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Record W2048361201 · doi:10.1039/c2cp42596c

Electrochemical-surface enhanced Raman spectroscopy (E-SERS) of uric acid: a potential rapid diagnostic method for early preeclampsia detection

2012· article· en· W2048361201 on OpenAlexaff
Barbara L. Goodall, Ashley M. Robinson, Christa L. Brosseau

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2012
Typearticle
Languageen
FieldMedicine
TopicPregnancy and preeclampsia studies
Canadian institutionsSaint Mary's University
FundersU.S. Department of Homeland Security
KeywordsUric acidRaman spectroscopyPreeclampsiaSurface-enhanced Raman spectroscopyUrineChemistryElectrochemistryRaman scatteringBiochemistryPregnancyBiologyOptics

Abstract

fetched live from OpenAlex

An increased level of uric acid in urine and plasma is indicative of the development of preeclampsia, a hypertensive disorder that can occur during pregnancy. The preliminary steps towards developing a rapid tool for early diagnosis of preeclampsia using electrochemical SERS (E-SERS) for the detection of uric acid in urine are presented herein. Characterization of the uric acid species was completed using cyclic voltammetry, UV spectroscopy, Raman spectroscopy and electrochemical surface-enhanced Raman spectroscopy (E-SERS). E-SERS was capable of easily detecting uric acid directly at concentrations <1 mM in urine simulant, without the need for costly enzymes and bulky equipment, and thus demonstrates promise as a rapid point-of-care diagnostic tool for detection of early onset preeclampsia in developing nation settings.

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 categoriesMeta-epidemiology (narrow)
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 score0.161
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.278
Teacher spread0.267 · 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.

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

Citations78
Published2012
Admission routes1
Has abstractyes

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