Hypertensive eye disease

Hypertensive eye disease includes a spectrum of pathological changes, the most well known being hypertensive retinopathy. Other commonly involved parts of the eye in hypertension include the choroid and optic nerve, sometimes referred to as hypertensive choroidopathy and hypertensive optic neuropathy. Together, hypertensive eye disease develops in response to acute and/or chronic elevation of blood pressure. Major advances in research over the past three decades have greatly enhanced our understanding of the epidemiology, systemic associations and clinical implications of hypertensive eye disease, particularly hypertensive retinopathy. Traditionally diagnosed via a clinical funduscopic examination, but increasingly documented on digital retinal fundus photographs, hypertensive retinopathy has long been considered a marker of systemic target organ damage (for example, kidney disease) elsewhere in the body. Epidemiological studies indicate that hypertensive retinopathy signs are commonly seen in the general adult population, are associated with subclinical measures of vascular disease and predict risk of incident clinical cardiovascular events. New technologies, including development of non-invasive optical coherence tomography angiography, artificial intelligence and mobile ocular imaging instruments, have allowed further assessment and understanding of the ocular manifestations of hypertension and increase the potential that ocular imaging could be used for hypertension management and cardiovascular risk stratification

Viewing the personality traits through a cerebellar lens. A focus on the constructs of novelty seeking, harm avoidance, and alexithymia

The variance in the range of personality trait expression appears to be linked to structural variance in specific brain regions. In evidencing associations between personality factors and neurobiological measures, it seems evident that the cerebellum has not been up to now thought as having a key role in personality. This paper will review the most recent structural and functional neuroimaging literature that engages the cerebellum in personality traits, as novelty seeking and harm avoidance, and it will discuss the findings in the context of contemporary theories of affective and cognitive cerebellar function. By using region of interest (ROI)- and voxel-based approaches, we recently evidenced that the cerebellar volumes correlate positively with novelty seeking scores and negatively with harm avoidance scores. Subjects who search for new situations as a novelty seeker does (and a harm avoiding does not do) show a different engagement of their cerebellar circuitries in order to rapidly adapt to changing environments. The emerging model of cerebellar functionality may explain how the cerebellar abilities in planning, controlling, and putting into action the behavior are associated to normal or abnormal personality constructs. In this framework, it is worth reporting that increased cerebellar volumes are even associated with high scores in alexithymia, construct of personality characterized by impairment in cognitive, emotional, and affective processing. On such a basis, it seems necessary to go over the traditional cortico-centric view of personality constructs and to address the function of the cerebellar system in sustaining aspects of motivational network that characterizes the different temperamental trait

Evaluating Indices of Delayed Cerebral Ischemia and Poor Outcomes after Subarachnoid Hemorrhage: The Role of Cerebral Perfusion Pressure in Disease Pathogenesis

Background and Purpose: Delayed cerebral ischemia (DCI) and Hunt and Hess (HH) grade are known risk factors for poor outcomes after aneurysmal subarachnoid hemorrhage (aSAH). DCI is often attributed to focal perfusion deficit (vasospasm/infarction). Global perfusion deficit (e.g. inadequate cerebral perfusion pressure (CPP)) can impair cerebral blood flow (CBF). The relationship between CPP and DCI remains unclear. Further, the exact mechanism of how HH grade relates to poor outcomes is uncertain. This study aimed to describe the temporal profiles of CPP change and to investigate the relationship between CPP, DCI, HH, and post-aSAH outcomes. Method: DCI was defined as clinical deterioration due to impaired CBF. Growth curve analysis was used to examine temporal profiles of CPP change. Logistic regression was utilized to examine the association between DCI and percentages of CPP values >110, >100, 100 or >110 mmHg, the odds of DCI increased by 1.21 and 1.43, respectively. For every 10 mmHg increase in CPP, the odds of DCI increased by 2.78 (95%CI 2.00-3.87). High CPP was associated with earlier onset of DCI (p<.001). DCI did not mediate the relationship between HH and outcomes. Conclusions: When used prophylactically, induced hypertension contributes to higher CPP values. Based on the CPP trends/correlations observed, induced hypertension may not confer expected benefits in patients with aSAH. Findings raise concerns about safety of induced hypertension and the need for determining limits for hypertension, which current guidelines lack

White matter lesions characterise brain involvement in moderate to severe chronic obstructive pulmonary disease, but cerebral atrophy does not.

BACKGROUND: Brain pathology is relatively unexplored in chronic obstructive pulmonary disease (COPD). This study is a comprehensive investigation of grey matter (GM) and white matter (WM) changes and how these relate to disease severity and cognitive function. METHODS: T1-weighted and fluid-attenuated inversion recovery images were acquired for 31 stable COPD patients (FEV1 52.1% pred., PaO2 10.1 kPa) and 24 age, gender-matched controls. T1-weighted images were segmented into GM, WM and cerebrospinal fluid (CSF) tissue classes using a semi-automated procedure optimised for use with this cohort. This procedure allows, cohort-specific anatomical features to be captured, white matter lesions (WMLs) to be identified and includes a tissue repair step to correct for misclassification caused by WMLs. Tissue volumes and cortical thickness were calculated from the resulting segmentations. Additionally, a fully-automated pipeline was used to calculate localised cortical surface and gyrification. WM and GM tissue volumes, the tissue volume ratio (indicator of atrophy), average cortical thickness, and the number, size, and volume of white matter lesions (WMLs) were analysed across the whole-brain and regionally - for each anatomical lobe and the deep-GM. The hippocampus was investigated as a region-of-interest. Localised (voxel-wise and vertex-wise) variations in cortical gyrification, GM density and cortical thickness, were also investigated. Statistical models controlling for age and gender were used to test for between-group differences and within-group correlations. Robust statistical approaches ensured the family-wise error rate was controlled in regional and local analyses. RESULTS: There were no significant differences in global, regional, or local measures of GM between patients and controls, however, patients had an increased volume (p = 0.02) and size (p = 0.04) of WMLs. In patients, greater normalised hippocampal volume positively correlated with exacerbation frequency (p = 0.04), and greater WML volume was associated with worse episodic memory (p = 0.05). A negative relationship between WML and FEV1 % pred. approached significance (p = 0.06). CONCLUSIONS: There was no evidence of cerebral atrophy within this cohort of stable COPD patients, with moderate airflow obstruction. However, there were indications of WM damage consistent with an ischaemic pathology. It cannot be concluded whether this represents a specific COPD, or smoking-related, effect

Digital solution for detection of undiagnosed diabetes using machine learning-based retinal image analysis

Introduction Undiagnosed diabetes is a global health issue. Previous studies have estimated that about 24.1%–75.1% of all diabetes cases are undiagnosed, leading to more diabetic complications and inducing huge healthcare costs. Many current methods for diabetes diagnosis rely on metabolic indices and are subject to considerable variability. In contrast, a digital approach based on retinal image represents a stable marker of overall glycemic status.Research design and methods Our study involves 2221 subjects for developing a classification model, with 945 subjects with diabetes and 1276 controls. The training data included 70% and the testing data 30% of the subjects. All subjects had their retinal images taken using a non-mydriatic fundus camera. Two separate data sets were used for external validation. The Hong Kong testing data contain 734 controls without diabetes and 660 subjects with diabetes, and the UK testing data have 1682 subjects with diabetes.Results The 10-fold cross-validation using the support vector machine approach has a sensitivity of 92% and a specificity of 96.2%. The separate testing data from Hong Kong provided a sensitivity of 99.5% and a specificity of 91.1%. For the UK testing data, the sensitivity is 98.0%. The accuracy of the Caucasian retinal images is comparable with that of the Asian data. It implies that the digital method can be applied globally. Those with diabetes complications in both Hong Kong and UK data have a higher probability of risk of diabetes compared with diabetes subjects without complications.Conclusions A digital machine learning-based method to estimate the risk of diabetes based on retinal images has been developed and validated using both Asian and Caucasian data. Retinal image analysis is a fast, convenient, and non-invasive technique for community health applications. In addition, it is an ideal solution for undiagnosed diabetes prescreening