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Record W1982488814 · doi:10.3791/2177

Fabrication of Micro-tissues using Modules of Collagen Gel Containing Cells

2010· article· en· W1982488814 on OpenAlexafffund
M. Dean Chamberlain, Mark Butler, Ema C. Ciucurel, Lindsay E. Fitzpatrick, Omar F. Khan, Brendan M. Leung, Chuen Lo, R. R. Patel, Alexandra Velchinskaya, Derek N. Voice, Michael V. Sefton

Bibliographic record

VenueJournal of Visualized Experiments · 2010
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Toronto
FundersNational Institute of Biomedical Imaging and BioengineeringNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsFabricationModular designMaterials scienceCoatingBiomedical engineeringNanotechnologyComputer sciencePathology

Abstract

fetched live from OpenAlex

This protocol describes the fabrication of a type of micro-tissues called modules. The module approach generates uniform, scalable and vascularized tissues. The modules can be made of collagen as well as other gelable or crosslinkable materials. They are approximately 2 mm in length and 0.7 mm in diameter upon fabrication but shrink in size with embedded cells or when the modules are coated with endothelial cells. The modules individually are small enough that the embedded cells are within the diffusion limit of oxygen and other nutrients but modules can be packed together to form larger tissues that are perfusable. These tissues are modular in construction because different cell types can be embedded in or coated on the modules before they are packed together to form complex tissues. There are three main steps to making the modules: neutralizing the collagen and embedding cells in it, gelling the collagen in the tube and cutting the modules and coating the modules with endothelial cells.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.423

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 head0.041
GPT teacher head0.430
Teacher spread0.389 · 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

Citations9
Published2010
Admission routes2
Has abstractyes

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