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Record W2602551881 · doi:10.1021/acs.jpcc.7b01179

Electrochemical-Surface Enhanced Raman Spectroscopic (EC-SERS) Study of 6-Thiouric Acid: A Metabolite of the Chemotherapy Drug Azathioprine

2017· article· en· W2602551881 on OpenAlexafffund
Bradley H. C. Greene, Dalal S. Alhatab, Cory C. Pye, Christa L. Brosseau

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsSaint Mary's University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationNova Scotia Research Innovation TrustMinistry of Higher Education and Scientific Research
KeywordsMetaboliteUrineElectrochemistryRaman spectroscopyChemistryDrugElectrodeAdsorptionPharmacologyMedicineBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

6-Thiouric acid (6-TUA) has the potential to be an important biomarker for the action of 6-mercaptopurine (6-MP), an immunosuppressive drug used in patients suffering from acute lymphoblastic leukemia (ALL). 6-TUA, a nonactive metabolite of 6-MP, is excreted in the urine, and routine monitoring of this metabolite can be useful in assessing the efficacy of 6-MP for immune system suppression in patients who have undergone stem cell replacement. In this work, electrochemical surface-enhanced Raman spectroscopy (EC-SERS) is used for the first time to study the adsorption and electrochemical behavior of 6-TUA at a nanostructured silver electrode surface, in both 0.1 M NaF and synthetic urine as supporting electrolytes. In addition, ab initio calculations were completed in an effort to understand the adsorption behavior. It was found that EC-SERS provided excellent signal for 6-TUA down to μM concentrations in synthetic urine and highlights the future potential of EC-SERS for rapid detection of important urine biomarkers at the patient point-of-care.

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 score0.001
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.005
GPT teacher head0.271
Teacher spread0.266 · 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

Citations73
Published2017
Admission routes2
Has abstractyes

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