A Passive Mixing Microfluidic Urinary Albumin Chip for Chronic Kidney Disease Assessment
Bibliographic record
Abstract
Urinary albumin level is an important indicator of kidney damage in chronic kidney disease (CKD) but effective routine albumin detection tools are lacking. In this paper, we developed a low-cost and high accuracy microfluidic urinary albumin chip (UAL-Chip) to rapidly measure albumin in urine. The UAL-Chip offers three major features: (1) we incorporated a fluorescent reaction assay into the chip to improve the detection accuracy; (2) we constructed a passive and continuous mixing module in the chip that provides user-friendly operation and greater signal stability; (3) we applied a pressure-balancing strategy based on the immiscible oil coverage that achieves precise control of the sample-dye mixing ratio. We validated the UAL-Chip using both albumin standards and urine samples from 12 CKD patients and achieved an estimated limit of detection (LOD) of 5.2 μg/mL. The albumin levels in CKD patients' urine samples measured by UAL-Chip is consistent with the traditional well-plate measurements and clinical results. We foresee the potential of extending this passive and precise mixing platform to assess various disease biomarkers.
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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".