Blue light reflectance (BLR) imaging offers a non-invasive, cost-effective method for evaluating retinal structures by analyzing the reflectance and absorption characteristics of the inner retinal layers. By leveraging blue light's interaction with retinal tissues, BLR enhances visualization beyond the retinal nerve fiber layer, improving detection of structures such as the outer plexiform layer and macular pigment. Its diagnostic utility has been demonstrated in distinct retinal conditions, including hyperreflectance in early macular telangiectasia, hyporeflectance in non-perfused areas indicative of ischemia, identification of pseudodrusen patterns (notably the ribbon type), and detection of peripheral retinal tears and degenerative retinoschisis in eyes with reduced retinal pigment epithelial pigmentation. Best practices for image acquisition and interpretation are discussed, emphasizing standardization to minimize variability. Common artifacts and mitigation strategies are also addressed, ensuring image reliability. BLR's clinical utility, limitations, and future research directions are highlighted, particularly its potential in automated image analysis and quantitative assessment. Different BLR acquisition methods, such as fundus photography, confocal scanning laser ophthalmoscopy, and broad line fundus imaging, are evaluated for their respective advantages and limitations. As research advances, BLR's integration into multimodal workflows is expected to improve early detection and precise monitoring of retinal diseases.
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http://dx.doi.org/10.1016/j.preteyeres.2024.101326 | DOI Listing |
Retina
January 2025
Department of Ophthalmology, Institute of Clinical Neurosciences of Southern Switzerland (INS), Ospedale Regionale di Lugano, Ente Ospedaliero Cantonale (EOC), Lugano, Switzerland.
Purpose: To assess if drusen volume can serve as structural clinical outcome marker in Malattia Leventinese (ML), and to evaluate whether cones or rods are more affected by its progression, using multimodal imaging and mesopic and two-color scotopic microperimetry.
Methods: This was a prospective monocentric cross-sectional cohort study of participants with genetically confirmed ML. Participants were classified according to morphology.
Retina
January 2025
Shiley Eye Institute, University of California, San Diego, CA, USA.
Purpose: To characterize retinal vessel whitening (RVW) in Retinitis Pigmentosa (RP).
Methods: Single-center cross-sectional study. Review of clinical notes of clinically confirmed RP patients was performed followed by grading ultra-widefield imaging.
Retina
January 2025
Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
Purpose: To present a novel bended-needle drainage system in vitreous cavity lavage (VCL) for postoperative vitreous cavity hemorrhage (POVCH).
Methods: This retrospective case series include all patients with POVCH who received VCL with the bended-needle drainage system at ophthalmology department of Peking Union Medical College Hospital from January 2022 to May 2024. Patients adopted a supine position that allows preparation and draping.
ACS Nano
January 2025
Department of Anesthesiology and Surgical Intensive Care Unit, Xinhua Hospital, School of Medicine and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China.
Identifying effective biomarkers has long been a persistent need for early diagnosis and targeted therapy of disease. While mass spectrometry-based label-free proteomics with trace cell has been demonstrated, deep proteomics with ultratrace human biofluid remains challenging due to low protein concentration, extremely limited patient sample volume, and substantial protein contact losses during preprocessing. Herein, we proposed and validated lanthanide metal-organic framework flowers (MOF-flowers), as effective materials, to trap and enrich protein in biofluid jointly through cation-π interaction and O-Ln coordination.
View Article and Find Full Text PDFBiomed Phys Eng Express
January 2025
Faculty of Information Technology, Beijing University of Technology, Beijing, People's Republic of China.
In fundus images, precisely segmenting retinal blood vessels is important for diagnosing eye-related conditions, such as diabetic retinopathy and hypertensive retinopathy or other eye-related disorders. In this work, we propose an enhanced U-shaped network with dual-attention, named DAU-Net, divided into encoder and decoder parts. Wherein, we replace the traditional convolutional layers with ConvNeXt Block and SnakeConv Block to strengthen its recognition ability for different forms of blood vessels while lightweight the model.
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