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Record W2083899507 · doi:10.1039/c1lc20050j

Chip-based size-selective sorting of biological cells using high frequency acoustic excitation

2011· article· en· W2083899507 on OpenAlexfundno aff
Gunjan Agarwal, Carol Livermore

Bibliographic record

VenueLab on a Chip · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsnot available
FundersOntario Council on Graduate Studies, Council of Ontario Universities
KeywordsSortingTemplateSf9Cell sortingMicrofluidicsBiological systemNanotechnologyMaterials scienceSpodopteraComputer scienceChemistryBiologyCellAlgorithm

Abstract

fetched live from OpenAlex

This work presents the size-selective sorting of single biological cells using the assembly process known as templated assembly by selective removal (TASR). We have demonstrated experimentally, for the first time, the selective placement and sorting of single SF9 cells (clonal isolate derived from Spodoptera frugiperda (Fall Armyworm) IPLB-Sf21-AE cells) into patterned hemispherical sites on rigid assembly templates using TASR. Nearly 100% of the assembly sites on the template were filled with matching cells (with assembly density as high as 900 sites per mm(2)) within short time spans of 3 minutes. 3-D reconstruction of cell profiles and volume analysis of cells trapped inside assembly sites demonstrates that only those cells that match the assembly site precisely (within 0.5 μm) in size are assembled on the template. The assembly conditions are also compatible with the extension of TASR to mammalian cells. TASR-based size-selective structuring and sorting of biological systems represents a valuable tool with potential for implementation in biological applications such as cell sorting for medical research or diagnostics, templating for artificial tissue replication, or isolation of single cells for the study of biological or mechanical behavior.

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 score0.014
Threshold uncertainty score0.510

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 head0.039
GPT teacher head0.223
Teacher spread0.184 · 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

Citations25
Published2011
Admission routes1
Has abstractyes

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