Rapid binary visual detection of oxalate in urine samples of urolithiasis patients via competitive recognition and distance reading test strips.
Piaopiao ChenLihang CenYue WangYunjin BaiTian ShiXianhua ChenPublished in: Journal of materials chemistry. B (2023)
Urolithiasis is a common disease with wide ranging effects, with oxalate stones being the most prevalent type. Existing clinical diagnostic methods rely on complex instruments and professionals, are difficult to distinguish between stone types, and have insufficient sensitivity. Moreover, high-sensitivity point-of-care testing (POCT) methods remain scarce. We constructed a rapid homogeneous dual fluorescence and binary visualization analysis system to diagnose oxalate urolithiasis because oxalate can efficiently reduce Cu 2+ to Cu + , which can be selectively competitively recognized by both calcein and cadmium telluride quantum dots (CdTe QDs). Under optimized conditions, the system exhibited high sensitivity to oxalate ranging from 10 pM to 10 nM within 3 min. Following that, visualized test strips of calcein and QDs were generated by inkjet printing; oxalate concentrations as low as 10 nM can be easily identified by reading the quenching distance on the strip. We then analyzed 66 clinical urine samples: 11 healthy, 10 oxalate-negative, and 45 oxalate-positive samples. The fluorescence and visual mode results were highly consistent with clinical computed tomography (CT) images and clinical diagnostics. Therefore, our analysis strategy has the potential to use POCT for the assessment of oxalate urolithiasis.
Keyphrases
- computed tomography
- quantum dots
- end stage renal disease
- energy transfer
- magnetic resonance
- ejection fraction
- deep learning
- working memory
- image quality
- photodynamic therapy
- single molecule
- chronic kidney disease
- dual energy
- peritoneal dialysis
- mass spectrometry
- machine learning
- sensitive detection
- particulate matter
- editorial comment
- tissue engineering
- urinary tract