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Record W2891293600 · doi:10.1039/c8an01490f

Analysing single live cells by scanning electrochemical microscopy

2018· review· en· W2891293600 on OpenAlexafffund
Fraser P. Filice, Zhifeng Ding

Bibliographic record

VenueThe Analyst · 2018
Typereview
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaWestern UniversityCanada Foundation for Innovation
KeywordsScanning electrochemical microscopyMicroscopyScanning ion-conductance microscopyScanning probe microscopyElectrochemistryNanotechnologyMaterials scienceBiophysicsChemistryBiologyScanning confocal electron microscopyPathologyMedicineElectrode

Abstract

fetched live from OpenAlex

Single live cell analysis methods provide information on the characteristics of individual cells, yielding not only bulk population averages but also their heterogeneity. Scanning electrochemical microscopy (SECM) offers single live cell activities along its topography with high accuracy probe tip positioning. Both intracellular and extracellular processes can be electrochemically examined through the use of SECM. This non-invasive technique allows for high resolution mapping of electrochemical measurements in or around the cell sample of interest. Reactive oxygen species and reactive nitrogen species can be determined in a non-invasive label-free method and utilized as a probe for cellular pathology and physiology. Membrane permeability and rate of membrane species transport can be quantified in SECM. The cell response to external stressors can be monitored and modelled. SECM is able to offer nanoscale mapping and low concentration detection, providing a powerful bioanalytical tool for live cell studies. Herein we present an overview of recent progress in the imaging and characterization of single live cells using SECM.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.762
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.303
Teacher spread0.282 · 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.

Study designBench or experimental
Domainnot available
GenreReview

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

Citations42
Published2018
Admission routes2
Has abstractyes

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