METHODOLOGY/PRINCIPAL FINDINGS: Photos of urine samples were taken in a customized photo booth, then processed using Adobe Photoshop to index urine colour into the red, green, and blue (RGB) colour space and assigned a unique RGB value. The RGB values were then correlated with patients' clinical and laboratory hydration indices using Pearson's correlation and multiple linear regression. There were strong correlations between urine osmolality and the RGB of urine colour, with r = -0.701 (red), r = -0.741 (green), and r = -0.761 (blue) (all p-value <0.05). There were strong correlations between urine specific gravity and the RGB of urine colour, with r = -0.759 (red), r = -0.785 (green), and r = -0.820 (blue) (all p-value <0.05). The blue component had the highest correlations with urine specific gravity and urine osmolality. There were moderate correlations between RGB components and serum urea, at r = -0.338 (red), -0.329 (green), -0.360 (blue). In terms of urine biochemical parameters linked to dehydration, multiple linear regression studies showed that the green colourimetry code was predictive of urine osmolality (β coefficient -0.082, p-value <0.001) while the blue colourimetry code was predictive of urine specific gravity (β coefficient -2,946.255, p-value 0.007).
CONCLUSIONS/SIGNIFICANCE: Urine colourimetry using mobile phones was highly correlated with the hydration status of dengue patients, making it a potentially useful hydration status tool.
METHODS: Four different solvent extracts of OS, namely aqueous, ethanolic, 50% aqueous ethanolic and methanolic, at a dose of 500 mg/kg body weight (bw) were orally administered for 14 days to diabetic rats induced via intraperitoneal injection of 60 mg/kg bw STZ. NMR metabolomics approach using pattern recognition combined with multivariate statistical analysis was applied in the rat urine to study the resulted metabolic perturbations.
RESULTS: OS aqueous extract (OSAE) caused a reversal of DM comparable to that of 10 mg/kg bw glibenclamide. A total of 15 urinary metabolites, which levels changed significantly upon treatment were identified as the biomarkers of OSAE in diabetes. A systematic metabolic pathways analysis identified that OSAE contributed to the antidiabetic activity mainly through regulating the tricarboxylic acid cycle, glycolysis/gluconeogenesis, lipid and amino acid metabolism.
CONCLUSIONS: The results of this study validated the ethnopharmacological use of OS in diabetes and unveiled the biochemical and metabolic mechanisms involved.