Photoacoustic Spectroscopy and Hyperglycemia in Experimental Type 1 Diabetes

Abstract

According to the World Health Organization, diabetes was the seventh leading cause of death in 2016. Long-term diabetes complications are associated with hyperglycemia. It is difficult to predict the beginning and evolution of those complications. The goal of the study was to evaluate the relationship between glycemia and blood spectroscopic variables in an experimental model of type 1 diabetes (streptozotocin model). Blood samples were taken weekly (10 weeks) from the tail of male Wistar rats with or without diabetes. Blood optical absorption spectra were obtained by means of photoacoustic spectroscopy. It was possible to estimate the time-course of blood characteristic peak ratios. The area under the curve of those peaks correlated with hyperglycemia. The evolution of the optical absorption at 450 nm, related to cytochrome p450, was obtained by using the phase-resolved method. The area under the curve of p450 correlated also with hyperglycemia. It is concluded that photoacoustic spectroscopy is a reliable technology to detect the effects of hyperglycemia on blood with possible applications in the study of long-term diabetes complications.

Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks

“…Meanwhile, a mini photoacoustic cell was designed, and the gelatin phantoms with varying glucose concentrations were used to verify the approximate linear relationship between photoacoustic spectra and absorption spectra. Cano [ 8 ] employed PAS to study the relationship between glycemia and blood photoacoustic peak-to-peak spectra during 10 weeks, and the phase-resolved method was utilized to obtain the evolution of the optical absorption related to the hyperglycemia at 450 nm. Zhang [ 9 ] proposed a novel “guide star” assisted photoacoustic method to noninvasively measure glucose, the sensitivity and accuracy of measuring glucose concentration was improved through optimizing optical path and combination of optical absorption and ultrasonic velocity.…”

“…The model achieved a prediction accuracy of 96.7%. Zhang [ 8 ] employed multivariate data analysis methods such as principal component regression (PCR) and partial least squares regression (PLSR) to extract the features most correlated with glucose concentration and establish the prediction models of BGC. Han [ 19 ] performed the noninvasive blood glucose detection based on near-infrared spectroscopy combined with linear PLSR and the nonlinear stacked auto-encoder deep neural network.…”

Optical Light Sources and Wavelengths within the Visible and Near-Infrared Range Using Photoacoustic Effects for Biomedical Applications

“…As the methods of expressing these absorption properties, there are absorption rates, absorption coefficients, molar extinction coefficients, and absorption spectrums [ 6 ]. Examples of these spectroscopic research results include studies on the optical properties of water [ 9 ], rat blood samples with induced hyperglycemia [ 10 ], and studies on the optical properties of human skin [ 11 ], which are important variables in research on photoacoustic technology.…”