MétaCan
Menu
Back to cohort
Record W2418721353 · doi:10.2144/000114245

Micropatterning Strategies to Engineer Controlled Cell and Tissue Architecture in Vitro

2015· article· en· W2418721353 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBioTechniques · 2015
Typearticle
Languageen
FieldEngineering
TopicNanofabrication and Lithography Techniques
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicropatterningMicrocontact printingNanotechnologyProcess (computing)Computer scienceProtocol (science)Materials science

Abstract

fetched live from OpenAlex

Micropatterning strategies, which enable control over cell and tissue architecture in vitro, have emerged as powerful platforms for modelling tissue microenvironments at different scales and complexities. Here, we provide an overview of popular micropatterning techniques, along with detailed descriptions, to guide new users through the decision making process of which micropatterning procedure to use, and how to best obtain desired tissue patterns. Example techniques and the types of biological observations that can be made are provided from the literature. A focus is placed on microcontact printing to obtain co-cultures of patterned, confluent sheets, and the challenges associated with optimizing this protocol. Many issues associated with microcontact printing, however, are relevant to all micropatterning methodologies. Finally, we briefly discuss challenges in addressing key limitations associated with current micropatterning technologies.

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: none
Teacher disagreement score0.680
Threshold uncertainty score0.626

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 head0.007
GPT teacher head0.228
Teacher spread0.220 · 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