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Record W2067744732 · doi:10.1063/1.3006000

A system for high-speed microinjection of adherent cells

2008· article· en· W2067744732 on OpenAlexafffund
Wenhui Wang, Yu Sun, Ming Zhang, Robin Anderson, Lowell Langille, Warren C. W. Chan

Bibliographic record

VenueReview of Scientific Instruments · 2008
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsToronto General HospitalUniversity of Toronto
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsMicroinjectionOperator (biology)Biomedical engineeringComputer scienceMaterials scienceConsistency (knowledge bases)SimulationBiologyCell biologyArtificial intelligenceMedicine

Abstract

fetched live from OpenAlex

This paper reports on a semi-automated microrobotic system for adherent cell injection. Different from embryos/oocytes that have a spherical shape and regular morphology, adherent cells are flat with a thickness of a few micrometers and are highly irregular in morphology. Based on computer vision microscopy and motion control, the system coordinately controls a three-degrees-of-freedom microrobot and a precision XY stage, demonstrating an injection speed of 25 endothelial cells per minute with a survival rate of 95.7% and a success rate of 82.4% (n=1012). The system has a high degree of performance consistency. It is operator skill independent and immune from human fatigue, only requiring a human operator to select injection destinations through computer mouse clicking as the only operator intervention. The microrobotic system makes the injection of a large number of adherent cells practical for testing cellular responses to foreign molecules.

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)0.001
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.020
GPT teacher head0.221
Teacher spread0.200 · 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
GenreMethods

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

Citations61
Published2008
Admission routes2
Has abstractyes

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