Enzyme-coupled polymersome microreactor for point-of-care blood urea sensing
Bibliographic record
Abstract
Vesicular microreactors have gained broad interest in drug delivery, biodetoxification, and green chemistry. We have expanded their use to diagnostic applications by leveraging the selective permeability of the vesicular membrane. In the past, we developed a transmembrane pH-gradient polymeric microreactor to sense ammonia, a widely used biomarker in liver disease. After diffusing across the membrane, ammonia is protonated in the acidic lumen of the polymersome. The pH increase is detected by a pH-sensitive near-infrared fluorophore in the lumen. The high ammonia selectivity of this polymersome microreactor relies on the highly hydrophobic membrane of poly(styrene)-b-poly(ethylene glycol) polymersomes. In this study, we are combining ammonia-sensing polymersomes with a highly selective ammonia-generating enzyme, urease, to expand the analyte space and enable urea sensing in whole blood. Blood urea is a widely used biomarker in kidney disease, notably to determine the adequate duration of hemodialysis. In clinical routine, blood urea measurements are performed in centralized laboratories. A bedside test would enable real-time urea monitoring during hemodialysis with the potential to reduce the risk of over- and underdialysis. We first optimized the assay components and parameters (PS-b-PEG polymersomes, pH-sensitive dye, urease, incubation time and temperature) to optimize the sensor response and kinetics in phosphate buffer at pH 7.4. The urease-coupled polymersome assay was subsequently tested in urea-spiked fresh mouse blood. We observed a rapid and linear response at clinically relevant urea concentrations. Based on these results, the assay was tested in an IRB-approved study in healthy volunteers. In fresh capillary blood, the assay was able to discriminate three clinically relevant spiked urea concentrations in under one minute. Therefore, coupling the urease-catalyzed hydrolysis of urea with ammonia-sensing polymersomes yielded a blood urea assay with high selectivity and a rapid response at clinically relevant concentrations. These results highlight the potential of combining a highly selective ammonia-generating enzyme with ammonia-sensing polymersome microreactors for blood metabolite sensing at the point-of-care.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".