Nano-Level DL-Methionine Biosensing in Blood Serum and Urine Using CuO/AgO Modified Graphite Electrode
Bibliographic record
Abstract
DL-methionine (DLM) is a common precursor for some amino acids essential to human physiology, (e.g. homocysteine and cysteine). Its contribution to detoxification and regulation of cell aging as well as fat build-up makes it an essential ingredient in the body fluids. Changes in blood level DL-methionine concentration may not be detrimental to human health, however, its contribution to regulating homocysteine level may contribute to some metabolic disorder in diabetic patients with non-cardiovascular diseases. The current research report features a new approach developed towards fabricating a new CuO/AgO modified graphite pencil electrode for sensing in human blood serum and urine samples at the nanoscale limit of detection. The fabricated sensor was characterized by means of X-ray Photoelectron Spectroscopy, Raman spectroscopy and scanning electron microscopy while being used to detect the DLM analyte. Electrode reactions were monitored using cyclic voltammetric and electrochemical impedance spectroscopic methods. The Ag-induced catalytic activity between pH 7-10 was observed following oxidation of the analyte at the electrode surface in the presence of hydroxide ions. DLM detection was ascribed to the unique electroactive Ag and Cu sites on the sensor electrode. The efficiency of this sensor towards analyte sensing within these biological samples in the presence of potential interferences was also investigated. This study features the success in DLM sensing in the presence of multiple amino acid interferences in complex biological samples. With the emergence of complications involving some neuro and cerebrovascular diseases associated with residual methionine, this work presents a biomolecular means of sensing of DLM as a disease biomarker at the nanoscale. The lowest DLM detection limit (DL) in this study was in the nanomolar level; 50 and 60 nM DLM in the serum and urine, respectively.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".