A Quantum Dot-Based FLIM Glucose Nanosensor

Non-invasive screening of bladder cancer using digital microfluidics and FLIM technology combined with deep learning

Non-invasive screening for bladder cancer is crucial for treatment and postoperative follow-up. This study combines digital microfluidics (DMF) technology with fluorescence lifetime imaging microscopy (FLIM) for urine analysis and introduces a novel non-invasive bladder cancer screening technique. Initially, the DMF was utilized to perform preliminary screening and enrichment of urine exfoliated cells from 54 participants, followed by cell staining and FLIM analysis to assess the viscosity of the intracellular microenvironment. Subsequently, a deep learning residual convolutional neural network was employed to automatically classify FLIM images, achieving a three-class prediction of high-risk (malignant), low-risk (benign), and minimal risk (normal) categories. The results demonstrated a high consistency with pathological diagnosis, with an accuracy of 91% and a precision of 93%. Notably, the method is sensitive for both high-grade and low-grade bladder cancer cases. This highly accurate non-invasive screening method presents a promising approach for bladder cancer screening with significant clinical application potential.

A Potentially Versatile Enzyme Sensor Platform: Enzyme-Loaded, Tagged, Porous Polymeric Nanocapsules

Enzymes are essential to life and indispensable in a wide range of industries (food, pharmaceutical, medical, biosensing, etc.); however, a significant shortcoming of these fragile biological catalysts is their poor stability. To address this challenge, a variety of immobilization methods have been described to enhance the enzyme's stability. These immobilization methods generally are specific to an individual enzyme or optimal for a particular application. The aim of this study is to explore the utility of porous, indicator moiety-tagged, polymeric nanocapsules (NCs) for the encapsulation of enzymes and measurement of the enzyme's substrate. As a model enzyme, glucose oxidase (GOx) is used. The GOx enzyme-loaded, fluorophore-tagged NCs were synthesized by using self-assembled surfactant vesicle templates. To show that the biological activity of GOx is preserved during entrapment, the rate of the GOx enzyme catalyzed reaction was measured. To evaluate the protective features of the porous NCs, the encapsulated GOx enzyme activity was followed in the presence of hydrolytic enzymes. During the encapsulation of GOx and the purification of the GOx-loaded NCs, the GOx activity decayed less than 10%, and up to 30% of the encapsulated GOx activity could be retained for 3-5 days in the presence of hydrolytic enzymes. In support of the potentially unique advantages of the enzyme-loaded NCs, as a proof-of-concept example, the fluorophore-tagged, GOx-loaded NCs were used for the determination of glucose in the concentration range between 18 and 162 mg/dL and for imaging the distribution of glucose concentration in imaging experiments.

FLIM as a Promising Tool for Cancer Diagnosis and Treatment Monitoring

Fluorescence lifetime imaging microscopy (FLIM) has been rapidly developed over the past 30 years and widely applied in biomedical engineering. Recent progress in fluorophore-dyed probe design has widened the application prospects of fluorescence. Because fluorescence lifetime is sensitive to microenvironments and molecule alterations, FLIM is promising for the detection of pathological conditions. Current cancer-related FLIM applications can be divided into three main categories: (i) FLIM with autofluorescence molecules in or out of a cell, especially with reduced form of nicotinamide adenine dinucleotide, and flavin adenine dinucleotide for cellular metabolism research; (ii) FLIM with Förster resonance energy transfer for monitoring protein interactions; and (iii) FLIM with fluorophore-dyed probes for specific aberration detection. Advancements in nanomaterial production and efficient calculation systems, as well as novel cancer biomarker discoveries, have promoted FLIM optimization, offering more opportunities for medical research and applications to cancer diagnosis and treatment monitoring. This review summarizes cutting-edge researches from 2015 to 2020 on cancer-related FLIM applications and the potential of FLIM for future cancer diagnosis methods and anti-cancer therapy development. We also highlight current challenges and provide perspectives for further investigation.