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Record W2297834396 · doi:10.1186/1753-6561-9-s9-p47

The differential polarizability of CHO cells can be used to monitor changes in metabolism

2015· article· en· W2297834396 on OpenAlexafffund
Katrin Braasch, Bahareh Saboktakin Rizi, Elham Salimi, Gregory Bridges, D. J. Thomson, Michael Butler

Bibliographic record

VenueBMC Proceedings · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolarizabilityDielectrophoresisPopulationCapacitanceMembrane potentialIonic bondingElectric fieldCellBiophysicsChemistryMaterials scienceCell biologyNanotechnologyBiologyElectrodeBiochemistryIonPhysicsMedicineMicrofluidics

Abstract

fetched live from OpenAlex

Background A healthy cell has a well-balanced ionic content. Changes in the ionic content, flux, membrane and mitochondrial capacitance within the cells have been correlated to physiological and metabolic changes linked to early stages of apoptosis [1]. The ionic content and structure of individual cells allow them to be polarized in an applied electrical field with an alternating current in the radiofrequency range [2]. Any changes in the ionic content will then lead to changes in the response of the cells to the applied electric field. Hence, methods based on dielectric measurements are particularly promising for identifying early emerging subpopulations of apoptotic cells. We have developed a prototype cytometer, which measures the dielectrophoretic properties of individual cells. This tracks the trajectory of individual cells as they pass through a bank of electrodes designed to differentially perturb the cells according to their polarizability. Cells are analyzed in the instrument (DEP cytometer) by their displacement in an electrical field with sensitivity of 0.1 μm and at a rate of 5 cells per second [3]. Bulk average capacitance measurements of a bioreactor cell population have been made possible by commercially available sterilizable probes. However, the analysis of single cells allows a unique insight into the metabolism and energy flow within cells. By using single cell analysis discoveries can be made that might otherwise be masked by the overall response of a cell population.

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 score0.002
Threshold uncertainty score0.381

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 head0.033
GPT teacher head0.230
Teacher spread0.197 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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