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Record W2897523069 · doi:10.1002/btpr.2733

Fabrication of thin‐layer matrigel‐based constructs for three‐dimensional cell culture

2018· article· en· W2897523069 on OpenAlexafffund
Kristin Robin Ko, Meng‐Chiao Tsai, John P. Frampton

Bibliographic record

VenueBiotechnology Progress · 2018
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMatrigelExtracellular matrixSelf-healing hydrogelsCell cultureMaterials science3D cell cultureTissue engineeringNanotechnologyBiomedical engineeringBiophysicsCellChemistryBiologyBiochemistryMedicinePolymer chemistry

Abstract

fetched live from OpenAlex

Extracellular matrix-based hydrogels such as Matrigel are easy-to-use, commercially available, and offer environments for three-dimensional (3-D) cell culture that mimic native tissue. However, manipulating small volumes of these materials to produce thin-layer 3-D culture systems suitable for analysis is difficult because of air-liquid-substrate interfacial tension effects and evaporation. Here, we demonstrate two simple techniques that use standard liquid-handling tools and nontreated 96-well plates to produce uniform, thin-layer constructs for 3-D culture of cells in Matrigel. The first technique, the floating 3-D cell culture method, uses phase-separating polymers to form a barrier between the dispensed Matrigel, air, and cultureware surface to generate consistently thin hydrogels from volumes as low as 5 μL. These unanchored gels provide a useful assay for investigating airway smooth muscle cell contraction and may have future applications in studying asthma pathophysiology. The second technique, the fixed 3-D cell culture method, provides an anchored gel system for culturing noncontractile cells (e.g., neurons) where 20 μL of Matrigel is dispensed into the bottom of a well filled with culture medium to form a thin gel containing embedded cells. This technique has potential widespread applications as an accessible 3-D culture platform for high-throughput production of disease models for evaluation of novel drug therapies. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2733, 2019.

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)0.000
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 score0.237
Threshold uncertainty score0.458

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.019
GPT teacher head0.285
Teacher spread0.266 · 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

Citations14
Published2018
Admission routes2
Has abstractyes

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