Results 191 to 200 of about 2,892,707 (247)
Genomic determinants of biological age estimated by deep learning applied to retinal images. [PDF]
Huang Y +20 more
europepmc +1 more source
We investigated the therapeutic potential and mechanism of chrysin in experimental diabetic retinal disease. Using a streptozotocin (STZ)‐induced diabetic mouse model and high‐glucose‐stimulated BV2 microglia, we revealed chrysin‐responsive pyroptosis‐associated signatures and identified GBP3 as a regulated node.
Qun Liu +9 more
wiley +1 more source
Anomaly Detection and Biomarkers Localization in Retinal Images. [PDF]
Tiosano L +6 more
europepmc +1 more source
Machine Learning Prediction of Cardiovascular Risk in Type 1 Diabetes Mellitus Using Radiomic Features from Multimodal Retinal Images. [PDF]
Tohà-Dalmau A +13 more
europepmc +1 more source
Medical image registration and its application in retinal images: a review. [PDF]
Nie Q, Zhang X, Hu Y, Gong M, Liu J.
europepmc +1 more source
PLA and PCL ocular implants prepared by tape casting and electrospinning provide sustained dexamethasone release with distinct release profiles governed by polymer type and processing method. The relationship between polymer structure, degradation behavior, and drug release demonstrates the potential of these fabrication techniques for controlled ...
Renata Bochanoski +6 more
wiley +1 more source
Autonomous Screening for Diabetic Macular Edema Using Deep Learning Processing of Retinal Images. [PDF]
Bressler I +7 more
europepmc +1 more source
Understanding the spacing of placodes in the eye: A comparative study across age and species
Abstract The conjunctival placodes of the avian eye form in an intriguing and conserved sequence in a circular annulus around the cornea. These 13–16 placodes develop into papillae that are essential for inducing underlying intramembranous flat bones, known as scleral ossicles, which form an important part of the ocular skeleton.
Florence Joseph +1 more
wiley +1 more source
Deep learning-based optical coherence tomography and retinal images for detection of diabetic retinopathy: a systematic and meta analysis. [PDF]
Bi Z, Li J, Liu Q, Fang Z.
europepmc +1 more source

