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Record W2497055362 · doi:10.1017/cbo9781139939751.005

Microscale generation of dynamic forces in cell culture systems

2015· book-chapter· en· W2497055362 on OpenAlexaff
Christopher Moraes, Luke A. MacQueen, Yu Sun, Craig A. Simmons

Bibliographic record

VenueCambridge University Press eBooks · 2015
Typebook-chapter
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicroscale chemistryComputer scienceFunction (biology)Relevance (law)NanotechnologySystems engineeringBiochemical engineeringEngineeringMaterials scienceBiologyCell biology

Abstract

fetched live from OpenAlex

Dynamic mechanical forces play a critical role in modulating cellular function, and inclusion of these extracellular stimuli in culture systems may improve the relevance and utility of biological results. In this work, we discuss recent advances made by our research group in applying dynamically controlled mechanical stimuli to cells cultured in 2-D and 3-D arrayed environments. Advantages in throughput and precision arising from microengineering such systems are demonstrated, with illustrative examples of potential biological applications. Engineering challenges associated with building these culture systems are explored, and the design and fabrication strategies that we have developed are discussed. Finally, the ability to incorporate additional sensing technologies into these dynamic screening platforms is explored.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.032
GPT teacher head0.225
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes1
Has abstractyes

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