Investigation of the effect of clinically relevant interferents on glucose monitoring using near-infrared spectroscopy

The artificial pancreas: two alternative approaches to achieve a fully closed-loop system with optimal glucose control

INTRODUCTION

Diabetes mellitus type 1 is a chronic disease that implies mandatory external insulin delivery. The patients must monitor their blood glucose levels and administer appropriate insulin boluses to keep their blood glucose within the desired range. It requires a lot of time and endeavour, and many patients struggle with suboptimal glucose control despite all their efforts.

MATERIALS AND METHODS

This narrative review combines existing knowledge with new discoveries from animal experiments.

DISCUSSION

In the last decade, artificial pancreas (AP) devices have been developed to improve glucose control and relieve patients of the constant burden of managing their disease. However, a feasible and fully automated AP is yet to be developed. The main challenges preventing the development of a true, subcutaneous (SC) AP system are the slow dynamics of SC glucose sensing and particularly the delay in effect on glucose levels after SC insulin infusions. We have previously published studies on using the intraperitoneal space for an AP; however, we further propose a novel and potentially disruptive way to utilize the vasodilative properties of glucagon in SC AP systems.

CONCLUSION

This narrative review presents two lesser-explored viable solutions for AP systems and discusses the potential for improvement toward a fully automated system: A) using the intraperitoneal approach for more rapid insulin absorption, and B) besides using glucagon to treat and prevent hypoglycemia, also administering micro-boluses of glucagon to increase the local SC blood flow, thereby accelerating SC insulin absorption and SC glucose sensor site dynamics.

Rapid Prediction of Mechanical Properties Based on the Chemical Components of Windmill Palm Fiber

During spinning, the chemical component content of natural fibers has a great influence on the mechanical properties. How to rapidly and accurately measure these properties has become the focus of the industry. In this work, a grey model (GM) for rapid and accurate prediction of the mechanical properties of windmill palm fiber (WPF) was established to explore the effect of chemical component content on the Young’s modulus. The chemical component content of cellulose, hemicellulose, and lignin in WPF was studied using near-infrared (NIR) spectroscopy, and an NIR prediction model was established, with the measured chemical values as the control. The value of R_C and R_CV were more than 0.9, while the values of RMSEC and RMSEP were less than 1, which reflected the excellent accuracy of the NIR model. External validation and a two-tailed t-test were used to evaluate the accuracy of the NIR model prediction results. The GM(1,4) model of WPF chemical components and the Young’s modulus was established. The model indicated that the increase in cellulose and lignin content could promote the increase in the Young’s modulus, while the increase in hemicellulose content inhibited it. The establishment of the two models provides a theoretical basis for evaluating whether WPF can be used in spinning, which is convenient for the selection of spinning fibers in practical application.

Performance improvement in a supercontinuum fiber-coupled system for near infrared absorption spectroscopy

Accurate, in-field-compatible, sensing based on near infrared spectroscopy (NIRS) requires development of instrumentation with low noise and long-term stability. Here, we present a fully fiber-optic spectroscopy setup using a supercontinuum source in the long-pulse regime (2 ns) and a balanced detector scheme to demonstrate high-accuracy NIRS-based sensing. The noise sources of the system are studied theoretically and experimentally. The relative intensity noise was reduced from typical values up to 6% to less than 0.1% by deploying a balanced detector and averaging. At well-balanced wavelengths, the system without transmission cells achieved a signal to noise ratio (SNR) above 70 dB, approaching the shot noise limit. With transmission cells and long-term measurements, the overall SNR was 55 dB. Glucose in physiological concentrations was measured as a model system, yielding a root mean square error of 4.8 mM, approaching the needed accuracy for physiological glucose monitoring.