The nanofiber materials of three-dimensional spatial structure synthesized by electrospun have the characteristics of high porosity, high specific surface area, and high similarity to the natural extracellular matrix (ECM) of the human body. These are beneficial for absorbing wound exudate, effectively blocking the invasion of external bacteria, and promoting cell respiration and proliferation, which provides an ideal microenvironment for wound healing. Moreover, electrospun nanofiber dressings can flexibly load drugs according to the condition of the wound, further promoting wound healing. Recently, electrospun nanofiber materials have shown promising application prospects as medical dressings in clinical. Based on current research, this article reviewed the development history of wound dressings and the principles of electrospun technology. Subsequently, based on the types of base material, polymer-based electrospun nanofiber dressing and electrospun nanofiber dressing containing drug-releasing factors were discussed. Furthermore, the application of electrospun nanofiber dressing on skin tissue is highlighted. This review aims to provide a detailed overview of the current research on electrospun nanomaterials for wound healing, addressing challenges and suggesting future research directions to advance the field of electrospun dressings in wound healing.

Deep learning–augmented reflectance microscopy enables label-free multiplexed cytometry.

We report herein the first visible light optical coherence tomography angiography (vis-OCTA) for human retinal imaging. Compared to the existing vis-OCT systems, we devised a spectrometer with a narrower bandwidth to increase the spectral power density for OCTA imaging, while retaining the major spectral contrast in the blood. We achieved a 100 kHz A-line rate, the fastest acquisition speed reported so far for human retinal vis-OCT. We rigorously optimized the imaging protocol such that a single acquisition took < 6 seconds with a field of view (FOV) of 3×7.8 mm². The angiography enables accurate localization of microvasculature down to the capillary level and thus enables oximetry at vessels < 100 µm in diameter. We demonstrated microvascular hemoglobin oxygen saturation (sO₂) at the feeding and draining vessels at the perifoveal region. The longitudinal repeatability was assessed by < 5% coefficient of variation (CV). The unique capabilities of our vis-OCTA system may allow studies on the role of microvascular oxygen in various retinal pathologies.

Deep learning, particularly convolutional neural networks (CNNs), has revolutionized endoscopic image processing, significantly enhancing the efficiency and accuracy of disease diagnosis through its exceptional ability to extract features and classify complex patterns. This technology automates medical image analysis, alleviating the workload of physicians and enabling a more focused and personalized approach to patient care. However, despite these remarkable achievements, there are still opportunities to further optimize deep learning models for endoscopic image analysis, including addressing limitations such as the requirement for large annotated datasets and the challenge of achieving higher diagnostic precision, particularly for rare or subtle pathologies. This review comprehensively examines the profound impact of deep learning on endoscopic image processing, highlighting its current strengths and limitations. It also explores potential future directions for research and development, outlining strategies to overcome existing challenges and facilitate the integration of deep learning into clinical practice. Ultimately, the goal is to contribute to the ongoing advancement of medical imaging technologies, leading to more accurate, personalized, and optimized medical care for patients.

Elastic light scattering spectroscopy (ELSS) has been proven a powerful method in measuring tissue structures with exquisite nanoscale sensitivity. However, ELSS contrast in the living human retina has been relatively underexplored, primarily due to the lack of imaging tools with a large spectral bandwidth. Here, we report a simple all fiber-based setup to implement dual-channel visible and near infrared (NIR) optical coherence tomography (vnOCT) for human retinal imaging, bridging over a 300nm spectral gap. Remarkably, the fiber components in our vnOCT system support single-mode propagation for both visible and NIR light, both of which maintain excellent interference efficiencies with fringe visibility of 97% and 90%, respectively. The longitudinal chromatic aberration from the eye is corrected by a custom-designed achromatizing lens. The elegant fiber-based design enables simultaneous imaging for both channels and allows comprehensive ELSS analysis on several important anatomical layers, including nerve fiber layer, outer segment of the photoreceptors and retinal pigment epithelium. This vnOCT platform and method of ELSS analysis open new opportunities in understanding structure-function relationship in the human retina and in exploring new biomarkers for retinal diseases.

Automatic retinal layer segmentation with medical images, such as optical coherence tomography (OCT) images, serves as an important tool for diagnosing ophthalmic diseases. However, it is challenging to achieve accurate segmentation due to low contrast and blood flow noises presented in the images. In addition, the algorithm should be lightweight to be deployed for practical clinical applications. Therefore, it is desired to design a lightweight network with high performance for retinal layer segmentation. In this article, we propose LightReSeg for retinal layer segmentation which can be applied to OCT images. Specifically, our approach follows an encoder-decoder structure, where the encoder part uses multiscale feature extraction and a transformer block for fully exploiting the semantic information of feature maps at all scales and making the features have better global reasoning capabilities, while the decoder part, we design a multiscale asymmetric attention (MAA) module for preserving the semantic information at each encoder scale. The experiments show that our approach achieves a better segmentation performance compared with the current state-of-the-art method TransUnet with 105.7 M parameters on both our collected dataset and two other public datasets, with only 3.3 M parameters.