Development of an electrochemical surface‐enhanced Raman spectroscopic biosensor for the direct detection of glutathione
Bibliographic record
Abstract
Abstract Glutathione is an important biological free radical scavenger, aiding in the prevention of oxidative stress in living organisms. A decrease in glutathione concentration in cells or bodily fluids is associated with serious health issues including certain cancers and Alzheimer's disease. Rapid detection and quantification of salivary glutathione could play a crucial role in healthcare monitoring as well as in therapeutic progress. This paper reports on the development of a direct electrochemical surface‐enhanced Raman spectroscopic (EC‐SERS) method for the monitoring of glutathione deposited via drop casting from a prepared aqueous stock solution and measured in 0.1‐M sodium fluoride and in artificial saliva. The detection platform for this study is constructed by modifying the working electrode of a carbon screen‐printed electrode with silver nanoparticles of approximately 30 nm in diameter, followed by potassium chloride treatment to remove interfering citrate anions prior to analysis. The coupling of SERS and an applied electrochemical potential resulted in a significantly enhanced spectrum for glutathione compared with those previously reported. Vibrational mode assignment confirmed that glutathione was indeed in close proximity to the surface of the working electrode and had varying anchor points as the potential was stepped anodically. Quantitative analysis of glutathione in artificial saliva showed that the 653 cm −1 marker peak intensity varies linearly ( R 2 = 0.990) over the concentration of 0.005–1.00 mM on the modified electrode surface. The signal limit of detection of glutathione using EC‐SERS in artificial saliva was determined to be 5 μM. This direct and rapid EC‐SERS detection platform is promising for point‐of‐care diagnostics.
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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 | 0.001 | 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".