Multi‐parameter detection of diabetes mellitus on multichannel poly(dimethylsiloxane) analytical chips coupled with nanoband microelectrode arrays
Bibliographic record
Abstract
This article demonstrates a novel method for multi-parameter detection of diabetes mellitus. We propose an approach for fabrication of a 3-D metal films array with gold and copper using electroless deposition technique on PDMS substrate. The obtained PDMS slices containing metal films are superimposed layer by layer as a sandwich structure to form 3-D metal films array. The cross-sections of the array could be used as nanoband array electrochemical detectors, which are further integrated with a multichannel microchip for simultaneously detecting multi-parameter of diabetes mellitus, including glucose and metabonomics of diabetes containing aldehyde compounds (glyoxal and methylglyoxal) and short organic acids (lactate, urate and 2-hydroxybutyrate). Under optimized separation and detection conditions, glucose, aldehyde compounds and short organic acids respond linearly in the concentration range of 10-2000, 1-500 and 5-600 μM, with the LODs of 4, 0.5 and 3 μM for glucose, aldehyde compounds and short organic acids, respectively. This system is successfully employed to detect these compounds in serums. This study reveals that the electrochemical array detectors with different materials integrated with multichannel microchip provide a flexible and inexpensive approach for routine, simultaneous and direct detection of some metabolites in metabonomics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".