Label-free electrochemical detection of glycated hemoglobin (HbA1c) and C-reactive protein (CRP) to predict the maturation of coronary heart disease due to diabetes
Bibliographic record
Abstract
The pathophysiological link between diabetes and heightened propensity for the development of coronary heart disease (CHD) is well-established. Prevailing evidence confirms that small increases in low concentrations of high-sensitivity C reactive protein (hs-CRP) in the human body can determine the tendency of developing CHD. Additionally, glycated hemoglobin (HbA1c) is a well-recognized biomarker to evaluate diabetes progression. Given the positive correlation between diabetes and CHD, this research presents a notably unprecedented label-free electrochemical approach for the dual detection of %HbA1c regarding Total Hb and hs-CRP, facilitating early CHD prediction and cost-effective point-of-care diagnostics. Furthermore, a novel redox probe O-(4-Nitrophenylphosphoryl)choline (C11H17N2O6P) was used for the electrochemical detection of CRP, a method not documented in scientific literature before. The calibration curves demonstrate a limit of detection (LOD) of 5 mg/mL in PBS (pH 8) and 6 mg/mL in simulated blood (SB) for a linear range of 0–30 mg/mL of HbA1c. Conjointly, a LOD of 0.007 mg/mL and 0.008 mg/mL for measurement in PBS (pH 7.4) and SB are reported for a linear range of 0–0.05 mg/mL of CRP. The electrochemical systems presented could accurately quantify HbA1c and CRP in mixed samples, demonstrating reasonable specificity and practical applicability for complex biological samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".