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Record W2030180978 · doi:10.1142/s2339547814500125

Rapid ratiometric biomarker detection with topically applied SERS nanoparticles

2014· article· en· W2030180978 on OpenAlexaff
Yu Wang, Altaz Khan, Madhura Som, Danni Wang, Ye Chen, Steven Y. Leigh, Daphne Meza, Patrick Z. McVeigh, Brian C. Wilson, Jonathan Liu

Bibliographic record

VenueTECHNOLOGY · 2014
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institutes of HealthStony Brook University
KeywordsIn vivoNanotechnologyEx vivoBiomarkerNanoparticleRaman scatteringMolecular biomarkersMaterials scienceChemistryBiomedical engineeringRaman spectroscopyMedicineBiologyOpticsBiochemistry

Abstract

fetched live from OpenAlex

Multiplexed surface-enhanced Raman scattering (SERS) nanoparticles (NPs) offer the potential for rapid molecular phenotyping of tissues, thereby enabling accurate disease detection as well as patient stratification to guide personalized therapies or to monitor treatment outcomes. The clinical success of molecular diagnostics based on SERS NPs would be facilitated by the ability to accurately identify tissue biomarkers under time-constrained staining and detection conditions with a portable device. In vitro, ex vivo and in vivo experiments were performed to optimize the technology and protocols for the rapid detection (0.1-s integration time) of multiple cell-surface biomarkers with a miniature fiber-optic spectral-detection probe following a brief (5 min) topical application of SERS NPs on tissues. Furthermore, we demonstrate that the simultaneous detection and ratiometric quantification of targeted and nontargeted NPs allows for an unambiguous assessment of molecular expression that is insensitive to nonspecific variations in NP concentrations.

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)0.000
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 score0.111
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.207
Teacher spread0.196 · 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

Citations74
Published2014
Admission routes1
Has abstractyes

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