Glaucoma: the retina and beyond

Over 60 million people worldwide are diagnosed with glaucomatous optic neuropathy, which is estimated to be responsible for 8.4 million cases of irreversible blindness globally. Glaucoma is associated with characteristic damage to the optic nerve and patterns of visual field loss which principally involves the loss of retinal ganglion cells (RGCs). At present, intraocular pressure (IOP) presents the only modifiable risk factor for glaucoma, although RGC and vision loss can continue in patients despite well-controlled IOP. This, coupled with the present inability to diagnose glaucoma until relatively late in the disease process, has led to intense investigations towards the development of novel techniques for the early diagnosis of disease. This review outlines our current understanding of the potential mechanisms underlying RGC and axonal loss in glaucoma. Similarities between glaucoma and other neurodegenerative diseases of the central nervous system are drawn before an overview of recent developments in techniques for monitoring RGC health is provided, including recent progress towards the development of RGC specific contrast agents. The review concludes by discussing techniques to assess glaucomatous changes in the brain using MRI and the clinical relevance of glaucomatous-associated changes in the visual centres of the brain

Automatic quantitative analysis of experimental primary and secondary retinal neurodegeneration: implications for optic neuropathies.

Secondary neurodegeneration is thought to play an important role in the pathology of neurodegenerative disease, which potential therapies may target. However, the quantitative assessment of the degree of secondary neurodegeneration is difficult. The present study describes a novel algorithm from which estimates of primary and secondary degeneration are computed using well-established rodent models of partial optic nerve transection (pONT) and ocular hypertension (OHT). Brn3-labelled retinal ganglion cells (RGCs) were identified in whole-retinal mounts from which RGC density, nearest neighbour distances and regularity indices were determined. The spatial distribution and rate of RGC loss were assessed and the percentage of primary and secondary degeneration in each non-overlapping segment was calculated. Mean RGC number (82 592±681) and RGC density (1695±23.3 RGC/mm(2)) in naïve eyes were comparable with previous studies, with an average decline in RGC density of 71±17 and 23±5% over the time course of pONT and OHT models, respectively. Spatial analysis revealed greatest RGC loss in the superior and central retina in pONT, but significant RGC loss in the inferior retina from 3 days post model induction. In comparison, there was no significant difference between superior and inferior retina after OHT induction, and RGC loss occurred mainly along the superior/inferior axis (~30%) versus the nasal-temporal axis (~15%). Intriguingly, a significant loss of RGCs was also observed in contralateral eyes in experimental OHT. In conclusion, a novel algorithm to automatically segment Brn3a-labelled retinal whole-mounts into non-overlapping segments is described, which enables automated spatial and temporal segmentation of RGCs, revealing heterogeneity in the spatial distribution of primary and secondary degenerative processes. This method provides an attractive means to rapidly determine the efficacy of neuroprotective therapies with implications for any neurodegenerative disorder affecting the retina

Conditional Sampling for Max-Stable Processes with a Mixed Moving Maxima Representation

This paper deals with the question of conditional sampling and prediction for the class of stationary max-stable processes which allow for a mixed moving maxima representation. We develop an exact procedure for conditional sampling using the Poisson point process structure of such processes. For explicit calculations we restrict ourselves to the one-dimensional case and use a finite number of shape functions satisfying some regularity conditions. For more general shape functions approximation techniques are presented. Our algorithm is applied to the Smith process and the Brown-Resnick process. Finally, we compare our computational results to other approaches. Here, the algorithm for Gaussian processes with transformed marginals turns out to be surprisingly competitive.Comment: 35 pages; version accepted for publication in Extremes. The final publication is available at http://link.springer.co

Improving the quality of orthopaedic elective and trauma operative notes: A completed audit loop study

Introduction: Good medical practice dictates that comprehensive documentation of all surgical procedures is paramount in maintaining a high standard of patient care. This study audited the quality of operative note keeping for elective and trauma procedures against the standards set by the British Orthopaedic Association (BOA) and The Royal College of Surgeons of England (RCSE) guidelines. Patients and methods: A retrospective assessment of the operative notes of every patient undergoing a total knee and hip replacement (elective cases) was carried out over a period of 2 months. Data recorded were compared against BOA guidelines. Within this time a randomised selection of trauma operative notes were also assessed, and the recorded data were compared against RCSE guidelines. Change in practice was implemented and the audit cycle completed. A total of 173 operative notes were evaluated. Results: There was a significant improvement (p-value < 0.05) in the quality of total knee replacement notes, with an increase in the percentage of data points from 68.6% to 93%. Similarly the quality of total hip replacement notes showed significant improvement (p-value < 0.01) with an increase in the percentage of data points from 67.5% to 86%. However trauma operative notes showed minimal improvement. Discussion: This study showed that the quality of elective operative notes was improved through surgeon education and the circulation of a guideline based electronic operative note. We have further plans to implement procedure specific notes for the most common types of trauma cases to help improve the quality of trauma operative notes

Assessing anesthetic activity through modulation of the membrane dipole potential

There is great individual variation in response to general anaesthetics leading to difficulties in optimal dosing and sometimes even accidental awareness during general anaesthesia (AAGA). AAGA is a rare but potentially devastating complication affecting between 0.1% and 2% of patients undergoing surgery. The development of novel, personalised screening techniques to accurately predict a patient's response to GA and the risk of AAGA remains an unmet clinical need. In the present study we demonstrate the principle of using a fluorescent reporter of the membrane dipole potential, di-8-ANEPPs, as a novel method to monitor anaesthetic activity using a well-described inducer/non-inducer pair. The membrane dipole potential has previously been suggested to contribute a novel mechanism of anaesthetic action (Qin et al 1995). We show the fluorescence ratio of di-8-ANEPPs changed in response to physiological concentrations of the anaesthetic 1-chloro-1,2,2-trifluorocyclobutane (F3) but not the structurally similar non-inducer 1,2-dichlorohexafluorocyclobutane (F6) to artificial membranes and in vitro retinal cell systems. Modulation of the membrane dipole provides an explanation to overcome limitations associated with alternative membrane-mediated mechanisms of GA action. Furthermore, by combining this technique with non-invasive retinal imaging technologies, we propose this technique could provide a novel and non-invasive technique to monitor GA susceptibility and identify patients at risk of AAGA

Annexins in glaucoma

Glaucoma is one of the leading causes of irreversible visual loss, which has been estimated to affect 3.5% of those over 40 years old and projected to affect a total of 112 million people by 2040. Such a dramatic increase in affected patients demonstrates the need for continual improvement in the way we diagnose and treat this condition. Annexin A5 is a 36 kDa protein that is ubiquitously expressed in humans and is studied as an indicator of apoptosis in several fields. This molecule has a high calcium-dependent affinity for phosphatidylserine, a cell membrane phospholipid externalized to the outer cell membrane in early apoptosis. The DARC (Detection of Apoptosing Retinal Cells) project uses fluorescently-labelled annexin A5 to assess glaucomatous degeneration, the inherent process of which is the apoptosis of retinal ganglion cells. Furthermore, this project has conducted investigation of the retinal apoptosis in the neurodegenerative conditions of the eye and brain. In this present study, we summarized the use of annexin A5 as a marker of apoptosis in the eye. We also relayed the progress of the DARC project, developing real-time imaging of retinal ganglion cell apoptosis in vivo from the experimental models of disease and identifying mechanisms underlying neurodegeneration and its treatments, which has been applied to the first human clinical trials. DARC has potential as a biomarker in neurodegeneration, especially in the research of novel treatments, and could be a useful tool for the diagnosis and monitoring of glaucoma

Subitizing with Variational Autoencoders

Numerosity, the number of objects in a set, is a basic property of a given visual scene. Many animals develop the perceptual ability to subitize: the near-instantaneous identification of the numerosity in small sets of visual items. In computer vision, it has been shown that numerosity emerges as a statistical property in neural networks during unsupervised learning from simple synthetic images. In this work, we focus on more complex natural images using unsupervised hierarchical neural networks. Specifically, we show that variational autoencoders are able to spontaneously perform subitizing after training without supervision on a large amount images from the Salient Object Subitizing dataset. While our method is unable to outperform supervised convolutional networks for subitizing, we observe that the networks learn to encode numerosity as basic visual property. Moreover, we find that the learned representations are likely invariant to object area; an observation in alignment with studies on biological neural networks in cognitive neuroscience