Carbon Nitride Nanocomposite-Based Electrochemiluminescence Biosensor Platform for In Vitro Detection of Diabetic Cardiomyopathy Markers
Bibliographic record
Abstract
Diabetic cardiomyopathy (DCM) is a dysregulated metabolic condition linked with diabetes mellitus characterized by hyperglycemia, lactic acidosis, and oxidative stress. Monitoring these altered metabolisms is crucial for developing targeted therapeutic interventions, as they lead to structural and functional impairments in the heart, ultimately causing cardiac dysfunction. This cardiac dysfunction is mimicked using H9c2 rat cardiomyoblast cells, which are widely used as an in vitro model to mimic human cardiomyocytes, the muscle cells responsible for heart contraction and blood circulation. Herein, a water-soluble carboxylic acid-rich g-C 3 N 4 nanoparticle (CNNP)-based electrochemiluminescence (ECL) biosensor is developed for the sensitive detection of glucose, lactate, and H 2 O 2 from H9c2 cells to investigate metabolic dysfunction under hyperglycemic and oxidative stress conditions. The CNNPs were deposited onto fluorine-doped tin oxide (FTO) via electrophoretic deposition (EPD), resulting in a significant improvement in the electrocatalytic activity. This surface modification led to an approximately 2.4-fold increase in ECL signal performance relative to that of the unmodified FTO surface, demonstrating its potential for sensitive detection of various metabolites. The performance of the sensor was examined and validated for detecting H 2 O 2, glucose, and lactate released from H9c2 cells at 8 h intervals over a duration of 72 h. The sensor exhibited a wide dynamic linear range and low detection limits of 0.91 μM, 642 μM, and 135 μM, respectively. Compared to other existing approaches, this method provides improved qualitative and quantitative detection, along with a noninvasive strategy for investigating metabolic dysfunction in cardiomyocytes. The proposed biosensing platform elucidates insights into metabolic dysregulation induced by hyperglycemia and oxidative stress in cardiomyocytes and shows promising potential for therapeutic interventions in diabetes-associated cardiac dysfunction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".