Quantitative Detection of Uric Acid by Electrochemical-Surface Enhanced Raman Spectroscopy Using a Multilayered Au/Ag Substrate
Bibliographic record
Abstract
Uric acid is a potential important biomarker in urine and serum samples for early diagnosis of preeclampsia, a life-threatening hypertensive disorder that occurs during pregnancy. Preeclampsia is a leading cause of maternal death, especially in developing nation settings. Quantitative detection of uric acid for rapid and routine diagnosis of early preeclampsia using electrochemical-surface enhanced Raman spectroscopy (EC-SERS) is presented herein. A uniform EC-SERS active Au/Ag substrate was developed by depositing nearly monodisperse gold and silver nanoparticles on the carbon working electrode surface of screen printed electrodes. The multilayered Au/Ag substrates were characterized by electron microscopy and used for quantitative detection of uric acid in 0.1 M NaF and synthetic urine at clinically relevant concentrations. These results showed a linear relationship between the EC-SERS signal intensity and the uric acid concentration. Relative errors calculated for selected concentrations were all within the Clinical Laboratory Improvement Amendments (CLIA) criterion for uric acid analysis (±17%). It is believed that routine and early diagnosis of disease could be possible through such quantitative detection of biomarkers in patient samples using this EC-SERS method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".