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Record W2107831290 · doi:10.1109/robot.2009.5152759

Automated mouse embryo injection moves toward practical use

2009· article· en· W2107831290 on OpenAlexaff
Xinyu Liu, Yu Sun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEmbryoComputer scienceSimulationBiomedical engineeringArtificial intelligenceBiologyEngineeringCell biology

Abstract

fetched live from OpenAlex

Taking a different architecture than manual operation and existing microrobotic systems, this paper presents the first automated system that employs novel microfabricated cell holding devices and vision-position based control of multiple motion control devices to achieve easy sample immobilization, rapid cell orientation, and fast injection of mouse embryos. The system requires minimal human involvement through a maximum of three computer mouse clicking per mouse embryo, is human operator skill independent, and is immune from fatigue. While no robotic systems have provided performance close to manual operation, according to the preliminary experimental results (n = 90) from this study, this microrobotic system demonstrated: (i) an injection speed of 9 embryos/min vs. 2 embryos/min in typical manual operation, (ii) a success rate of 98.9%, a higher rate than the best success rate (90%) achieved by proficient injection technicians with over 10 years' experience, and (iii) a high survival rate of 82.1%, a rate comparable with the best survival rate (~80%) achieved by proficient injection technicians. Further improvement of the automated system will change the way of how mouse embryos are injected and promise its practical use in biology laboratories and mouse facilities.

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 score0.266
Threshold uncertainty score0.376

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 head0.027
GPT teacher head0.256
Teacher spread0.230 · 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

Citations17
Published2009
Admission routes1
Has abstractyes

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