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Record W4389247402 · doi:10.1364/boe.504978

Single source CARS-based multimodal microscopy system for biological tissue imaging [Invited]

2023· article· en· W4389247402 on OpenAlexafffund
Ming-Yu Sheng, Yuan Zhao, Zhenguo Wu, Jianhua Zhao, Harvey Lui, Sunil Kalia, Haishan Zeng

Bibliographic record

VenueBiomedical Optics Express · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsCanadian Centre for Applied Research in Cancer ControlVancouver Coastal HealthVancouver Coastal Health Research InstituteUniversity of British ColumbiaBC Children's HospitalBC Cancer Agency
FundersCanadian Institutes of Health ResearchBC HydroVGH and UBC Hospital FoundationCanadian Dermatology Foundation
KeywordsMicroscopyBiological imagingMedical imagingComputer scienceOptical imagingImage processingOpticsBiomedical engineeringComputer visionArtificial intelligenceMedicinePhysics

Abstract

fetched live from OpenAlex

A coherent anti-Stokes Raman scattering (CARS)-based multimodality microscopy system was developed using a single Ti:sapphire femtosecond laser source for biological imaging. It provides three complementary and co-registered imaging modalities: CARS, MPM (multiphoton microscopy), and RCM (reflectance confocal microscopy). The imaging speed is about 1 frame-per-second (fps) with a digital resolution of 1024 × 1024 pixels. This microscopy system can provide clear 2-dimensional and 3-dimensional images of ex-vivo biological tissue samples. Its spectral selection initiates vibrational excitation in lipid cells (approximately 2850 cm -1 ) using two filters on the pump and Stokes beam paths. The excitation can be tuned over a wide spectral range with adjustable spectral filters. The imaging capability of this CARS-based multimodal microscopy system was demonstrated using porcine fat, murine skin, and murine liver tissue samples.

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)0.001
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 score0.412
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.021
GPT teacher head0.336
Teacher spread0.315 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

Explore more

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