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Record W2107139758 · doi:10.1039/b715707j

Microfluidic depletion of endothelial cells, smooth muscle cells, and fibroblasts from heterogeneous suspensions

2008· article· en· W2107139758 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLab on a Chip · 2008
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Toronto
FundersAmerican Health Assistance Foundation
KeywordsMicrofluidicsAdhesionCell adhesionNanotechnologyLigand (biochemistry)CellChemistryBiophysicsCell typeMaterials scienceReceptorBiochemistryBiology

Abstract

fetched live from OpenAlex

Interactions between ligands and cell surface receptors can be exploited to design adhesion-based microfluidic cell separation systems. When ligands are immobilized on the microfluidic channel surfaces, the resulting cell capture devices offer the typical advantages of small sample volumes and low cost associated with microfluidic systems, with the added benefit of not requiring complex fabrication schemes or extensive operational infrastructure. Cell-ligand interactions can range from highly specific to highly non-specific. This paper describes the design of an adhesion-based microfluidic separation system that takes advantage of both types of interactions. A 3-stage system of microfluidic devices coated with the tetrapeptides arg-glu-asp-val (REDV), val-ala-pro-gly (VAPG), and arg-gly-asp-ser (RGDS) is utilized to deplete a heterogeneous suspension containing endothelial cells, smooth muscle cells, and fibroblasts. The ligand-coated channels together with a large surface area allow effective depletion of all three cell types in a stagewise manner.

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.

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)0.000
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 score0.033
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.017
GPT teacher head0.222
Teacher spread0.205 · 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