Vascular Reactivity to Hypercapnia Is Impaired in the Cerebral and Retinal Vasculature in the Acute Phase After Experimental Subarachnoid Hemorrhage

Objective: Impaired cerebral blood flow (CBF) regulation, such as reduced reactivity to hypercapnia, contributes to the pathophysiology after aneurysmal subarachnoid hemorrhage (SAH), but temporal dynamics in the acute phase are unknown. Featuring comparable molecular regulation mechanisms, the retinal vessels participate in chronic and subacute stroke- and SAH-associated vessel alterations in patients and can be studied non-invasively. This study is aimed to characterize the temporal course of the cerebral and retinal vascular reactivity to hypercapnia in the acute phase after experimental SAH and compare the potential degree of impairment.Methods: Subarachnoid hemorrhage was induced by injecting 0.5 ml of heparinized autologous blood into the cisterna magna of male Wistar rats using two anesthesia protocols [isoflurane/fentanyl n = 25 (Sham + SAH): Iso—Group, ketamine/xylazine n = 32 (Sham + SAH): K/X—Group]. CBF (laser speckle contrast analysis) and physiological parameters were measured continuously for 6 h. At six predefined time points, hypercapnia was induced by hypoventilation controlled via blood gas analysis, and retinal vessel diameter (RVD) was determined non-invasively.Results: Cerebral reactivity and retinal reactivity in Sham groups were stable with only a slight attenuation after 2 h in RVD of the K/X—Group. In the SAH Iso—Group, cerebral and retinal CO2 reactivity compared to baseline was immediately impaired starting at 30 min after SAH (CBF p = 0.0090, RVD p = 0.0135) and lasting up to 4 h (p = 0.0136, resp. p = 0.0263). Similarly, in the K/X—Group, cerebral CO2 reactivity was disturbed early after SAH (30 min, p = 0.003) albeit showing a recovery to baseline after 2 h while retinal CO2 reactivity was impaired over the whole observation period (360 min, p = 0.0001) in the K/X—Group. After normalization to baseline, both vascular beds showed a parallel behavior regarding the temporal course and extent of impairment.Conclusion: This study provides a detailed temporal analysis of impaired cerebral vascular CO2 reactivity starting immediately after SAH and lasting up to 6 h. Importantly, the retinal vessels participate in these acute changes underscoring the promising role of the retina as a potential non-invasive screening tool after SAH. Further studies will be required to determine the correlation with functional outcomes.

Compensatory contribution of retinal larger vessels to perfusion density in diabetics without retinopathy

Vessel and perfusion densities may decrease before diabetic retinopathy appears; it is unknown whether these changes affect the contribution of vessel density to perfusion density. This was a non-experimental, comparative, prospective, cross-sectional study in non-diabetic subjects (group 1) and diabetics without retinopathy (group 2). Vessel and perfusion densities in the superficial capillary plexus were compared between groups at the center, inner, and full regions and by field (superior, temporal, inferior, nasal) using optical coherence tomography angiography. Coefficients of determination (R2) between vessel and perfusion densities were calculated to find the contribution of larger retinal vessels to perfusion density. Percent differences were used to evaluate the contribution of these vessels to perfusion density in a regression model. There were 62 participants, 31 eyes by group; vessel and perfusion densities as well as the coefficients of determination between them were lower in group 2, especially in the nasal field (R2 0.85 vs. 0.71), which showed a higher contribution of larger retinal vessels to perfusion density. The regression model adjusted to a quadratic equation. In diabetics without retinopathy the contribution of vessel density to perfusion density may decrease; a low vessel density may increase the contribution of larger retinal vessels to perfusion density.

Multiscale U-Net with Spatial Positional Attention for Retinal Vessel Segmentation

Retinal vessel segmentation is essential for the detection and diagnosis of eye diseases. However, it is difficult to accurately identify the vessel boundary due to the large variations of scale in the retinal vessels and the low contrast between the vessel and the background. Deep learning has a good effect on retinal vessel segmentation since it can capture representative and distinguishing features for retinal vessels. An improved U-Net algorithm for retinal vessel segmentation is proposed in this paper. To better identify vessel boundaries, the traditional convolutional operation CNN is replaced by a global convolutional network and boundary refinement in the coding part. To better divide the blood vessel and background, the improved position attention module and channel attention module are introduced in the jumping connection part. Multiscale input and multiscale dense feature pyramid cascade modules are used to better obtain feature information. In the decoding part, convolutional long and short memory networks and deep dilated convolution are used to extract features. In public datasets, DRIVE and CHASE_DB1, the accuracy reached 96.99% and 97.51%. The average performance of the proposed algorithm is better than that of existing algorithms.

A clinical case of central retinal artery occlusion after pneumonia caused by SARS-CoV-2 (COVID-19)

In domestic and foreign medical periodical literature, highlighting the problem of ophthalmopathology against the background of coronavirus infection, the lesion of the anterior segment of the eye is most often described. It is extremely hard to find reports about pathology of the retina, optic nerve or central parts of the visual analyzer. However, it is widely acknowledged that there is a high risk of developing coagulopathy against the background of COVID-19 infection, which leads to occlusion and thrombosis of retinal vessels, ischemic neuropathies. The problem of irreversible loss of vision due to circulatory disorders of the retinal vessels was urgent even before the wide spread of coronavirus infection due to the high prevalence of atherosclerosis, hypertension and type 1 and 2 diabetes in the population. Also, it is widely known that occlusions and thrombosis of retinal vessels can be formidable harbingers of the developing life-threatening conditions. Knowledge about the high risk of vascular ophthalmopatology against the background of a previous COVID-19 infection by the primary echelon ophthalmologists, early diagnostics and treatment of ischemic conditions of the posterior segment of the eye will reduce the frequency of irreversible vision loss due to these diseases, secondary neovascular glaucoma cases, and will also help to send patients to the multidisciplinary hospitals in a timely manner for the prevention of fatal complications of coagulopathy. The article provides a brief overview of foreign literary sources regarding the history of outbreaks of coronavirus infection in the world, as well as possible ways of damage to the organ of vision by the coronavirus. A clinical case of damage to the vascular bed of the retina in both eyes due to coagulopathy against the background of pneumonia caused by COVID-19 is presented, which is actual due to the low illumination of similarly cases.

Retinal Vessel Segmentation Algorithm Based on Residual Convolution Neural Network

Retinal vessels are the only deep micro vessels that can be observed in human body, the accurate identification of which has great significance on the diagnosis of hypertension, diabetes and other diseases. To this end, a retinal vessel segmentation algorithm based on residual convolution neural network is proposed according to the characteristics of the retinal vessels on fundus images. Improved residual attention module and deep supervision module are utilized, in which the low-level and high-level feature graphs are joined to construct the encoder-decoder network structure, and atrous convolution is introduced to the pyramid pooling. The experiments result on the fundus image data set DRIVE and STARE show that this algorithm can obtain complete retinal vessel segmentation as well as connected vessel stems and terminals. The average accuracy on DRIVE and STARE reaches 95.90 and 96.88%, and the average specificity is 98.85 and 97.85%, which shows superior performance compared to other methods. This algorithm is verified feasible and effective for retinal vessel segmentation of fundus images and has the ability to detect more capillaries.

Fundus Image Registration Technique Based on Local Feature of Retinal Vessels

Feature-based retinal fundus image registration (RIR) technique aligns fundus images according to geometrical transformations estimated between feature point correspondences. To ensure accurate registration, the feature points extracted must be from the retinal vessels and throughout the image. However, noises in the fundus image may resemble retinal vessels in local patches. Therefore, this paper introduces a feature extraction method based on a local feature of retinal vessels (CURVE) that incorporates retinal vessels and noises characteristics to accurately extract feature points on retinal vessels and throughout the fundus image. The CURVE performance is tested on CHASE, DRIVE, HRF and STARE datasets and compared with six feature extraction methods used in the existing feature-based RIR techniques. From the experiment, the feature extraction accuracy of CURVE (86.021%) significantly outperformed the existing feature extraction methods (p ≤ 0.001*). Then, CURVE is paired with a scale-invariant feature transform (SIFT) descriptor to test its registration capability on the fundus image registration (FIRE) dataset. Overall, CURVE-SIFT successfully registered 44.030% of the image pairs while the existing feature-based RIR techniques (GDB-ICP, Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG) only registered less than 27.612% of the image pairs. The one-way ANOVA analysis showed that CURVE-SIFT significantly outperformed GDB-ICP (p = 0.007*), Harris-PIIFD, Ghassabi’s-SIFT, H-M 16, H-M 17 and D-Saddle-HOG (p ≤ 0.001*).

Ocular drug delivery system (ODDS): Exploration the challenges and approaches to improve ODDS

The remarkable life structures and physiology of the eye presents huge difficulties to researchers in the field of visual medication conveyance frameworks. Nearby infusion is the most fitting and proper medication organization technique for the treatment of foremost front sickness. There are two kinds of hindrances in ophthalmic medication conveyance frameworks: static boundaries and dynamic obstructions. Static lamellae contain corneal, dermal, retinal, and retinal vessels while dynamic lamellae contain placental blood stream, conjunctiva, tear evacuation, and lymphatic seepage. These limitations influence the bioavailability of the medication. This article examines the limits of customary ophthalmic practice and the central point affecting the pharmacokinetics of the eye. Likewise, eye salves, gels, prodrugs, intranasal infusions, thickeners, entrance energizers, liposomes, microparticles, nanoparticles, visual infusions, inserts, nanoparticles, nanostructures, microemulsions, gels and periocular infusions. It guarantees the bioavailability of the medication and the controlled and constant control of the medication in the foremost and back alveoli.