Near-infrared reflectance (NIR) is a noninvasive, contactless, and rapid in vivo imaging technique for visualizing subretinal alterations in the photoreceptor layer, retinal pigment epithelium, and choroid. The present report describes the application of this imaging method in retinal and choroidal pathologies affecting young patients where scarce cooperation, poor fixation, and intense glare sensation can result in a challenging clinical examination. A literature search of the MEDLINE database was performed using the terms “near-infrared reflectance” and “spectral-domain optical coherence tomography.” Articles were selected if they described the diagnostic use of NIR in children or young adults. Of 700 publications, 42 manuscripts published between 2005 and 2020 were inherent to children or young adults and were considered in this narrative literature review. The first disease category is the phakomatoses where NIR is essential in visualizing choroidal alterations recognized as cardinal biomarkers in neurofibromatosis type 1, microvascular retinal alterations, and retinal astrocytic hamartomas. Another diagnostic application is the accurate visualization of crystals of various nature, including the glistening crystals that characterize Bietti crystalline dystrophy. Acute macular neuropathy and paracentral acute middle maculopathy represent a further disease category with young adulthood onset where NIR is not only diagnostic but also essential to monitor disease progression. A further interesting clinical application is to facilitate the detection of laser-induced maculopathy where funduscopic examination can be normal or subnormal. In conclusion, NIR imaging has a noninterchangeable role in diagnosing certain retinal diseases, especially in children and young adults where there is scarce collaboration and a lack of evident clinical findings. Moreover, this technique can reveal unique retinal and choroidal biomarkers highly specific to rare conditions.
Upon pathogen attack, plants very quickly undergo rather complex physico-chemical changes, such as the production of new chemicals or alterations in membrane and cell wall properties, to reduce disease damages. An underestimated threat is represented by root parasitic nematodes. In Vitis vinifera L., the nematode Xiphinema index is the unique vector of Grapevine fanleaf virus, responsible for fanleaf degeneration, one of the most widespread and economically damaging diseases worldwide. The aim of this study was to investigate changes in the emission of biogenic volatile organic compounds (BVOCs) in grapevines attacked by X. index. BVOCs play a role in plant defensive mechanisms and are synthetized in response to biotic damages. In our study, the BVOC profile was altered by the nematode feeding process. We found a decrease in β-ocimene and limonene monoterpene emissions, as well as an increase in α-farnesene and α-bergamotene sesquiterpene emissions in nematode-treated plants. Moreover, we evaluated the PR1 gene expression. The transcript level of PR1 gene was higher in the nematode-wounded roots, while in the leaf tissues it showed a lower expression compared to control grapevines.
Artificial intelligence (AI) represents a growing and promising branch of computer science that is expanding the horizon of prediction, screening, and disease monitoring. The use of multimodal imaging in retinal diseases is particularly advantageous to valorize the integration of machine learning and deep learning for early diagnosis, prediction, and management of retinal disorders. In age-related macular degeneration (AMD) beyond its diagnosis and characterization, the prediction of AMD high-risk phenotypes evolving into late forms remains a critical point. The main multimodal imaging modalities adopted included color fundus photography, fundus autofluorescence, and optical coherence tomography (OCT), which represents undoubtful advantages over other methods. OCT features identified as predictors of late AMD include the morphometric evaluation of retinal layers, drusen volume and topographic distribution, reticular pseudodrusen, and hyperreflective foci quantification. The present narrative review proposes to analyze the current evidence on AI models and biomarkers identified to predict disease progression with particular attention to OCT-based features and to highlight potential perspectives for future research.artificial intelligence; age-related macular degeneration; deep learning; multimodal imaging
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