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Record W2736923812 · doi:10.1109/icra.2017.7989635

A high-precision robot-aided single-cell biopsy system

2017· article· en· W2736923812 on OpenAlexaff
Adnan Shakoor, Tao Luo, Shuxun Chen, Mingyang Xie, James K. Mills, Dong Sun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicromanipulatorPipetteForeskinBiopsyCellOrganelleBiomedical engineeringFibroblastRobotMicrofluidicsNucleusMicrofluidic chipComputer scienceMaterials scienceChemistryBiologyNanotechnologyCell biologyArtificial intelligenceCell cultureEngineeringPathologyMedicine

Abstract

fetched live from OpenAlex

In this paper, we present a precise robot-aided single-cell surgery system to perform single-cell biopsy for cells <25 μm in diameter. A microfluidic chip is designed to arrange upto 100 individual cells in an array. A micropipette mounted onto a 3-DOF micromanipulator and a computer mouse-operated high-precision XY stage is developed to perform high-precision and high-throughput single-cell biopsy. The system is evaluated experimentally by extracting two organelles from adherent cells patterned in a microfluidic chip. The fluorescent-labeled nucleus and mitochondria of human foreskin fibroblast cells are biopsied to demonstrate the capability of the proposed system. The survival rate of the semi-automated biopsy is 73% and 45% for mitochondrial and nucleus biopsies, respectively.

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 score0.081
Threshold uncertainty score0.507

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 head0.019
GPT teacher head0.201
Teacher spread0.182 · 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
Published2017
Admission routes1
Has abstractyes

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