Visual electrophysiology measurements are important for ophthalmic diagnostic testing. Electrodes with combined optical transparency and softness are highly desirable, and sometimes indispensable for many ocular electrophysiology measurements. Here we report the fabrication of soft graphene contact lens electrodes (GRACEs) with broad-spectrum optical transparency, and their application in conformal, full-cornea recording of electroretinography (ERG) from cynomolgus monkeys. The GRACEs give higher signal amplitude than conventional ERG electrodes in recordings of various full-field ERG responses. High-quality topographic mapping of multifocal ERG under simultaneous fundus monitoring is realized. A conformal and tight interface between the GRACEs and cornea is revealed. Neither corneal irritation nor abnormal behavior of the animals is observed after ERG measurements with GRACEs. Furthermore, spatially resolved ERG recordings on rabbits with graphene multi-electrode array reveal a stronger signal at the central cornea than the periphery. These results demonstrate the unique capabilities of the graphene-based electrodes for in vivo visual electrophysiology studies.

An electronic “smart” contact lens device with high gas permeability and optical transparency, as well as mechanical compliance and robustness, offers daily wear capability in eye interfacing and can have broad applications ranging from ocular diagnosis to augmented reality. Most existing contact lens electronics utilize gas-impermeable substrates, electronic components, and interfacial adhesion layers, which impedes them from applications requiring continuous daily wear. Here we report on the design and fabrication of an eye interfacing device with a commercial ocular contact lens as the substrate, metal-coated nanofiber mesh as the conductor, and in situ electrochemically deposited poly­(3,4-ethylenedioxythiophene) (PEDOT) /poly­(styrene sulfonate) (PSS) as the adhesion material. This hydrogel contact lens device shows high gas permeability, wettability, and level of hydration, in addition to excellent optical transparency, mechanical compliance, and robustness. Using a rabbit model, we found that the animals wearing these hydrogel contact lens devices continuously for 12 hours showed a level of corneal fluorescein staining comparable to those wearing pure hydrogel contact lenses for same period of time, with no obvious corneal abrasion or irritation, indicating their high level of safety for continuous daily wear. Finally, full-field electroretinogram (ERG) recordings on rabbits were carried out to demonstrate the functionality of this device. We believe that the strategy of integrating nanofiber mesh-based electronic components demonstrated here can offer a general platform for hydrogel electronics with the advantages of preserving the physiological and mechanical properties of the hydrogel, thus enabling seamless integration with biological tissues and providing various wearable or implantable sensors with improved biocompatibility for health monitoring or medical treatment.

Background Axial myopia is the most common type of myopia. However, due to the high incidence of myopia in Chinese children, few studies estimating the physiological elongation of the ocular axial length (AL), which does not cause myopia progression and differs from the non-physiological elongation of AL, have been conducted. The purpose of our study was to construct a machine learning (ML)-based model for estimating the physiological elongation of AL in a sample of Chinese school-aged myopic children. Methods In total, 1011 myopic children aged 6 to 18 years participated in this study. Cross-sectional datasets were used to optimize the ML algorithms. The input variables included age, sex, central corneal thickness (CCT), spherical equivalent refractive error (SER), mean K reading (K-mean), and white-to-white corneal diameter (WTW). The output variable was AL. A 5-fold cross-validation scheme was used to randomly divide all data into 5 groups, including 4 groups used as training data and one group used as validation data. Six types of ML algorithms were implemented in our models. The best-performing algorithm was applied to predict AL, and estimates of the physiological elongation of AL were obtained as the partial derivatives of ALpredicted-age curves based on an unchanged SER value with increasing age. Results Among the six algorithms, the robust linear regression model was the best model for predicting AL, with a R2 value of 0.87 and relatively minimal averaged errors between the predicted AL and true AL. Based on the partial derivatives of the ALpredicted-age curves, the estimated physiological AL elongation varied from 0.010 to 0.116 mm/year in male subjects and 0.003 to 0.110 mm/year in female subjects and was influenced by age, SER and K-mean. According to the model, the physiological elongation of AL linearly decreased with increasing age and was negatively correlated with the SER and the K-mean. Conclusions The physiological elongation of the AL is rarely recorded in clinical data in China. In cases of unavailable clinical data, an ML algorithm could provide practitioners a reasonable model that can be used to estimate the physiological elongation of AL, which is especially useful when monitoring myopia progression in orthokeratology lens wearers.