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Record W2016440365 · doi:10.1002/jbio.201000057

Fluorescence imaging to quantify the fluorescent microspheres in cardiac tissue

2010· article· en· W2016440365 on OpenAlexaff
Eugene Gussakovsky, Bozena Kuzio, Yanmin Yang, V.V. Kupriyanov

Bibliographic record

VenueJournal of Biophotonics · 2010
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsUniversity of ManitobaNational Research Council Institute for Biodiagnostics
Fundersnot available
KeywordsFluorescenceMicrosphereMaterials scienceRepeatabilityBiomedical engineeringFluorescence-lifetime imaging microscopyPixelResolution (logic)ChemistryOpticsChromatographyComputer sciencePhysicsMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

To quantify the fluorescent microsphere (FM) content in cardiac tissue, which is an indicative of blood flow, fluorescence imaging of both sides of the pig heart slice was employed. Despite the light scattering inside the tissue and contributions from multiple tissue layers to the total emission, it is shown that the fluorescence intensity at any pixel is proportional to the FM content and the fluorescence image may be transformed to the image of the FM concentration. A convenient standard for the emission-FM concentration transformation is proposed. The approach has several advantages in comparison with the traditional "digestion & extraction" method such as: non-destructiveness, high spatial resolution, high throughput, repeatability and simplicity of operation.

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: none
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 communication0.0000.000
Open science0.0000.000
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 head0.007
GPT teacher head0.312
Teacher spread0.305 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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