An Electrochemical Biosensor Platform for Testing of Dehydroepiandrosterone 3‐Sulfate (DHEA−S) as a Model for Doping Materials
Bibliographic record
Abstract
Abstract Endogenous steroids such as dehydroepiandrosterone (DHEA) and dehydroepiandrosterone 3‐sulfate (DHEA−S) have commonly used as doping materials by athletes and to date novel techniques are needed for detection of these molecules. In this study, antibody‐based electrochemical biosensor has developed for testing level of the DHEA−S. For this aim, gold surfaces were initially modified with cysteamine (Cys) and then, DHEA−S antibody was immobilized on the surface via glutaraldehyde (GA) as a crosslinking agent. The stepwise modification of electrode surface was monitored by using various electrochemical techniques such as cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). Linear range was determined as 2.5–100 ng/mL DHEA−S using differential pulse voltammetry (DPV) technique, as well. Moreover, repeatability (±S.D.), coefficient of variation (%) and limit of detection (LOD) values were calculated as 0.033, 1.030 and 3.971, respectively. Also, DHEA−S in synthetic serum and urine samples were successfully determined with standard addition method and confirmation analysis were performed with liquid chromatography quadrupole‐time of flight mass spectrometry (LC‐QTOF/MS) system. The selectivity was studied with the addition of some interfering molecules (testosterone, bovine serum albumin (BSA), cholesterol, uric acid, lactic acid, codein (COD), ascorbic acid, DHEA). Consequently, this work is proposed as practical, innovative and cost‐effective technique that can be easily adapted for the miniaturized form for the analysis of other doping substances as well as DHEA−S for the future works.
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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) | 0.001 | 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) | 0.001 | 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".