MétaCan
Menu
Back to cohort
Record W2071176149 · doi:10.1002/bit.23127

Cell to aperture interaction in patch‐clamp chips visualized by fluorescence microscopy and focused‐ion beam sections

2011· article· en· W2071176149 on OpenAlexaff
Christophe Py, Danish Salim, Robert Monette, Tanya Comas, Jeffrey W. Fraser, Dolores Fernández Martínez, Marzia Martina, Geoffrey Mealing

Bibliographic record

VenueBiotechnology and Bioengineering · 2011
Typearticle
Languageen
FieldEngineering
TopicIon-surface interactions and analysis
Canadian institutionsInstitute for Biological SciencesInstitute for Microstructural Sciences
Fundersnot available
KeywordsFluorescence microscopeMicroscopyFocused ion beamFluorescenceIon beamAperture (computer memory)Materials scienceBiophysicsMultiphoton fluorescence microscopeIonOpticsBeam (structure)ChemistryNanotechnologyBiologyPhysics

Abstract

fetched live from OpenAlex

Patch-clamp is an important method to monitor the electrophysiological activity of cells and the role of pharmacological compounds on specific ion channel proteins. In recent years, planar patch-clamp chips have been developed as a higher throughput approach to the established glass-pipette method. However, proper conditions to optimize the high resistance cell-to-probe seals required to measure the small currents resulting from ion channel activity are still the subject of conjecture. Here, we report on the design of multiple-aperture (sieve) chips to rapidly facilitate assessment of cell-to-aperture interactions in statistically significant numbers. We propose a method to pre-screen the quality of seals based on a dye loading protocol through apertures in the chip and subsequent evaluation with fluorescence confocal microscopy. We also show the first scanning electron micrograph of a focused ion beam section of a cell in a patch-clamp chip aperture.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.218
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueBiotechnology and BioengineeringSame topicIon-surface interactions and analysisFrench-language works237,207