Diabetes mellitus is one of the most common and severe threats towards human health. According to the WHO 2016 Global Report on Diabetes, an estimated 422 million adults were living with diabetes in 2014, compared to 108 million in 1980. The global prevalence (age-standardized) of diabetes has nearly doubled since 1980, rising from 4.7% to 8.5% in the adult population. Currently, most patients rely on the “finger-stick” methods to measure blood glucose levels. However, this method is painful, invasive and inconvenient. These drawbacks resulted in a low patient compliance and often leads to poor results in glucose controls.
In contrast, monitoring glucose levels in human body liquids (such as saliva and tears) using fluorescent chemo-sensors that have a high affinity for glucose possesses the inherent advantages of non-invasive, simple to use. By combining chemo-coordination and fluorescence detection, a series of fluorescent sensors have been synthesized and applied into glucose sensing over the past two decades. However, their sensitivity and selectively to glucose remain as a bottleneck of this technology.
We aim to address these limitations via computation-guided molecular design. The computational studies will help to optimize the compound structures and forecast the coordination of fluorescent sensors and glucose. Subsequent synthesis and validation will further enhance our understanding of this detection process and help to improve our sensor design.
Interested students should contact Dr. Liu Xiaogang (firstname.lastname@example.org) with their recent CVs. Students are also encouraged to propose their research projects, within the umbrella of fluorescent technologies.