Imaging features and ultraearly hematoma growth in intracerebral hemorrhage associated with COVID-19

Purpose: Intracerebral hemorrhage (ICH) is an uncommon but deadly event in patients with COVID-19 and its imaging features remain poorly characterized. We aimed to describe the clinical and imaging features of COVID-19-associated ICH. Methods: Multicenter, retrospective, case-control analysis comparing ICH in COVID-19 patients (COV19\u2009+) versus controls without COVID-19 (COV19\u2009-). Clinical presentation, laboratory markers, and severity of COVID-19 disease were recorded. Non-contrast computed tomography (NCCT) markers (intrahematoma hypodensity, heterogeneous density, blend sign, irregular shape fluid level), ICH location, and hematoma volume (ABC/2 method) were analyzed. The outcome of interest was ultraearly hematoma growth (uHG) (defined as NCCT baseline ICH volume/onset-to-imaging time), whose predictors were explored with multivariable linear regression. Results: A total of 33 COV19\u2009+\u2009patients and 321 COV19\u2009-\u2009controls with ICH were included. Demographic characteristics and vascular risk factors were similar in the two groups. Multifocal ICH and NCCT markers were significantly more common in the COV19\u2009+\u2009population. uHG was significantly higher among COV19\u2009+\u2009patients (median 6.2 mL/h vs 3.1 mL/h, p\u2009=\u20090.027), and this finding remained significant after adjustment for confounding factors (systolic blood pressure, antiplatelet and anticoagulant therapy), in linear regression (B(SE)\u2009=\u20090.31 (0.11), p\u2009=\u20090.005). This association remained consistent also after the exclusion of patients under anticoagulant treatment (B(SE)\u2009=\u20090.29 (0.13), p\u2009=\u20090.026). Conclusions: ICH in COV19\u2009+\u2009patients has distinct NCCT imaging features and a higher speed of bleeding. This association is not mediated by antithrombotic therapy and deserves further research to characterize the underlying biological mechanisms

Muscle quantitative MRI as a novel biomarker in hereditary transthyretin amyloidosis with polyneuropathy: a cross-sectional study

BACKGROUND: The development of reproducible and sensitive outcome measures has been challenging in hereditary transthyretin (ATTRv) amyloidosis. Recently, quantification of intramuscular fat by magnetic resonance imaging (MRI) has proven as a sensitive marker in patients with other genetic neuropathies. The aim of this study was to investigate the role of muscle quantitative MRI (qMRI) as an outcome measure in ATTRv. METHODS: Calf- and thigh-centered multi-echo T2-weighted spin-echo and gradient-echo sequences were obtained in patients with ATTRv amyloidosis with polyneuropathy (n = 24) and healthy controls (n = 12). Water T2 (wT2) and fat fraction (FF) were calculated. Neurological assessment was performed in all ATTRv subjects. Quantitative MRI parameters were correlated with clinical and neurophysiological measures of disease severity. RESULTS: Quantitative imaging revealed significantly higher FF in lower limb muscles in patients with ATTRv amyloidosis compared to controls. In addition, wT2 was significantly higher in ATTRv patients. There was prominent involvement of the posterior compartment of the thighs. Noticeably, FF and wT2 did not exhibit a length-dependent pattern in ATTRv patients. MRI biomarkers correlated with previously validated clinical outcome measures, Polyneuropathy Disability scoring system, Neuropathy Impairment Score (NIS) and NIS-lower limb, and neurophysiological parameters of axonal damage regardless of age, sex, treatment and TTR mutation. CONCLUSIONS: Muscle qMRI revealed significant difference between ATTRv and healthy controls. MRI biomarkers showed high correlation with clinical and neurophysiological measures of disease severity making qMRI as a promising tool to be further investigated in longitudinal studies to assess its role at monitoring onset, progression, and therapy efficacy for future clinical trials on this treatable condition

Modeled deposition of nitrogen and sulfur in Europe estimated by 14 air quality model systems: evaluation, effects of changes in emissions and implications for habitat protection

The evaluation and intercomparison of air quality models is key to reducing model errors and uncertainty. The projects AQMEII3 and EURODELTA-Trends, in the framework of the Task Force on Hemispheric Transport of Air Pollutants and the Task Force on Measurements and Modelling, respectively (both task forces under the UNECE Convention on the Long Range Transport of Air Pollution, LTRAP), have brought together various regional air quality models to analyze their performance in terms of air concentrations and wet deposition, as well as to address other specific objectives. This paper jointly examines the results from both project communities by intercomparing and evaluating the deposition estimates of reduced and oxidized nitrogen (N) and sulfur (S) in Europe simulated by 14 air quality model systems for the year 2010. An accurate estimate of deposition is key to an accurate simulation of atmospheric concentrations. In addition, deposition fluxes are increasingly being used to estimate ecological impacts. It is therefore important to know by how much model results differ and how well they agree with observed values, at least when comparison with observations is possible, such as in the case of wet deposition. This study reveals a large variability between the wet deposition estimates of the models, with some performing acceptably (according to previously defined criteria) and others underestimating wet deposition rates. For dry deposition, there are also considerable differences between the model estimates. An ensemble of the models with the best performance for N wet deposition was made and used to explore the implications of N deposition in the conservation of protected European habitats. Exceedances of empirical critical loads were calculated for the most common habitats at a resolution of 100  ×  100 m2 within the Natura 2000 network, and the habitats with the largest areas showing exceedances are determined. Moreover, simulations with reduced emissions in selected source areas indicated a fairly linear relationship between reductions in emissions and changes in the deposition rates of N and S. An approximate 20 % reduction in N and S deposition in Europe is found when emissions at a global scale are reduced by the same amount. European emissions are by far the main contributor to deposition in Europe, whereas the reduction in deposition due to a decrease in emissions in North America is very small and confined to the western part of the domain. Reductions in European emissions led to substantial decreases in the protected habitat areas with critical load exceedances (halving the exceeded area for certain habitats), whereas no change was found, on average, when reducing North American emissions in terms of average values per habitat

The prevalence of autosomal dominant polycystic kidney disease (ADPKD): A meta-analysis of European literature and prevalence evaluation in the Italian province of Modena suggest that ADPKD is a rare and underdiagnosed condition

ADPKD is erroneously perceived as a not rare condition, which is mainly due to the repeated citation of a mistaken interpretation of old epidemiological data, as reported in the Dalgaard's work (1957). Even if ADPKD is not a common condition, the correct prevalence of ADPKD in the general population is uncertain, with a wide range of estimations reported by different authors. In this work, we have performed a meta-analysis of available epidemiological data in the European literature. Furthermore we collected the diagnosis and clinical data of ADPKD in a province in the north of Italy (Modena). We describe the point and predicted prevalence of ADPKD, as well as the main clinical characteristics of ADPKD in this region

Interstitial Lung Disease after Kidney Transplantation and the Role of Everolimus

Background Kidney transplant recipients are at higher risk of developing pulmonary complications related to immunosuppression, and inhibitor of the mammalian target of rapamycin (mTORi) has been reported as a potential cause. Methods Five hundred kidney-transplanted patients were retrospectively analyzed for pulmonary complications on the basis of clinical and instrumental data (chest radiography, high-resolution computed tomography, broncho-alveolar lavage, oximetry). Results We found 26 interstitial lung diseases (ILD) (16%): 12 cases (46.2%) were from infections (42.8% by Pneumocystis jirovecii) and 14 cases of ILD (53.8%) resulted as drug-induced ILD (DI-ILD). According to anti-rejection protocols, DI-ILD occurred in 8 patients (57%) while on triple regimen including steroids, everolimus (EVL), and cyclosporine (CyA) and in 6 patients on double regimen with steroids and mTORi: EVL or sirolimus (43%). In ILD+ patients, everolimus trough-concentration (EVLTLC) and cyclosporine (2nd-hour concentration: CyAC2) levels were higher than in patients in the same regimen but with ILD- (EVLTLC [ng/mL] 9.84 versus 6.85; CyAC2 [ng/mL] 303.97 versus 298.56). The formula that used the combined blood levels of both drugs (EVLTLC + CyAC2/100) resulted in a significant difference between groups of patients (12.88 ± 1.61 versus 9.83 ± 1.91). Applying receiver operator characteristic curve (ROC) analysis to detect risk of developing ILD when on combined protocol with EVL and CyA, we obtained an area under the curve of 0.8622 (P =.0081) and 0.9082 (P =.0028), respectively, when using EVLTLC or the combination formula with both drugs. Conclusions In renal transplant patients, we obtained a relationship of ILD to specific drug concentration. On the basis of ROC analysis, patients on EVL and CyA combined protocol are at risk of ILD when EVLTLC is >9.03 ng/mL or >11.41 when a formula with summation of EVLTLC and CyAC2 is used

On the automation of high level synthesis of convolutional neural networks

Convolutional Neural Networks (CNNs) are a particular type of Artificial Neural Networks (ANNs) inspired by cells in the primary visual cortex of animals, and represent the state of the art in image recognition and classification. Nowadays, such supervised learning technique is very popular in Big Data analytics. In this context, due to the huge amount of data to be processed, it is crucial to find techniques to speed up the computation. In particular, the dataflow pattern of CNN algorithm results to be suitable for hardware acceleration. This paper proposes a framework to automatically generate a hardware implementation of CNNs on Field Programmable Gate Arrays (FPGAs), based on the High Level Synthesis (HLS) of configurable offline-trained networks

Hardware design automation of convolutional neural networks

Convolutional Neural Networks (CNNs) are a variation of feed-forward Neural Networks inspired by the biological process in the visual cortex of animals. The interest in this supervised learning algorithm has rapidly grown in many fields like image and video recognition and natural language processing. Nowadays they have become the state of the art in various applications like mobile robot vision, video surveillance and Big Data analytics. The specific computation pattern of CNNs results to be highly suitable for hardware acceleration, in fact different types of accelerators have been proposed based on GPU, Field Programmable Gate Array (FPGA) and ASIC. In particular, in the embedded systems context, due to real time and power consumption challenges, it is crucial to find the right tradeoff between performance, energy efficiency, fast development round and cost. This work proposes a framework meant as a tool for the user to accelerate and simplify the design and the implementation of CNNs on FPGAS by leveraging High Level Synthesis, still providing a certain level of customization of the hardware design