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Record W1818178467 · doi:10.1039/c5an00996k

Naked-eye nanobiosensor for therapeutic drug monitoring of methotrexate

2015· article· en· W1818178467 on OpenAlexafffund
Hélène Yockell-Lelièvre, Natalia Bukar, Jacynthe L. Toulouse, Joelle N. Pelletier, Jean‐François Masson

Bibliographic record

VenueThe Analyst · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversité de Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaInstitut MérieuxCanada Foundation for Innovation
KeywordsMethotrexateTherapeutic drug monitoringNaked eyeDrugPharmacologyMedicineNanotechnologyChemistryMaterials scienceInternal medicineChromatography

Abstract

fetched live from OpenAlex

Sensing of methotrexate at clinically-relevant concentrations was achieved with a plasmon-coupling assay. In this assay, free methotrexate and folic acid Au nanoparticles competed for human dihydrofolate reductase (hDHFR)-functionalized Au nanoparticles (Au NP). The hDHFR-functionalized Au NPs were immobilized on a small glass sensor inserted in a portable 4-channel LSPR reader. This allowed rapid (minutes) and sensitive (nanomolar range) measurement of methotrexate concentration by means of total internal reflection plasmonic spectroscopy. The large bathochromic shifts of the plasmon-coupling assay led to striking colour changes visible to the naked eye for methotrexate at clinically-relevant concentrations. The results demonstrate the potential for therapeutic drug monitoring of a widely used chemotherapy agent, as assessed with the naked eye.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.333
Teacher spread0.297 · 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

Citations26
Published2015
Admission routes2
Has abstractyes

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