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In patients with Alzheimer's disease (AD) and in animal models, the increased accumulation of amyloid β (Aβ) in retinal blood vessels strongly correlates with brain amyloid deposits and cognitive decline. The accumulation of Aβ in blood vessels may result from impaired transcytosis and a dysfunctional ocular glymphatic system in AD. High-dose fish oil (FO) supplementation has been shown to significantly change the expression of major facilitator superfamily domain-containing protein 2a (Mfsd2a), a key regulator of transcytosis, and Aquaporin 4 (Aqp4), an essential component of the glymphatic system in the retinas of WT mice. We examined the expression of Mfsd2a and Aqp4 in the retinas of 4-month-old 5xFAD female mice supplemented with high-dose FO for three weeks. There was a significant increase in Mfsd2a expression in 5xFAD retinas supplemented with FO compared to control 5xFAD mice. Additionally, the increase in Aqp4 expression observed in 4-month-old 5xFAD retinas, indicative of an impaired glymphatic system, was significantly decreased. Simultaneously, Aβ accumulation in 5xFAD retinal blood vessels was reduced following FO supplementation. These findings suggest that high-dose FO supplementation could serve as an adjunct in developing new treatments aimed at improving the regulation of transcytosis or the function of the glymphatic system in the AD retina. [ABSTRACT FROM AUTHOR]

PURPOSE: To characterize developing retinal blood vessels with vascular markers and to relate the histochemical profile of maturing vessels to morphologic stages in retinal vascular development. METHODS: Vessels were examined in frozen and paraffin-embedded retinas and in wholemounts of Macaca monkeys ranging in age from fetal day 75 (F75) to adulthood. Endothelial cells were visualized immunohistochemically using antisera to von Willebrand's factor and CD31 with lectins Ulex europaeus, Bandeiraea simplicifolia, peanut agglutinin, Ricinis communis, and wheat germ agglutinin, and by ATPase and ADPase enzymatic histochemistry. Antibodies to vascular basement membrane and matrix markers laminin, fibronectin, and collagen types I and VIII, and antisera recognizing cell cycle-specific nuclear proteins (cyclin, Ki-67, Mib-1) also were used. RESULTS: Newly formed and mature vessels were reactive with reagents specific for CD31, von Willebrand's factor, types I and VIII collagens, laminin, fibronectin, U. europaeus, R. communis, and peanut agglutinin. Wheat germ agglutinin labeled vessels only after pretreatment with neuraminidase. All vascular markers appeared simultaneously, but some were distributed differentially between capillaries and larger vessels, along the central-peripheral extent of a vascular plexus, and among different vascular laminae. Markers of vessels failed to label spindle-shaped presumed vascular precursor cells lying peripheral to the advancing vessels during development. Spindle cells exhibited cyclin, Ki-67, and Mib-1 immunoreactivity. CONCLUSIONS: Immature and mature vitread and sclerad vessels displayed histochemical profiles that were qualitatively similar but that had subtle quantitative differences. Results do not support identification of spindle-shaped cells as vascular precursors in the developing monkey retina and are discussed in relation to mechanisms of retinal vascularization.

Retinal blood vessels have become promising biomarkers for the early diagnosis and evaluation of systemic disorders, including myocardial infarction (heart disease) and cerebrovascular diseases. With an emphasis on current developments in retinal imaging techniques and their importance in identifying early vascular changes connected to both diseases, this review article explores the relationship between retinal blood vessels and these two significant systemic ailments. The benefits and drawbacks of several non-invasive imaging techniques for documenting retinal vascular changes, such as fundus photography, fluorescein angiography, and optical coherence tomography (OCT), are discussed. The study examines a variety of information that connects myocardial infarction risk, occurrence, and development to retinal vessel characteristics like calibre, tortuosity, and branching patterns. The underlying processes that might connect retinal vascular alterations to these systemic diseases are also perhaps investigated. In addition, common risk factors and systemic diseases that affect both the retinal and systemic vasculature are described as shared pathophysiological pathways. The promise of retinal vessel analysis as a non-invasive method for risk assessment, diagnosis, and monitoring of cardiovascular and cerebrovascular illnesses is highlighted by these findings. Retinal vascular analysis has the potential for improved patient outcomes through prompt interventions because of its affordability, accessibility, and non-invasive nature. To fully realize the clinical potential of this strategy, a number of issues, such as the standardization of imaging methods and the requirement for extensive longitudinal investigations, must be resolved. [ABSTRACT FROM AUTHOR]

A diagnostic medical tool is described that allows patients to non-invasively observe, describe and document their own retinal blood vessels using the Purkinje vascular entoptic test. We envision key applications of the Purkinje retinal image stabilization (PRIS) device in monitoring of diabetic retinopathy and cost-effective assessment of retinal visual acuity. To optimize our PRIS principle, we have developed a benchtop device with interchangeable light modules. The modular design is intended to provide maximum flexibility for determining appropriate PRIS stimulation parameters. In self-tests, we found that a pleasant entoptic perception occurs when a green luminous light spot (approx. 525 nm) moves on a circular path. The challenging part is to derive an objective diagnosis from a subjective phenomenon.

Changes in the retinal vasculature of the fundus image help in identifying retinal diseases. This is done by segmenting the retinal blood vessels. Computational approaches are preferred over traditional approaches for the segmentation of vessels as it is a time-consuming process. There are various techniques involved in the proposed Contrast Enhancement using Histogram Equalization algorithm such as image pre-processing and post-processing techniques, supervised and unsupervised learning techniques. Each stage is responsible for performing a series of actions. As the images are pre-processed, a feature vector is formed to which Principal Component Analysis is applied. The output is then subjected to k-means clustering to group the pixels obtained as vessel clusters or non-vessel clusters. The vessel clusters are not processed further, while the non-vessel clusters are subjected to ensemble classification which makes use of a decision tree along with bagging. The segmented image thus obtained is the combined result of clustering and ensemble classification technique. This segmented image thus obtained is then subjected to post-processing using morphological techniques. The images are then validated which shows that compared to the existing techniques, the proposed model for blood vessel segmentation shows 95% accuracy. [ABSTRACT FROM AUTHOR]

PURPOSE: To characterize developing retinal blood vessels with vascular markers and to relate the histochemical profile of maturing vessels to morphologic stages in retinal vascular development. METHODS: Vessels were examined in frozen and paraffin-embedded retinas and in wholemounts of Macaca monkeys ranging in age from fetal day 75 (F75) to adulthood. Endothelial cells were visualized immunohistochemically using antisera to von Willebrand's factor and CD31 with lectins Ulex europaeus, Bandeiraea simplicifolia, peanut agglutinin, Ricinis communis, and wheat germ agglutinin, and by ATPase and ADPase enzymatic histochemistry. Antibodies to vascular basement membrane and matrix markers laminin, fibronectin, and collagen types I and VIII, and antisera recognizing cell cycle-specific nuclear proteins (cyclin, Ki-67, Mib-1) also were used. RESULTS: Newly formed and mature vessels were reactive with reagents specific for CD31, von Willebrand's factor, types I and VIII collagens, laminin, fibronectin, U. europaeus, R. communis, and peanut agglutinin. Wheat germ agglutinin labeled vessels only after pretreatment with neuraminidase. All vascular markers appeared simultaneously, but some were distributed differentially between capillaries and larger vessels, along the central-peripheral extent of a vascular plexus, and among different vascular laminae. Markers of vessels failed to label spindle-shaped presumed vascular precursor cells lying peripheral to the advancing vessels during development. Spindle cells exhibited cyclin, Ki-67, and Mib-1 immunoreactivity. CONCLUSIONS: Immature and mature vitread and sclerad vessels displayed histochemical profiles that were qualitatively similar but that had subtle quantitative differences. Results do not support identification of spindle-shaped cells as vascular precursors in the developing monkey retina and are discussed in relation to mechanisms of retinal vascularization.

The main purpose of identifying and locating the retina vessels is to specify from the fundus image the various tissues of the vascular structure, which can be wide or tight. The classification of vessels in the retinal image often confronts several challenges, such as the low contrast accompanying the fundus image, the inhomogeneity of the background lighting, and the noise. Moreover, fuzzy c-means (FCM) is one of the most frequently used algorithms for medical image segmentation due to its effectiveness. Hence, many FCM method derivatives have been developed to improve their noise robustness and time-consuming. This paper aims to analyze the performance of some improved FCM algorithms to recommend the best ones for the segmentation of retinal blood vessels. Eight derivatives of FCM algorithm are detained in this study: FCM, EnFCM, SFCM, FGFCM, FRFCM, DSFCM_N, FCM_SICM and SSFCA. The performance analysis is conducted from three viewpoints: noise robustness, blood vessels segmentation performance, and time-consuming. At first, the noise robustness of improved FCM clustering algorithms is evaluated using a synthetic image degraded by various types and levels of noise. Then, the ability of the selected algorithms to segment retinal blood vessels is assessed based on images from the DRIVE and STARE databases after a pre-processing phase. Finally, the time consumption of each algorithm is measured. The experiments demonstrate that the FRFCM and DSFCM_N algorithms achieve better results in terms of noise robustness and blood vessels segmentation. Regarding the running time, the FRFCM algorithm requires less time than other algorithms in the segmentation of retinal images. The results of this study are extensively discussed, and some suggestions are proposed at the end of this paper. [ABSTRACT FROM AUTHOR]

Retinal vein segmentation is a crucial task that helps in the early detection of health problems, making it an essential area of research. With recent advancements in artificial intelligence, we can now develop highly reliable and efficient models for this task. CNN has been the traditional choice for image analysis tasks. However, the emergence of visual transformers with their unique attention mechanism has proved to be a game-changer. However, visual transformers require a large amount of data and computational power, making them unsuitable for tasks with limited data and resources. To deal with this constraint, we adapted the attention module of visual transformers and integrated it into a CNN-based UNET network, achieving superior performance compared to other models. The model achieved a 0.89 recall, 0.98 AUC, 0.97 accuracy, and 0.97 sensitivity on various datasets, including HRF, Drive, LES-AV, CHASE-DB1, Aria-A, Aria-D, Aria-C, IOSTAR, STARE and DRGAHIS. Moreover, the model can recognize blood vessels accurately, regardless of camera type or the original image resolution, ensuring that it generalizes well. This breakthrough in retinal vein segmentation could improve the early diagnosis of several health conditions.

Nitric oxide synthases (NOS) are essential regulators of vascular function, and their role in ocular blood vessels is of paramount importance for maintaining ocular homeostasis. Three isoforms of NOS—endothelial (eNOS), neuronal (nNOS), and inducible (iNOS)—contribute to nitric oxide production in ocular tissues, exerting multifaceted effects on vascular tone, blood flow, and overall ocular homeostasis. Endothelial NOS, primarily located in endothelial cells, is pivotal for mediating vasodilation and regulating blood flow. Neuronal NOS, abundantly found in nerve terminals, contributes to neurotransmitter release and vascular tone modulation in the ocular microvasculature. Inducible NOS, expressed under inflammatory conditions, plays a role in response to pathological stimuli. Understanding the distinctive contributions of these NOS isoforms in retinal blood vessels is vital to unravel the mechanisms underlying various ocular diseases, such diabetic retinopathy. This article delves into the unique contributions of NOS isoforms within the complex vascular network of the retina, elucidating their significance as potential therapeutic targets for addressing pathological conditions.

Accurate retinal blood vessels segmentation is an important step in the clinical diagnosis of ophthalmic diseases. Many deep learning frameworks have come up for retinal blood vessels segmentation tasks. However, the complex vascular structure and uncertain pathological features make blood vessel segmentation still very challenging. This paper proposes a novel multimodule concatenation via a U-shaped network for retinal vessels segmentation, which is based on atrous convolution and multikernel pooling. The proposed network structure retains three layers of the essential structure of U-Net, in which the atrous convolution combining the multikernel pooling blocks are designed to obtain more contextual information. The spatial attention module is concatenated with the dense atrous convolution module and the multikernel pooling module to form a multimodule concatenation. And different dilation rates are selected by cascading to acquire a larger receptive field in atrous convolution. Adequate comparative experiments are conducted on these public retinal datasets: DRIVE, STARE, and CHASE_DB1. The results show that the proposed method is effective, especially for microvessels. The code will be released at https://github.com/rocklijun/MC-UNet. [ABSTRACT FROM AUTHOR]

Accurate segmentation of retinal vessels is crucial for the early diagnosis and treatment of eye diseases, for example, diabetic retinopathy, glaucoma, and macular degeneration. Due to the intricate structure of retinal vessels, it is essential to extract their features with precision for the semantic segmentation of medical images. In this study, an improved deep learning neural network was developed with a focus on feature extraction based on the U‐Net structure. The enhanced U‐Net combines the architecture of convolutional neural networks (CNNs) with SE blocks (squeeze‐and‐excitation blocks) to adaptively extract image features after each U‐Net encoder's convolution. This approach aids in suppressing nonvascular regions and highlighting features for specific segmentation tasks. The proposed method was trained and tested on the DRIVECHASE_DB1 and STARE datasets. As a result, the proposed model had an algorithmic accuracy, sensitivity, specificity, Dice coefficient (Dc), and Matthews correlation coefficient (MCC) of 95.62/0.9853/0.9652, 0.7751/0.7976/0.7773, 0.9832/0.8567/0.9865, 82.53/87.23/83.42, and 0.7823/0.7987/0.8345, respectively, outperforming previous methods, including UNet++, attention U‐Net, and ResUNet. The experimental results demonstrated that the proposed method improved the retinal vessel segmentation performance. [ABSTRACT FROM AUTHOR]

While our left and right eyes clearly have a high degree of external bilateral similarity, it is less obvious to what degree they have internal bilateral similarity. This is especially true for the central retinal blood vessels (CRBVs), which are responsible for supplying blood to retinas and also can be used as a strong biometric. In this paper, we investigate whether the CRBVs of the left and right retinas possess strong enough bilateral similarity so that we reliably tell whether a pair of the left and right retinas belong to a single subject. We evaluate and analyse the performance of both human- and deep neural network-based bilateral verification by experimenting on two publicly available data sets.

Nitric oxide synthases (NOS) are essential regulators of vascular function, and their role in ocular blood vessels is of paramount importance for maintaining ocular homeostasis. Three isoforms of NOS-endothelial (eNOS), neuronal (nNOS), and inducible (iNOS)-contribute to nitric oxide production in ocular tissues, exerting multifaceted effects on vascular tone, blood flow, and overall ocular homeostasis. Endothelial NOS, primarily located in endothelial cells, is pivotal for mediating vasodilation and regulating blood flow. Neuronal NOS, abundantly found in nerve terminals, contributes to neurotransmitter release and vascular tone modulation in the ocular microvasculature. Inducible NOS, expressed under inflammatory conditions, plays a role in response to pathological stimuli. Understanding the distinctive contributions of these NOS isoforms in retinal blood vessels is vital to unravel the mechanisms underlying various ocular diseases, such diabetic retinopathy. This article delves into the unique contributions of NOS isoforms within the complex vascular network of the retina, elucidating their significance as potential therapeutic targets for addressing pathological conditions., Competing Interests: Given his role as Guest Editor, Adrian Gericke had no involvement in the peer-review of this article and has no access to information regarding its peer review. Full responsibility for the editorial process for this article was delegated to Said El Shamieh. The authors declare no conflict of interest., (© 2024 The Author(s). Published by IMR Press.)

This study investigated the possibility of using low-cost, handheld, retinal imaging devices for the automatic extraction of quantifiable measures of retinal blood vessels. Initially, the available handheld devices were compared using a Zeiss model eye incorporating a USAF resolution test chart to assess their optical properties. The only suitable camera of the five evaluated was the Horus DEC 200. This device was then subjected to a detailed evaluation in which images in human eyes taken from the handheld camera were compared in a quantitative analysis with those of the same eye from a Canon CR-DGi retinal desktop camera. We found that the Horus DEC 200 exhibited shortcomings in capturing images of human eyes by comparison with the Canon. More images were rejected as being unevaluable or suffering failures in automatic segmentation than with the Canon, and even after exclusion of affected images, the Horus yielded lower measurements of vessel density than the Canon. A number of issues affecting handheld cameras in general and some features of the Horus in particular have been identified that might contribute to the observed differences in performance. Some potential mitigations are discussed which might yield improvements in performance, thus potentially facilitating use of handheld retinal imaging devices for quantitative retinal microvascular measurements.

Background: Affections of eye are commonly encountered in all the species of animals. The age-related changes of the retina primarily cause loss of visual acuity as well as reduction of the visual field. Hence, the current study was carried out to establish basic data related to ageing changes in the retina of buffaloes. Methods: The study was conducted on 63 eye balls of locally available buffaloes of different breeds. These buffaloes were categorized into 3 groups i.e., group I (1-5 yrs), group II (6-10 yrs) and group III (11 yrs and above). The eyeballs were isolated and fixed with Davidson's fluid. The paraffin sections were subjected for routine histological study. Result: The thickness of retinal pigment epithelium was increased from group I to III buffaloes from 6.06±0.18 to 8.44±0.32 and the quantity of melanin pigment was decreased with advancement of age. Rod and cone cells of the photoreceptor layer were tightly packed in young age, loosely arranged in old animals. The mean thickness (µm) of photoreceptors and outer nuclear layers together was decreased from 64.22±1.84 to 58.72±2.1 with age advancement. The displacement of nuclei from outer nuclear layer into outer plexiform layer was significantly decreased in old animals. The outer limiting membrane was continuous and uninterrupted throughout the life. The thickness of outer plexiform layer was increased with advancement of age from 8.78±0.58 to 10±0.51 due to enhancement of synaptic fibers density. As age advances the number and density of horizontal, bipolar and amacrine cells were decreased and also the mean thickness (µm) of this layer was decreased from 30.83±1.48 to 22.56±0.62 in the inner nuclear layer. The thickness of inner plexiform layer was increased from 42.44±2.23 to 45.39±0.81 with advancement of age due to increased cystoids spaces and thickening of retinal blood vessels. In ganglionic cell layer, the number of β-ganglion cells were more than the β-ganglion cells, their common number were decreased approximately from 16-20 cells/sq.mm to 7-10 cells/sq.mm, whereas the average size was increased from 4.72±0.49 to 15.83±0.83 with advancement of age. In aged buffaloes nerve fiber layer showed corpora amylacea and thickened blood vessels. The inner limiting membrane became thick and uninterrupted in old buffaloes. The total thickness (µm) of retina in group I, II and III buffaloes were 235.5±7.25, 184.33±3.64 and 201.05±5.15 respectively. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) refers to a micro-vascular complication causing vision problems in diabetic patients. It is a serious global public health issue that damages the retinal blood vessels and leaves a large number of patients with loss of vision. Micro-aneurysms, hemorrhages, macular edema, and exudates are some of the lesions used to diagnose it. When detected early, Diabetic Retinopathy can be treated well with precise identification of lesions. Computer-aided diagnosis refers to the approaches used to extract specific features associated with the disease by using image processing filters. Some Machine Learning tools, especially the ones well-suited to data analysis applications are particularly useful. This paper primarily focuses on a comparative performance evaluation of many machine learning and deep learning-based techniques applied for the diagnosis of this vision ailment in diabetic patients. [ABSTRACT FROM AUTHOR]