utilized FN coated hydrogels with increased stiffness for endothelial substrates within the microchannels. As a result, we demonstrated that endothelial integrity on FN coated microchannels is likely to be undermined exhibiting a random orientation in response to the applied fluid flow, notable disruption of vascular endothelial cadherins (VE-cadherins), and higher endothelial permeability as opposed to that on microchannels coated with collagen (CL), the atheroresistant vascular model.

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 categoriesInsufficient payload (model declined to judge)
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 score0.137
Threshold uncertainty score1.000

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)0.0010.001

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 head0.013
GPT teacher head0.239
Teacher spread0.226 · 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 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
Published2019
Admission routes1
Has abstractyes

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