Assessing oxygen saturation (sO ₂ ) remains challenging but is nonetheless necessary for understanding retinal metabolism. We and others previously achieved oximetry on major retinal vessels and measured the total retinal oxygen metabolic rate in rats using visible-light optical coherence tomography. Here we extend oximetry measurements to capillaries and investigate all three retinal vascular plexuses by amplifying and extracting the spectroscopic signal from each capillary segment under the guidance of optical coherence tomography (OCT) angiography. Using this approach, we measured capillary sO ₂ in the retinal circulation in rats, demonstrated reproducibility of the results, validated the measurements in superficial capillaries with known perfusion pathways, and determined sO ₂ responses to hypoxia and hyperoxia in the different retinal capillary beds. OCT capillary oximetry has the potential to provide new insights into the retinal circulation in the normal eye as well as in retinal vascular diseases.

An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

Organoids, derived from human induced pluripotent stem cells (hiPSCs), are intricate three-dimensional in vitro structures that mimic many key aspects of the complex morphology and functions of in vivo organs such as the retina and heart. Traditional histological methods, while crucial, often fall short in analyzing these dynamic structures due to their inherently static and destructive nature. In this study, we leveraged the capabilities of optical coherence tomography (OCT) for rapid, non-invasive imaging of both retinal, cerebral, and cardiac organoids. Complementing this, we developed a sophisticated deep learning approach to automatically segment the organoid tissues and their internal structures, such as hollows and chambers. Utilizing this advanced imaging and analysis platform, we quantitatively assessed critical parameters, including size, area, volume, and cardiac beating, offering a comprehensive live characterization and classification of the organoids. These findings provide profound insights into the differentiation and developmental processes of organoids, positioning quantitative OCT imaging as a potentially transformative tool for future organoid research.

Elevated intraocular pressure (IOP) is the best-characterized risk factor for glaucoma, the second leading cause of blindness worldwide,¹ and lowering IOP can preserve vision for many glaucoma patients.² However, despite the success of IOP-lowering drugs,³ the association of IOP with glaucoma and the mechanisms by which elevated IOP damages the retina as well as the optic nerve remain unclear and require further investigation.⁴

Current treatments targeting individual protein quality control pathways have limited efficacy in alleviating proteinopathies, highlighting the prerequisite for a common druggable target capable of global proteostasis modulation. Building upon our prior research establishing nuclear speckles as pivotal membrane-less organelles for transcriptional control of proteostasis, we aim to alleviate proteinopathies through nuclear speckle rehabilitation. We identify pyrvinium pamoate as a nuclear speckle rehabilitator that enhances protein quality control gene expression and suppresses YAP1 transcriptional activity via decreasing the surface/interfacial tension of nuclear speckle condensates through interaction with the intrinsically disordered region of nuclear speckle scaffold protein SON. In pre-clinical models, nanomolar pyrvinium pamoate protects against retinal degeneration and tauopathy mainly by promoting autophagy and ubiquitin-proteasome activity in a SON-dependent manner without causing stress. Aberrant nuclear speckle morphology, reduced protein quality control and increased YAP1 activity are observed in human tauopathies. Our study provides proof-of-principle of targeting nuclear speckles to ameliorate proteinopathies.

Fiber optic sensors are used for a large variety of sensing applications, including security applications and the monitoring of bridges, dams, and pipelines. We propose an algorithm that can achieve highly accurate and robust detection of multiple intrusions over distributed localizations (distributed sensing) in the Sagnac fiber sensing system. This distributed-sensing algorithm involves the application of the fast Fourier transform to the linear spectrum of the phase difference signals resulting from the intrusion. The distances or localizations for intrusions occurring at different places correspond to different “response” peaks, which can be conveniently evaluated in the final localization chart. The fundamental theory underlying the algorithm is presented, and its efficacy is demonstrated via a series of experiments with a 130-km long sensing fiber. The localization-sensing performance of our algorithm, with a minimum standard deviation of 28 m for 23 intrusions at same position, shows high robustness. We believe that our approach can significantly contribute to the development of fiber-optic sensing.