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Record W2053461601 · doi:10.1002/jbio.200910039

Flow cytometry with gold nanoparticles and their clusters as scattering contrast agents: FDTD simulation of light–cell interaction

2009· article· en· W2053461601 on OpenAlexaff
Stoyan Tanev, Wenbo Sun, James Pond, Valery V. Tuchin, Vladimir P. Zharov

Bibliographic record

VenueJournal of Biophotonics · 2009
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsLumerical Solutions (Canada)Carleton University
FundersNational Institute of Biomedical Imaging and BioengineeringNational Cancer Institute
KeywordsFinite-difference time-domain methodFlow cytometryContrast (vision)NanoparticleLight scatteringColloidal goldScatteringFlow (mathematics)Materials scienceNanotechnologyOpticsChemistryPhysicsMechanicsMedicine

Abstract

fetched live from OpenAlex

The formulation of the finite-difference time-domain (FDTD) approach is presented in the framework of its potential applications to in-vivo flow cytometry based on light scattering. The consideration is focused on comparison of light scattering by a single biological cell alone in controlled refractive-index matching conditions and by cells labeled by gold nanoparticles. The optical schematics including phase contrast (OPCM) microscopy as a prospective modality for in-vivo flow cytometry is also analyzed. The validation of the FDTD approach for the simulation of flow cytometry may open up a new avenue in the development of advanced cytometric techniques based on scattering effects from nanoscale targets.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.000
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 head0.009
GPT teacher head0.217
Teacher spread0.209 · 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 designSimulation or modeling
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

Citations41
Published2009
Admission routes1
Has abstractyes

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