An Enzyme‐Composite Sweat Sensor for the Detection of Multiplexed Diabetic Nephropathy Biomarkers
Bibliographic record
Abstract
Abstract Conventional approaches to diagnosing diabetic nephropathy (DN) typically involve blood tests, imaging or biopsy. However, these methods are invasive and require large, specialized equipment, making them unsuitable for frequent monitoring. As precision medicine advances, there is an increasing demand for diagnostic tools that are simple and accessible. Here, a novel sensing system is introduced to simultaneously detect three key DN biomarkers in sweat: glucose (Glu), uric acid (UA), and K + . The system employs a binary composite electrode with high conductivity, large surface area, and abundant active sites, ensuring excellent electrochemical performance and sensitivity for trace detection.To ensure specificity, glucose oxidase (GOx), urate oxidase (UAOx), and K + ‐selective carriers are incorporated. Glu and UA are detected through GOx‐ and UAOx‐catalyzed amperometric reactions, whereas K + levels are determined through open‐circuit potential based on selective ion recognition. To enhance enzyme stability, GOx and UAOx are encapsulated within the microporous covalent organic framework. This universal enzyme protection strategy improves enzyme stability and bioactivity by limiting molecular mobility and shielding the enzymes from environmental stress. The system is successfully applied to analyze sweat samples collected from volunteers, demonstrating its strong potential for routine non‐invasive monitoring of renal health in individuals with diabetes.
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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) | 0.001 | 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".