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Record W2083214390 · doi:10.1117/12.819494

Waveguide evanescent field fluorescence microscopy: from cell-substratum distances to kinetic cell behaviour

2009· article· en· W2083214390 on OpenAlexaff
Abdollah Hassanzadeh, Heung Kan, Souzan Armstrong, S. Jeffrey Dixon, Stephen M. Sims, Silvia Mittler

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Biosensing Techniques and Applications
Canadian institutionsWestern University
Fundersnot available
KeywordsTotal internal reflection fluorescence microscopeMicroscopyEvanescent waveTotal internal reflectionMaterials scienceFluorescenceFluorescence microscopeSubstrate (aquarium)OpticsWaveguideReflection (computer programming)Optical microscopeLive cell imagingOptoelectronicsChemistryCellScanning electron microscopePhysics

Abstract

fetched live from OpenAlex

We demonstrate an alternative to total internal reflection fluorescence (TIRF) microscopy. A method for imaging ultra thin films and living cells located on waveguides illuminated with their evanescent fields is introduced. Analysis of ion-exchanged waveguides focusing on their application as substrates for microscopic study of interfacial phenomena is presented. Various LB film stacks were imaged to verify the intensity interpretation due to the exponentially decaying evanescent fields of the waveguides. The paper gives an overview on the imaging applications of this technique. The fluorescence intensity has been used to determine quantitatively the cell attachment of osteoblasts (bone forming cells) to substrate surfaces. In live cell studies trypsin (a protease) was used to alter attachment of the cells to the substrate, as a means to demonstrate feasibility of the method in measuring attachment dynamics of cells in real time.

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 score0.003

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.242
Teacher spread0.235 · 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

Citations13
Published2009
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Biosensing Techniques and ApplicationsFrench-language works237,207