Low-Dose CT Image Enhancement Using Deep Learning

The application of ionizing radiation for diagnostic imaging is common around the globe. However, the process of imaging, itself, remains to be a relatively hazardous operation. Therefore, it is preferable to use as low a dose of ionizing radiation as possible, particularly in computed tomography (CT) imaging systems, where multiple x-ray operations are performed for the reconstruction of slices of body tissues. A popular method for radiation dose reduction in CT imaging is known as the quarter-dose technique, which reduces the x-ray dose but can cause a loss of image sharpness. Since CT image reconstruction from directional x-rays is a nonlinear process, it is analytically difficult to correct the effect of dose reduction on image quality. Recent and popular deep-learning approaches provide an intriguing possibility of image enhancement for low-dose artifacts. Some recent works propose combinations of multiple deep-learning and classical methods for this purpose, which over-complicate the process. However, it is observed here that the straight utilization of the well-known U-NET provides very successful results for the correction of low-dose artifacts. Blind tests with actual radiologists reveal that the U-NET enhanced quarter-dose CT images not only provide an immense visual improvement over the low-dose versions, but also become diagnostically preferable images, even when compared to their full-dose CT versions

Analysing the Large Decline in Coronary Heart Disease Mortality in the Icelandic Population Aged 25-74 between the Years 1981 and 2006

BACKGROUND: Coronary heart disease (CHD) mortality rates have been decreasing in Iceland since the 1980s. We examined how much of the decrease between 1981 and 2006 could be attributed to medical and surgical treatments and how much to changes in cardiovascular risk factors. METHODOLOGY: The previously validated IMPACT CHD mortality model was applied to the Icelandic population. The data sources were official statistics, national quality registers, published trials and meta-analyses, clinical audits and a series of national population surveys. PRINCIPAL FINDINGS: Between 1981 and 2006, CHD mortality rates in Iceland decreased by 80% in men and women aged 25 to 74 years, which resulted in 295 fewer deaths in 2006 than if the 1981 rates had persisted. Incidence of myocardial infarction (MI) decreased by 66% and resulted in some 500 fewer incident MI cases per year, which is a major determinant of possible deaths from MI. Based on the IMPACT model approximately 73% (lower and upper bound estimates: 54%-93%) of the mortality decrease was attributable to risk factor reductions: cholesterol 32%; smoking 22%; systolic blood pressure 22%, and physical inactivity 5% with adverse trends for diabetes (-5%), and obesity (-4%). Approximately 25% (lower and upper bound estimates: 8%-40%) of the mortality decrease was attributable to treatments in individuals: secondary prevention 8%; heart failure treatments 6%; acute coronary syndrome treatments 5%; revascularisation 3%; hypertension treatments 2%, and statins 0.5%. CONCLUSIONS: Almost three quarters of the large CHD mortality decrease in Iceland between 1981 and 2006 was attributable to reductions in major cardiovascular risk factors in the population. These findings emphasize the value of a comprehensive prevention strategy that promotes tobacco control and a healthier diet to reduce incidence of MI and highlights the potential importance of effective, evidence based medical treatments

Sustained Na⁺/H⁺ exchanger activation promotes gliotransmitter release from reactive hippocampal astrocytes following oxygen-glucose deprivation

Hypoxia ischemia (HI)-related brain injury is the major cause of long-term morbidity in neonates. One characteristic hallmark of neonatal HI is the development of reactive astrogliosis in the hippocampus. However, the impact of reactive astrogliosis in hippocampal damage after neonatal HI is not fully understood. In the current study, we investigated the role of Na +/H+ exchanger isoform 1 (NHE1) protein in mouse reactive hippocampal astrocyte function in an in vitro ischemia model (oxygen/glucose deprivation and reoxygenation, OGD/REOX). 2 h OGD significantly increased NHE1 protein expression and NHE1-mediated H+ efflux in hippocampal astrocytes. NHE1 activity remained stimulated during 1-5 h REOX and returned to the basal level at 24 h REOX. NHE1 activation in hippocampal astrocytes resulted in intracellular Na+ and Ca2+ overload. The latter was mediated by reversal of Na+/Ca2+ exchange. Hippocampal astrocytes also exhibited a robust release of gliotransmitters (glutamate and pro-inflammatory cytokines IL-6 and TNFα) during 1-24 h REOX. Interestingly, inhibition of NHE1 activity with its potent inhibitor HOE 642 not only reduced Na+ overload but also gliotransmitter release from hippocampal astrocytes. The noncompetitive excitatory amino acid transporter inhibitor TBOA showed a similar effect on blocking the glutamate release. Taken together, we concluded that NHE1 plays an essential role in maintaining H + homeostasis in hippocampal astrocytes. Over-stimulation of NHE1 activity following in vitro ischemia disrupts Na+ and Ca2+ homeostasis, which reduces Na+-dependent glutamate uptake and promotes release of glutamate and cytokines from reactive astrocytes. Therefore, blocking sustained NHE1 activation in reactive astrocytes may provide neuroprotection following HI. © 2014 Cengiz et al

Assessment of endogenous fibrinolysis in clinical using novel tests - Ready for clinical roll-out?

© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.The occurrence of thrombotic complications, which can result in excess mortality and morbidity, represent an imbalance between the pro-thrombotic and fibrinolytic equilibrium.The mainstay treatment of these complications involves the use of antithrombotic agents but despite advances in pharmacotherapy, there remains a significant proportion of patients who continue to remain at risk.Endogenous fibrinolysis is a physiological counter-measure against lasting thrombosis and may be measured using several techniques to identify higher risk patients who may benefit from more aggressive pharmacotherapy. However, the assessment of the fibrinolytic systemis not yet accepted into routine clinical practice.In this review, we will revisit the different methods of assessing endogenous fibrinolysis (factorial assays, turbidimetric lysis assays, viscoelastic and the global thrombosis tests), including the strengths, limitations, correlation to clinical outcomes of each method and howwe might integrate the assessment of endogenous fibrinolysis into clinical practice in the future.Peer reviewedFinal Published versio

Systematic review with meta-analysis of the epidemiological evidence relating smoking to COPD, chronic bronchitis and emphysema

<p>Abstract</p> <p>Background</p> <p>Smoking is a known cause of the outcomes COPD, chronic bronchitis (CB) and emphysema, but no previous systematic review exists. We summarize evidence for various smoking indices.</p> <p>Methods</p> <p>Based on MEDLINE searches and other sources we obtained papers published to 2006 describing epidemiological studies relating incidence or prevalence of these outcomes to smoking. Studies in children or adolescents, or in populations at high respiratory disease risk or with co-existing diseases were excluded. Study-specific data were extracted on design, exposures and outcomes considered, and confounder adjustment. For each outcome RRs/ORs and 95% CIs were extracted for ever, current and ex smoking and various dose response indices, and meta-analyses and meta-regressions conducted to determine how relationships were modified by various study and RR characteristics.</p> <p>Results</p> <p>Of 218 studies identified, 133 provide data for COPD, 101 for CB and 28 for emphysema. RR estimates are markedly heterogeneous. Based on random-effects meta-analyses of most-adjusted RR/ORs, estimates are elevated for ever smoking (COPD 2.89, CI 2.63-3.17, n = 129 RRs; CB 2.69, 2.50-2.90, n = 114; emphysema 4.51, 3.38-6.02, n = 28), current smoking (COPD 3.51, 3.08-3.99; CB 3.41, 3.13-3.72; emphysema 4.87, 2.83-8.41) and ex smoking (COPD 2.35, 2.11-2.63; CB 1.63, 1.50-1.78; emphysema 3.52, 2.51-4.94). For COPD, RRs are higher for males, for studies conducted in North America, for cigarette smoking rather than any product smoking, and where the unexposed base is never smoking any product, and are markedly lower when asthma is included in the COPD definition. Variations by sex, continent, smoking product and unexposed group are in the same direction for CB, but less clearly demonstrated. For all outcomes RRs are higher when based on mortality, and for COPD are markedly lower when based on lung function. For all outcomes, risk increases with amount smoked and pack-years. Limited data show risk decreases with increasing starting age for COPD and CB and with increasing quitting duration for COPD. No clear relationship is seen with duration of smoking.</p> <p>Conclusions</p> <p>The results confirm and quantify the causal relationships with smoking.</p

Abstracts from the 20th International Symposium on Signal Transduction at the Blood-Brain Barriers

https://deepblue.lib.umich.edu/bitstream/2027.42/138963/1/12987_2017_Article_71.pd

Treatment of skewed multi-dimensional training data to facilitate the task of engineering neural models

WOS: 000246315200015Successful application of neural network models relies heavily on problem-dependent internal parameters. As the theory does not facilitate the choice of the optimal parameters of neural models, these can solely be obtained through a tedious trial-and-error process. The process requires performing multiple training simulations with various network parameters, until satisfactory performance criteria of a neural model are met. In literature, it has been shown that neural models are not consistently good in prediction under highly skewed data. Consequently, the cost of engineering neural models rises in such circumstance to seek for appropriate internal parameters. In this paper the aim is to show that a recently proposed treatment of highly skewed data eases the task of practitioners in engineering neural network models to meet satisfactory performance criteria. As the applications of neural models grows dramatically in diverse engineering domains, the understanding of the treatment show indispensable practical values. (c) 2006 Elsevier Ltd. All rights reserved

Treatment of multi-dimensional data to enhance neural network estimators in regression problems

WOS: 000242979100033This paper proposes and explains a data treatment technique to improve the accuracy of a neural network estimator in regression problems, where multi-dimensional input data set is highly skewed and non-normally distributed. The proposed treatment modifies the distribution characteristics of the data set. The prediction of the suspended sediment, which is an important problem in river engineering applications, will be considered as a case study. Conventional approaches lack in providing high accuracy due to the inherently employed simplicity in order to obtain empirical formulae. On the other hand, artificial neural networks are able to model the non-linear characteristics of the mechanism of the sediment transport and have a growing body of applications in diverse applications in civil engineering. It will be shown that a significant enhancement and superior score in accuracy, compared with the classical approaches, are obtainable when the proposed treatment is employed. The proposed technique is an extension to the understanding of the practical aspects of neural computing applications. Therefore the outcome of the present study is important as it is applicable to any scenario where neural network approaches are involved. (C) 2006 Elsevier Ltd. All rights reserved