The Role of Estriol and Estrone in Keratoconic Stromal Sex Hormone Receptors

Keratoconus (KC) is a progressive corneal thinning disease that manifests in puberty and worsens during pregnancy. KC onset and progression are attributed to diverse factors that include: environmental, genetics, and hormonal imbalances; however, the pathobiology remains elusive. This study aims to determine the role of corneal stroma sex hormone receptors in KC and their interplay with estrone (E1) and estriol (E3) using our established 3D in vitro model. Healthy cornea stromal cells (HCFs) and KC cornea stromal cells (HKCs), both male and female, were stimulated with various concentrations of E1 and E3. Significant changes were observed between cell types, as well as between males and females in the sex hormone receptors tested; androgen receptor (AR), progesterone receptor (PR), estrogen receptor alpha (ERα), and estrogen receptor beta (ERβ) using Western blot analysis. E1 and E3 stimulations in HCF females showed AR, PR, and ERβ were significantly upregulated compared to HCF males. In contrast, ERα and ERβ had significantly higher expression in HKC’s females than HKC’s males. Our data suggest that the human cornea is a sex-dependent, hormone-responsive tissue that is significantly influenced by E1 and E3. Therefore, it is plausible that E1, E3, and sex hormone receptors are involved in the KC pathobiology, warranting further investigation.

Single cell RNA-seq identifies developing corneal cell fates in the human cornea organoid

The cornea is a protective and refractive barrier in the eye crucial for vision. Understanding the human cornea in health, disease and cell-based treatments can be greatly advanced with cornea organoids developed in culture from induced pluripotent stem cells. While a limited number of studies have investigated the single-cell transcriptomic composition of the human cornea, its organoids have not been examined similarly. Here we elucidated the transcriptomic cell fate map of 4 month-old human cornea organoids and the central cornea from three donors. The organoids harbor cell clusters representing corneal epithelium, stroma and endothelium with sub populations that capture signatures of early developmental states. Unlike the adult cornea where the largest cell population is stromal, the organoids develop almost equal proportion of the three major cell types. These corneal organoids offer a three-dimensional platform to model corneal diseases and integrated responses of the different cell types to treatments.

Incidence of Corneal Inflammation of Varying Severity among Contact Lens Wearers

The human cornea is an avascular, transparent tissue that permits or allows light rays to enter or reach the retina. The cornea is made up of several layers, each of which plays an important role in preserving the transparency and power of cornea. The epithelium, Bowmans layer, Descemet’s membrane and endothelium are the layers of the cornea. This study aimed to discover the incidences of severe and non-severe corneal inflammation amongst wearers of modern technology touch lenses. Studies on touch lens users providing with a corneal ulcer/infiltrates were reported. On the basis of severity, a medical severity matrix was utilized among cases of severe and non-severe corneal inflammation. There was a large distinction in severe corneal infection among ew-hydrogel and ew-silicone hydrogel. When it comes to evaluating touch lens-related corneal inflammation, a medical severity matrix is available in hand. Wearers of contact lenses who sleep with them have a appreciably low risk of severe corneal inflammation than those who simplest put on lenses for the duration of the day. Those cases who everyday sleep with lenses have a 5-fold lower risk of severe corneal inflammation as compared day-to-day hydrogel lenses for prolonged utilization.

Human Adenovirus Species D Interactions with Corneal Stromal Cells

Notable among the many communicable agents known to infect the human cornea is the human adenovirus, with less than ten adenoviruses having corneal tropism out of more than 100 known types. The syndrome of epidemic keratoconjunctivitis (EKC), caused principally by human adenovirus, presents acutely with epithelial keratitis, and later with stromal keratitis that can be chronic and recurrent. In this review, we discuss the current state of knowledge regarding the molecular biology of adenovirus infection of corneal stromal cells, among which the fibroblast-like keratocyte is the most predominant, in order to elucidate basic pathophysiologic mechanisms of stromal keratitis in the human patient with EKC.

Local information pattern descriptor for corneal diseases diagnosis

<span lang="EN-US">Light penetrates the human eye through the cornea, which is the outer part of the eye, and then the cornea directs it to the pupil to determine the amount of light that reaches the lens of the eye. Accordingly, the human cornea must not be exposed to any damage or disease that may lead to human vision disturbances. Such damages can be revealed by topographic images used by ophthalmologists. Consequently, an important priority is the early and accurate diagnosis of diseases that may affect corneal integrity through the use of machine learning algorithms, particularly, use of local feature extractions for the image. Accordingly, we suggest a new algorithm called local information pattern (LIP) descriptor to overcome the lack of local binary patterns that loss of information from the image and solve the problem of image rotation. The LIP based on utilizing the sub-image center intensity for estimating neighbors' weights that can use to calculate what so-called contrast based centre (CBC). On the other hand, calculating local pattern (LP) for each block image, to distinguish between two sub-images having the same CBC. LP is the sum of transitions of neighbors' weights, from sub-image center value to one and vice versa. Finally, creating histograms for both CBC and LP, then blending them to represent a robust local feature vector. Which can use for diagnosing, detecting.</span>