Moorfields Eye Hospital

About: Moorfields Eye Hospital is a healthcare organization based out in London, United Kingdom. It is known for research contribution in the topics: Visual acuity & Glaucoma. The organization has 3721 authors who have published 6790 publications receiving 246004 citations.

Activation and suppression of fibroblast function.

Abstract: The fibroblast is the central player in the wound repair and scarring processes that occur in the anterior segment of the eye. Glaucoma filtration surgery is the ultimate example of the importance of the wound healing process, as this process is the major determinant of the success of this procedure. We highlight the role of the fibroblast, and discuss some of the growth factors stimulating fibroblast proliferation, migration and extracellular matrix production in the wound environment. We also review current methods of suppressing fibroblast proliferation, the new concepts that have arisen from laboratory studies, and future directions of investigation and treatment.

Function of indoleamine 2,3-dioxygenase in corneal allograft rejection and prolongation of allograft survival by over-expression

Abstract: Indoleamine 2,3-dioxygenase (IDO) suppresses T cell responses by its action in catabolising tryptophan. It is important in maintenance of immune privilege in the placenta. We investigated the activity of IDO in the cornea, following corneal transplantation and the effect of IDO over-expression in donor corneal endothelium on the survival of corneal allografts. IDO expression was analysed and functional activity was quantified in normal murine cornea and in corneas following transplantation as allografts. Low levels of IDO, at both mRNA and protein levels, was detected in the normal cornea, up-regulated by IFN-gamma and TNF. Expression of IDO in cornea was significantly increased following corneal transplantation. However, inhibition of IDO activity in vivo had no effect on graft survival. Following IDO cDNA transfer, murine corneal endothelial cells expressed functional IDO, which was effective at inhibiting allogeneic T cell proliferation. Over-expression of IDO in donor corneal allografts resulted in prolonged graft survival. While, on one hand, our data indicate that IDO may augment corneal immune privilege, up-regulated IDO activity following cytokine stimulation may serve to inhibit inflammatory cellular responses. While increasing IDO mRNA expression was found in allogeneic corneas at rejection, over-expression in donor cornea was found to significantly extend survival of allografts.

Fundus Photography in the 21st Century—A Review of Recent Technological Advances and Their Implications for Worldwide Healthcare

Abstract: Background: The introduction of fundus photography has impacted retinal imaging and retinal screening programs significantly. Literature Review: Fundus cameras play a vital role in addressing the cause of preventive blindness. More attention is being turned to developing countries, where infrastructure and access to healthcare are limited. One of the major limitations for tele-ophthalmology is restricted access to the office-based fundus camera. Results: Recent advances in access to telecommunications coupled with introduction of portable cameras and smartphone-based fundus imaging systems have resulted in an exponential surge in available technologies for portable fundus photography. Retinal cameras in the near future would have to cater to these needs by featuring a low-cost, portable design with automated controls and digitalized images with Web-based transfer. Conclusions: In this review, we aim to highlight the advances of fundus photography for retinal screening as well as discuss the advan...

Relationship between visual field sensitivity and retinal nerve fiber layer thickness as measured by scanning laser polarimetry.

Abstract: PURPOSE: To evaluate the strength and pattern of the relationship between visual field (VF) sensitivity and retinal nerve fiber layer (RNFL) thickness measurements by scanning laser polarimetry (SLP). METHODS: Fifty-four eyes of 54 normal subjects (age, 42 +/- 15 years; VF mean deviation [MD], -0.69 +/- 1.01 dB) and 51 eyes of 51 glaucoma patients (age, 66 +/- 14 years; VF MD, -6.92 +/- 5.43 dB) were imaged with an SLP using fixed corneal compensation (FCC) and variable corneal compensation (VCC). VF sensitivity was recorded in the dB and the 1/L scales. Linear and logarithmic relationships were sought globally and in six VF sectors. Relationships of VF and RNFL thickness with age were sought in normal subjects. RESULTS: Both VF sensitivity and RNFL thickness declined with age (as determined by the regression slope): -0.13% (P = 0.0005) and -0.64% (P = 0.0001) per year for dB and 1/L VF sensitivity, respectively, and -0.25% (P = 0.003) per year for VCC RNFL thickness. FCC RNFL thickness was not statistically significantly related to age. The relationship of VF sensitivity to VCC global (R(2) = 0.49) and sectoral (R(2) = 0.00-0.47) RNFL thickness was greater than for FCC global (R(2) = 0.12) and sectoral (R(2) = 0.00-0.21) RNFL thickness. Relationships were curvilinear with the dB scale, with logarithmic regression of dB VF sensitivity against RNFL thickness being significantly better than linear regression. Logarithmic regression of 1/L VF sensitivity against RNFL thickness was no better than linear regression for all sectors. There was no relationship between VF sensitivity and RNFL thickness in the temporal peripapillary RNFL sector. CONCLUSIONS: The strength of the structure/function relationships compare well with previous reports in the literature. The relationships were curvilinear with the dB scale and linear with the 1/L scale, and were much stronger with VCC than with FCC RNFL thickness measurements.

Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study

Abstract: Summary Background Deep learning has the potential to transform health care; however, substantial expertise is required to train such models. We sought to evaluate the utility of automated deep learning software to develop medical image diagnostic classifiers by health-care professionals with no coding—and no deep learning—expertise. Methods We used five publicly available open-source datasets: retinal fundus images (MESSIDOR); optical coherence tomography (OCT) images (Guangzhou Medical University and Shiley Eye Institute, version 3); images of skin lesions (Human Against Machine [HAM] 10000), and both paediatric and adult chest x-ray (CXR) images (Guangzhou Medical University and Shiley Eye Institute, version 3 and the National Institute of Health [NIH] dataset, respectively) to separately feed into a neural architecture search framework, hosted through Google Cloud AutoML, that automatically developed a deep learning architecture to classify common diseases. Sensitivity (recall), specificity, and positive predictive value (precision) were used to evaluate the diagnostic properties of the models. The discriminative performance was assessed using the area under the precision recall curve (AUPRC). In the case of the deep learning model developed on a subset of the HAM10000 dataset, we did external validation using the Edinburgh Dermofit Library dataset. Findings Diagnostic properties and discriminative performance from internal validations were high in the binary classification tasks (sensitivity 73·3–97·0%; specificity 67–100%; AUPRC 0·87–1·00). In the multiple classification tasks, the diagnostic properties ranged from 38% to 100% for sensitivity and from 67% to 100% for specificity. The discriminative performance in terms of AUPRC ranged from 0·57 to 1·00 in the five automated deep learning models. In an external validation using the Edinburgh Dermofit Library dataset, the automated deep learning model showed an AUPRC of 0·47, with a sensitivity of 49% and a positive predictive value of 52%. Interpretation All models, except the automated deep learning model trained on the multilabel classification task of the NIH CXR14 dataset, showed comparable discriminative performance and diagnostic properties to state-of-the-art performing deep learning algorithms. The performance in the external validation study was low. The quality of the open-access datasets (including insufficient information about patient flow and demographics) and the absence of measurement for precision, such as confidence intervals, constituted the major limitations of this study. The availability of automated deep learning platforms provide an opportunity for the medical community to enhance their understanding in model development and evaluation. Although the derivation of classification models without requiring a deep understanding of the mathematical, statistical, and programming principles is attractive, comparable performance to expertly designed models is limited to more elementary classification tasks. Furthermore, care should be placed in adhering to ethical principles when using these automated models to avoid discrimination and causing harm. Future studies should compare several application programming interfaces on thoroughly curated datasets. Funding National Institute for Health Research and Moorfields Eye Charity.