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Record W3005648042 · doi:10.1002/jbio.201960222

A waveguide imaging platform for live‐cell TIRF imaging of neurons over large fields of view

2020· article· en· W3005648042 on OpenAlexfundno aff
Ida S. Opstad, Florian Ströhl, Marcus Fantham, Colin Hockings, Oliver Vanderpoorten, Francesca W. van Tartwijk, Julie Qiaojin Lin, Jean‐Claude Tinguely, Firehun Tsige Dullo, Gabriele S. Kaminski Schierle, Balpreet Singh Ahluwalia, Clemens F. Kaminski

Bibliographic record

VenueJournal of Biophotonics · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsnot available
FundersH2020 Marie Skłodowska-Curie ActionsEngineering and Physical Sciences Research CouncilMedical Research CouncilHorizon 2020 Framework ProgrammeMedical Research Council CanadaFP7 Ideas: European Research CouncilWellcome TrustUniversitetet i TromsøAstraZeneca
KeywordsTotal internal reflection fluorescence microscopeLive cell imagingMicroscopyHippocampal formationMicroscopeNeuroscienceFluorescence microscopeOpticsBiologyPhysicsCellFluorescence

Abstract

fetched live from OpenAlex

Large fields of view (FOVs) in total internal reflection fluorescence microscopy (TIRFM) via waveguides have been shown to be highly beneficial for single molecule localisation microscopy on fixed cells [1,2] and have also been demonstrated for short-term live-imaging of robust cell types [3-5], but not yet for delicate primary neurons nor over extended periods of time. Here, we present a waveguide-based TIRFM set-up for live-cell imaging of demanding samples. Using the developed microscope, referred to as the ChipScope, we demonstrate successful culturing and imaging of fibroblasts, primary rat hippocampal neurons and axons of Xenopus retinal ganglion cells (RGCs). The high contrast and gentle illumination mode provided by TIRFM coupled with the exceptionally large excitation areas and superior illumination homogeneity offered by photonic waveguides have potential for a wide application span in neuroscience applications.

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 score0.130
Threshold uncertainty score0.493

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 head0.010
GPT teacher head0.281
Teacher spread0.270 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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