The model was independently tested and successfully identified micro- and macro albuminuria with sensitivity of >91 percent and specificity of >99 percent. We applied support vector machine classification and regression models to extract the diagnostic information contained within the infrared spectra. Visual inspection of the spectra indicated a strong correlation between the amide bands from proteins and the total protein content of the samples. We measured the urine protein extract – obtained by ultrafiltration – from 22 controls and 155 diabetic patients with normo-, micro-, and macro-albuminuria. Could you please share some details of your research? Our instrument is the size of a shoebox, and provides accurate results in a few minutes at a reasonable per test cost. These techniques should be simple, fast, and portable enough to be used in close proximity to the patient in pharmacies or community clinics. And so, analytical tools that can quantify proteins at low levels are fundamental for the early detection of DKD. The presence of specific proteins in urine is indicative of a wide range of diseases – including DKD and chronic kidney disease. Our work focuses on the development of an infrared-based spectroscopic method that combines machine learning for the quantification and characterization of proteins in urine. Could you please introduce your work and its importance? To gain more perspective, we asked the lead authors of the study – David Pérez-Guaita, Bayden Wood, and Karin Jandeleit-Dahm – a few questions. Recently, a group of Australian researchers have combined ATR-FTIR spectroscopy with machine learning to rapidly profile proteins – such as albumin – in urine to detect DKD in its early stages (1). Current routine screening methods not only have point of care limitations, but only identify patients in the late disease stages. Albumin excretion in urine is a common biomarker of diabetic kidney disease (DKD).
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