Fluorescence sensing technologies for ophthalmic diagnosis

Personalized and point-of-care (POC) diagnoses are critical for ocular physiology and disease diagnosis. Real-time monitoring and continuous sampling abilities of tear fluid and user-friendliness have become the key characteristics for the applied ophthalmic techniques. Fluorescence technologies, as one of the most popular methods that can fulfill the requirements of clinical ophthalmic applications for optical sensing, have been raised and applied for tear sensing and diagnostic platforms in recent decades. Wearable sensors in this case have been increasingly developed for ocular diagnosis. Contact lenses, as one of the commercialized and popular tools for ocular dysfunction, have been developed as a platform for fluorescence sensing in tears diagnostics and real-time monitoring. Numbers of biochemical analytes have been examined through developed fluorescent contact lens sensors, including pH values, electrolytes, glucose, and enzymes. These sensors have been proven for monitoring ocular conditions, enhancing and detecting medical treatments, and tracking efficiency of related ophthalmic surgeries at POC settings. This review summarizes the applied ophthalmic fluorescence sensing technologies in tears for ocular diagnosis and monitoring. In addition, the cooperation of fabricated fluorescent sensor with mobile phone readout devices for diagnosing ocular diseases with specific biomarkers continuously is also discussed. Further perspectives for the developments and applications of fluorescent ocular sensing and diagnosing technologies are also provided

Laser Nanopatterning of Colored Ink Thin Films for Photonic Devices

Nanofabrication through conventional methods such as electron beam writing and photolithography is time-consuming, high cost, complex, and limited in terms of the materials which can be processed. Here, we present the development of a nanosecond Nd:YAG laser (532 nm, 220 mJ) in holographic Denisyuk reflection mode method for creating ablative nanopatterns from thin films of four ink colors (black, red, blue, and brown). We establish the use of ink as a recording medium in different colors and absorption ranges to rapidly produce optical nanostructures in 1D geometries. The gratings produced with four different types of ink had the same periodicity (840 nm); however, they produce distant wavelength dependent diffraction responses to monochromatic and broadband light. The nanostructures of gratings consisting of blue and red inks displayed high diffraction efficiency of certain wavelengths while the black and brown ink based gratings diffracted broadband light. These gratings have high potential to be used as low-cost photonic structures in wavelength-dependent optical filters. We anticipate that the rapid production of gratings based on different ink formulations can enable optics applications such as holographic displays in data storage, light trapping, security systems, and sensors

Holographic Point-of-Care Diagnostic Devices

Developing non-invasive and accurate diagnostics that are easily manufactured, robust and reusable will provide monitoring of high-risk individuals in any clinical or point-of-care environment, particularly in the developing world. There is currently no rapid, low-cost and generic sensor fabrication technique capable of producing narrow-band, uniform, reversible colorimetric readouts with a high-tuneability range. This thesis aims to present a theoretical and experimental basis for the rapid fabrication, optimisation and testing of holographic sensors for the quantification of pH, organic solvents, metal cations, and glucose in solutions. The sensing mechanism was computationally modelled to optimise its optical characteristics and predict the readouts. A single pulse of a laser (6 ns, 532 nm, 350 mJ) in holographic “Denisyuk” reflection mode allowed rapid production of sensors through silver-halide chemistry, in situ particle size reduction and photopolymerisation. The fabricated sensors consisted of off-axis Bragg diffraction gratings of ordered silver nanoparticles and localised refractive index changes in poly(2-hydroxyethyl methacrylate) and polyacrylamide films. The sensors exhibited reversible Bragg peak shifts, and diffracted the spectrum of narrow-band light over the wavelength range λpeak ≈ 500-1100 nm. The application of the holographic sensors was demonstrated by sensing pH in artificial urine over the physiological range (4.5-9.0), with a sensitivity of 48 nm/pH unit between pH 5.0 and 6.0. For sensing metal cations, a porphyrin derivative was synthesised to act as the crosslinker, the light absorbing material, the component of a diffraction grating, as well as the cation chelating agent. The sensor allowed reversible quantification of Cu2+ and Fe2+ ions (50 mM - 1 M) with a response time within 50 s. Clinical trials of a glucose sensor in the urine samples of diabetic patients demonstrated that the glucose sensor has an improved performance compared to a commercial high-throughput urinalysis device. The experimental sensitivity of the glucose sensor exhibited a limit of detection of 90 µM, and permitted diagnosis of glucosuria up to 350 mM. The sensor response was achieved within 5 min and the sensor could be reused about 400 times without compromising its accuracy. Holographic sensors were also tested in flake form, and integrated with paper-iron oxide composites, dyed filter and chromatography papers, and nitrocellulose-based test strips. Finally, a generic smartphone application was developed and tested to quantify colorimetric tests for both Android and iOS operating systems. The developed sensing platform and the smartphone application have implications for the development of low-cost, reusable and equipment-free point-of-care diagnostic devices

Deep learning-enabled technologies for bioimage analysis.

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases

Ophthalmic sensing technologies for ocular disease diagnostics

Point-of-care diagnosis and personalized treatments are critical in ocular physiology and disease. Continuous sampling of tear fluid for ocular diagnosis is a need for further exploration. Several techniques have been developed for possible ophthalmological applications, from traditional spectroscopies to wearable sensors. Contact lenses are commonly used devices for vision correction, as well as for other therapeutic and cosmetic purposes. They are increasingly being developed into ocular sensors, being used to sense and monitor biochemical analytes in tear fluid, ocular surface temperature, intraocular pressure, and pH value. These sensors have had success in detecting ocular conditions, optimizing pharmaceutical treatments, and tracking treatment efficacy in point-of-care settings. However, there is a paucity of new and effective instrumentation reported in ophthalmology. Hence, this review will summarize the applied ophthalmic technologies for ocular diagnostics and tear monitoring, including both conventional and biosensing technologies. Besides applications of smart readout devices for continuous monitoring, targeted biomarkers are also discussed for the convenience of diagnosis of various ocular diseases. A further discussion is also provided for future aspects and market requirements related to the commercialization of novel types of contact lens sensors

Quantitative brain-derived neurotrophic factor lateral flow assay for point-of-care detection of glaucoma

Glaucoma, a ruinous group of eye diseases with progressive degeneration of the optic nerve and vision loss, is the leading cause of irreversible blindness. Accurate and timely diagnosis of glaucoma is critical to promote secondary prevention and early disease-modifying therapies. Reliable, cheap, and rapid tests for measuring disease activities are highly required. Brain-derived neurotrophic factor (BDNF) plays an important role in maintaining the function and survival of the central nervous system. Decreased BDNF levels in tear fluid can be seen in glaucoma patients, which indicates that BDNF can be regarded as a novel biomarker for glaucoma. Conventional ELISA is the standard method to measure the BDNF level, but the multi-step operation and strict storage conditions limit its usage in point-of-care settings. Herein, a one-step and a portable glaucoma detection method was developed based on the lateral flow assay (LFA) to quantify the BDNF concentration in artificial tear fluids. The results of the LFA were analyzed by using a portable and low-cost system consisting of a smartphone camera and a dark readout box fabricated by 3D printing. The concentration of BDNF was quantified by analyzing the colorimetric intensity of the test line and the control line. This assay yields reliable quantitative results from 25 to 300 pg mL-1 with an experimental detection limit of 14.12 pg mL-1. The LFA shows a high selectivity for BDNF and high stability in different pH environments. It can be readily adapted for sensitive and quantitative testing of BDNF in a point-of-care setting. The BDNF LFA strip shows it has great potential to be used in early glaucoma detection

Identity, reputation and social interaction with an application to sequential voting

We analyze binary choices in a random utility model assuming that the agent's preferences are affected by conformism (with respect to the behavior of the society) and coherence (with respect to his identity). We apply the analysis to sequential voting when voters like to win

Measures of disease activity in glaucoma

Glaucoma is the leading cause of irreversible blindness globally which significantly affects the quality of life and has a substantial economic impact. Effective detective methods are necessary to identify glaucoma as early as possible. Regular eye examinations are important for detecting the disease early and preventing deterioration of vision and quality of life. Current methods of measuring disease activity are powerful in describing the functional and structural changes in glaucomatous eyes. However, there is still a need for a novel tool to detect glaucoma earlier and more accurately. Tear fluid biomarker analysis and new imaging technology provide novel surrogate endpoints of glaucoma. Artificial intelligence is a post-diagnostic tool that can analyse ophthalmic test results. A detail review of currently used clinical tests in glaucoma include intraocular pressure test, visual field test and optical coherence tomography are presented. The advanced technologies for glaucoma measurement which can identify specific disease characteristics, as well as the mechanism, performance and future perspectives of these devices are highlighted. Applications of AI in diagnosis and prediction in glaucoma are mentioned. With the development in imaging tools, sensor technologies and artificial intelligence, diagnostic evaluation of glaucoma must assess more variables to facilitate earlier diagnosis and management in the future