Sensitive Electrochemical Analysis of Hydroxyproline in Achilles Tendon Collagen and Human Urine
Bibliographic record
Abstract
L-hydroxyproline (Hyp) is one of the significant amino acids present in connective tissue proteins such as collagen, elastin, and gelatin. The quantitative analysis of Hyp levels in bodily fluids is critical to assist with diagnosing diseases and early treatments. In the present study, for the first time, we report on a facile electrochemical method for the detection of Hyp using gold nanoparticles (AuNPs), which were electrochemically deposited on a glassy carbon electrode (GCE). The electrochemical behavior of the AuNPs/GCE for the oxidation of Hyp was examined using cyclic voltammetry, demonstrating higher electrocatalytic activity in contrast to GCE and bulk Au electrodes. Additionally, the mechanism for the electrochemical oxidation of Hyp was investigated using in situ Fourier transform infrared (FT-IR) spectroscopy. Moreover, the electrochemical sensing performance of the AuNPs was investigated using differential pulse voltammetry (DPV), exhibiting a low limit of detection (0.026 mM) and high sensitivity (8.5 μA (mM cm2)−1). The interference of other amino acids present in collagen and urine has been further tested, demonstrating high selectivity and good reproducibility. The novel electrochemical sensing approach described in the present study may lead to a facile non-enzymatic technique for the sensitive detection of Hyp, a significant biomarker.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".