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Record W2534385355 · doi:10.1109/embc.2016.7591359

Towards a multi-wavelength spectroscopy platform for blood characterization and analysis

2016· article· en· W2534385355 on OpenAlexaff
Alireza Avakh Kisomi, Hamza Landari, Mounir Boukadoum, Amine Miled, Benoit Gosselin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversité du Québec à MontréalUniversité Laval
Fundersnot available
KeywordsAbsorbanceTransmittanceSpectroscopyWavelengthMaterials scienceOptoelectronicsMicrofluidicsVisible spectrumCharacterization (materials science)OpticsDetectorAnalyteComputer scienceChemistryNanotechnologyPhysicsChromatography

Abstract

fetched live from OpenAlex

Passing multiple light wavelengths through a blood sample makes it possible to investigate the presence and composition of cells, metabolytes and analytes such as blood cells, glucose, lactate and oxygen, providing valuable indications for diagnostic and health monitoring. In this paper, we present a test prototype of a multi-wavelength blood spectroscopy platform integrated with a microfluidic substrate to collect and convey blood samples through a series of micro-LEDs and a photo-detector. This spectroscopy platform is a proof of concept for a system that can collect absorbance and transmittance parameters of blood samples at several wavelengths within the visible and NIR spectrum, and transmit them wirelessly to a base station for real-time calculation and analysis. In-vitro measurements are performed with the proposed prototype with 5 channels covering wavelength from 400 nm to 940 nm A full characterization results of the proposed device are presented.

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)0.001
metaresearch head score (Gemma)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.017
GPT teacher head0.318
Teacher spread0.301 · 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
GenreMethods

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

Citations2
Published2016
Admission routes1
Has abstractyes

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