MétaCan
Menu
Back to cohort
Record W1987453866 · doi:10.1038/srep01890

A Method for accurate in vivo micro-Raman spectroscopic measurements under guidance of advanced microscopy imaging

2013· article· en· W1987453866 on OpenAlexafffund
Hequn Wang, Anthony M. D. Lee, Harvey Lui, David I. McLean, Haishan Zeng

Bibliographic record

VenueScientific Reports · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsVancouver Coastal Health Research InstituteUniversity of British ColumbiaVancouver Coastal HealthBC Cancer Agency
FundersCIHR Skin Research Training CentreBC Cancer AgencyCanadian Institutes of Health ResearchBC HydroMichael Smith Health Research BCCanadian Dermatology Foundation
KeywordsRaman spectroscopyConfocalMicroscopyBiomedical engineeringConfocal microscopySpectral imagingMaterials scienceOpticsMicroscopeComputer scienceBiological systemComputer visionArtificial intelligencePhysicsMedicineBiology

Abstract

fetched live from OpenAlex

The movement from the subjects during in vivo confocal Raman spectral measurements could change the measurement volume, leading to non-specific signals and inaccurate interpretation of the acquired spectrum. Here we introduce a generally applicable method that includes (1) developing a multimodal system to achieve real-time monitoring of every spectral measurement with reflectance confocal microscopy (RCM) and multiphoton microscopy (MPM) imaging; (2) performing region-of-interest measurement by scanning an area of the tissue during spectral acquisition. The developed method has been validated by measuring different micro-structures of in vivo human skin. Our results demonstrated great consistency between RCM images and confocal Raman spectra. The superior quality of the images and spectra allows us to derive blood flow velocity and blood glucose level. We believe this method is valuable for realizing accurate microscopic spectral measurement and have great potential to be adapted into clinic to achieve non-invasive measurement of important biological parameters.

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)0.001
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 score0.180
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.373
Teacher spread0.353 · 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

Citations36
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueScientific ReportsSame topicSpectroscopy Techniques in Biomedical and Chemical ResearchFrench-language works237,207