A Multiplexed and In Situ Self‐Calibration Biosensor Integrated System for Metabolic Health Analysis in Human Urine
Bibliographic record
Abstract
Abstract Metabolic health is a major global health challenge associated with increased risks of disability, cancers, premature deaths, and a negative impact on patient quality of life. Precise monitoring of biomarkers in human urine, including metabolites and electrolytes, provides crucial guidance for treatment. While rich impurities in urine will induce cross‐interference of the biosensors array, making continuous and reliable analysis of multiple biomarkers challenging. To address these issues, a multiplexed and in situ self‐calibrated biosensing system (MSSCBS) is proposed with remarkably high stability and sensitivity in untreated urine samples. These desirable characteristics are achieved through the design of multiple electrochemical channels with self‐assembled nanostructure and an in situ bio‐signals self‐calibration method of dynamic normalized least mean squares. MSSCBS delivers reliable sensitivities (e.g., 0.04 µA/µ m for uric acid), a wide linear range (e.g., 0.2–23.6 m m for glucose), and long‐term stability (>20 days) of urinary impurities. Combination with a customized circuit for signal processing and wireless data transmission to mobiles supports convenient and long‐term continuous monitoring of urine. This study validates MSSCBS's usability in assessing metabolic states in healthy participants and patients with diabetes, kidney disease, showing the potential of at‐home biosensing platform for healthcare applications.
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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 | 0.001 | 0.001 |
| 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".