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Record W2114059954 · doi:10.1109/tbme.2009.2021577

Development of an Autonomous Biological Cell Manipulator With Single-Cell Electroporation and Visual Servoing Capabilities

2009· article· en· W2114059954 on OpenAlexaff
Kelly Sakaki, Nikolai Dechev, Robert D. Burke, Edward J. Park

Bibliographic record

VenueIEEE Transactions on Biomedical Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of VictoriaSimon Fraser University
Fundersnot available
KeywordsVisual servoingElectroporationMicroinjectionPipetteAutomationArtificial intelligenceComputer scienceComputer visionBiomedical engineeringRobotEngineeringBiologyCell biologyChemistryMechanical engineering

Abstract

fetched live from OpenAlex

Studies of single cells via microscopy and microinjection are a key component in research on gene functions, cancer, stem cells, and reproductive technology. As biomedical experiments become more complex, there is an urgent need to use robotic systems to improve cell manipulation and microinjection processes. Automation of these tasks using machine vision and visual servoing creates significant benefits for biomedical laboratories, including repeatability of experiments, higher throughput, and improved cell viability. This paper presents the development of a new 5-DOF robotic manipulator, designed for manipulating and microinjecting single cells. This biological cell manipulator (BCM) is capable of autonomous scanning of a cell culture followed by autonomous injection of cells using single-cell electroporation (SCE). SCE does not require piercing the cell membrane, thereby keeping the cell membrane fully intact. The BCM features high-precision 3-DOF translational and 2-DOF rotational motion, and a second z-axis allowing top-down placement of a micropipette tip onto the cell membrane for SCE. As a technical demonstration, the autonomous visual servoing and microinjection capabilities of the single-cell manipulator are experimentally shown using sea urchin eggs.

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 score0.391
Threshold uncertainty score0.694

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 head0.010
GPT teacher head0.188
Teacher spread0.178 · 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

Citations38
Published2009
Admission routes1
Has abstractyes

Explore more

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