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Record W2159266673 · doi:10.1039/c2lc20967e

Electrical sorting of Caenorhabditis elegans

2012· article· en· W2159266673 on OpenAlexafffund
Pouya Rezai, Sangeena Salam, P. Ravi Selvaganapathy, Bhagwati P. Gupta

Bibliographic record

VenueLab on a Chip · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Aging, and Longevity in Model Organisms
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsCaenorhabditis elegansSortingsortBiologyThroughputComputer scienceMicrofluidicsModel organismBiological systemIsolation (microbiology)Computational biologyNanotechnologyBioinformaticsGeneticsAlgorithmGeneMaterials science

Abstract

fetched live from OpenAlex

The nematode (worm) C. elegans is one of the widely studied animal model organisms in biology. It develops through 4 larval stages (L1-L4) in 2 to 3 days before becoming a young adult. Biological assays involving C. elegans frequently require a large number of animals that are appropriately staged and exhibit a similar behaviour. We have developed a new method to synchronize animals that relies on the electrotactic response (electric field-induced motion) of C. elegans to sort them in parallel based on their age, size and phenotype. By using local electric field traps in a microfluidic device, we can efficiently sort worms from a mixed culture in a semi-continuous flow manner (with a minimum throughput of 78 worms per minute per load-run) and obtain synchronized populations of animals. In addition to sorting larvae, our device can also distinguish between young and old adults efficiently. Unlike fluorescent based sorting systems that use active imaging based feedback, this method is passive and automatic and uses the innate behaviour of the worm. Considering that the entire procedure takes only a few minutes to run and is cost-effective, it promises to simplify and accelerate experiments requiring homogeneous cultures of worms as well as to facilitate isolation of mutants that have abnormal electrotaxis. More importantly, our method of isolating and separating worms using locomotion as a defining characteristic promises development of advanced microfluidics-based systems to study the neuronal basis of movement-related defects in worms and facilitate high-throughput chemical screening and drug discovery.

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 score0.016
Threshold uncertainty score0.478

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 head0.011
GPT teacher head0.237
Teacher spread0.227 · 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

Citations71
Published2012
Admission routes2
Has abstractyes

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