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Several noninvasive vascular biomarkers have been proposed to improve risk stratification for atherothrombotic events. To identify biomarkers suitable for detecting intermediate-risk individuals who might benefit from lipid-lowering treatment in primary prevention, the present study tested the association of plasma LDL-cholesterol with coronary artery calcification (CAC) Agatston score, high carotid and femoral intima-media thickness (IMT), low carotid distensibility and high carotid-femoral pulse-wave velocity in 260 asymptomatic individuals at intermediate cardiovascular risk and without diabetes and lipid-lowering treatment. High or low vascular biomarkers were considered when their value was above the 95th or below the 5th percentile, respectively, of the distribution in the healthy or in the study population. LDL-cholesterol was independently associated with the CAC score = 0 (OR 0.67; 95%CI 0.48-0.92, p = 0.01), CAC score > 100 (1.59; 1.08-2.39, p = 0.01) and high common femoral artery (CFA) IMT (1.89; 1.19-3.06, p < 0.01), but not with other biomarkers. Our data confirm that in individuals at intermediate risk, lipid-lowering treatment can be avoided in the presence of a CAC score = 0, while it should be used with a CAC score > 100. CFA IMT could represent a useful biomarker for decisions regarding lipid-lowering treatment. However, sex- and age-specific reference values should be established in a large healthy population.

Various metals have toxic effects by the inhalation route, and electric arc furnace (EAF) steel slag is known to contain metals with a potential for toxicity to humans. In some states, EAF slag is applied to unpaved (gravel) roads as a low-cost supplement to limestone and other crushed stone, where it may be a public health concern for the local population. This study compared the mass of selected metals in the PM 10 size fraction of fugitive dust from roads where slag was applied to metals in fugitive dust where slag was not applied. Manganese, designated by the EPA as a hazardous air pollutant (HAP) and one of the primary metals of concern in the slag, was 1.3 times more concentrated in the PM 10 fraction from the slag-covered roads as compared to the PM 10 fraction from the non-slag-covered roads, but that increase was not significant ( p = 0.26). Other metals detected in the airborne dust from both slag-covered and non-slag-covered roads that are also designated as HAPs are antimony, arsenic, chromium, cobalt, lead, nickel, and selenium. In addition, hourly sampling of PM 10 and metals in the PM 10 fraction was conducted at one of the sample locations where slag had been applied to the road. Manganese mass in the PM 10 was positively correlated (Spearman r = 0.86) with the particulate mass in the PM 10 . Wind direction and the interaction of traffic and wind direction were found to be statistically significant factors affecting manganese concentrations in the fugitive emissions from the road to which EAF slag had been applied. This research demonstrated that application of steel slag can result in elevated levels of manganese in the airborne dust generated by vehicular traffic on the unpaved roadway., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Importance: Although imaging has become a standard tool of modern medicine, its widespread use has been paralleled by an increasing cumulative radiation dose to patients despite technological advancements and campaigns calling for better awareness and minimization of unnecessary exposures., Objective: To assess patients' knowledge about medical radiation and related risks., Design, Setting, and Participants: A survey study of hospitals in Italy was conducted; all patients in waiting rooms for medical imaging procedures before undergoing imaging examinations at 16 teaching and nonteaching hospitals were approached to take the survey. The survey was performed from June 1, 2019, to May 31, 2020., Main Outcomes and Measures: Survey respondents' basic knowledge of ionizing radiation levels and health risks, earlier imaging tests performed, and information and communication about radiation protection issues., Results: Among 3039 patients invited to participate, the response rate was 94.3% (n = 2866). Participants included 1531 women (53.4%); mean (SD) age was 44.9 (17.3) years. Of the 2866 participants, 1529 (53.3%) were aware of the existence of natural sources of ionizing radiation. Mammography (1101 [38.4%]) and magnetic resonance imaging (1231 [43.0%]) were categorized as radiation-based imaging modalities. More than half of the 2866 patients (1579 [55.1%]; P = .03) did not know that chest computed tomography delivers a larger dose of radiation than chest radiography, and only 1499 (52.3%) knew that radiation can be emitted after nuclear medicine examinations (P = .004). A total of 667 patients (23.3%) believed that radiation risks were unrelated to age, 1273 (44.4%) deemed their knowledge about radiation risks inadequate, and 2305 (80.4%) preferred to be informed about radiation risks by medical staff. A better knowledge of radiation issues was associated with receiving information from health care professionals (odds ratio [OR], 1.71; 95% CI, 1.43-2.03; P < .001) and having a higher educational level (intermediate vs low: OR, 1.48; 95% CI, 1.17-1.88; P < .001; high vs low: OR, 2.68; 95% CI, 2.09-3.43; P < .001)., Conclusions and Relevance: The results of this survey suggest that patients undergoing medical imaging procedures have overall limited knowledge about medical radiation. Intervention to achieve better patient awareness of radiation risks related to medical exposures may be beneficial.

Compliance of member States to the Treaty on the Non-Proliferation of Nuclear Weapons is monitored through nuclear safeguards. The Passive Gamma Emission Tomography (PGET) system is a novel instrument developed within the framework of the International Atomic Energy Agency (IAEA) project JNT 1510, which included the European Commission, Finland, Hungary and Sweden. The PGET is used for the verification of spent nuclear fuel stored in water pools. Advanced image reconstruction techniques are crucial for obtaining high-quality cross-sectional images of the spent-fuel bundle to allow inspectors of the IAEA to monitor nuclear material and promptly identify its diversion. In this work, we have developed a software suite to accurately reconstruct the spent-fuel cross sectional image, automatically identify present fuel rods, and estimate their activity. Unique image reconstruction challenges are posed by the measurement of spent fuel, due to its high activity and the self-attenuation. While the former is mitigated by detector physical collimation, we implemented a linear forward model to model the detector responses to the fuel rods inside the PGET, to account for the latter. The image reconstruction is performed by solving a regularized linear inverse problem using the fast-iterative shrinkage-thresholding algorithm. We have also implemented the traditional filtered back projection (FBP) method based on the inverse Radon transform for comparison and applied both methods to reconstruct images of simulated mockup fuel assemblies. Higher image resolution and fewer reconstruction artifacts were obtained with the inverse-problem approach, with the mean-square-error reduced by 50%, and the structural-similarity improved by 200%. We then used a convolutional neural network (CNN) to automatically identify the bundle type and extract the pin locations from the images; the estimated activity levels finally being compared with the ground truth. The proposed computational methods accurately estimated the activity levels of the present pins, with an associated uncertainty of approximately 5%.

Background: Cardiovascular magnetic resonance (CMR) is part of the diagnostic work-up for cardiac amyloidosis (CA). Deep learning (DL) is an application of artificial intelligence that may allow to automatically analyze CMR findings and establish the likelihood of CA., Methods: 1.5 T CMR was performed in 206 subjects with suspected CA (n = 100, 49% with unexplained left ventricular (LV) hypertrophy; n = 106, 51% with blood dyscrasia and suspected light-chain amyloidosis). Patients were randomly assigned to the training (n = 134, 65%), validation (n = 30, 15%), and testing subgroups (n = 42, 20%). Short axis, 2-chamber, 4-chamber late gadolinium enhancement (LGE) images were evaluated by 3 networks (DL algorithms). The tags "amyloidosis present" or "absent" were attributed when the average probability of CA from the 3 networks was ≥ 50% or < 50%, respectively. The DL strategy was compared to a machine learning (ML) algorithm considering all manually extracted features (LV volumes, mass and function, LGE pattern, early blood-pool darkening, pericardial and pleural effusion, etc.), to reproduce exam reading by an experienced operator., Results: The DL strategy displayed good diagnostic accuracy (88%), with an area under the curve (AUC) of 0.982. The precision (positive predictive value), recall score (sensitivity), and F1 score (a measure of test accuracy) were 83%, 95%, and 89% respectively. A ML algorithm considering all CMR features had a similar diagnostic yield to DL strategy (AUC 0.952 vs. 0.982; p = 0.39)., Conclusions: A DL approach evaluating LGE acquisitions displayed a similar diagnostic performance for CA to a ML-based approach, which simulates CMR reading by experienced operators.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic management is limited by great uncertainty, for both health systems and citizens. Facing this information gap requires a paradigm shift from traditional approaches to healthcare to the participatory model of improving health. This work describes the design and function of the Doing Risk sElf-assessment and Social health Support for COVID (Dress-COV) system. It aims to establish a lasting link between the user and the tool; thus, enabling modeling of the data to assess individual risk of infection, or developing complications, to improve the individual's self-empowerment. The system uses bot technology of the Telegram application. The risk assessment includes the collection of user responses and the modeling of data by machine learning models, with increasing appropriateness based on the number of users who join the system. The main results reflect: (a) the individual's compliance with the tool; (b) the security and versatility of the architecture; (c) support and promotion of self-management of behavior to accommodate surveillance system delays; (d) the potential to support territorial health providers, e.g., the daily efforts of general practitioners (during this pandemic, as well as in their routine practices). These results are unique to Dress-COV and distinguish our system from classical surveillance applications.

This report describes a case of a young lady who, following extracardiac bypass between ascending and descending aorta for severe aortic coarctation, underwent 4-dimensional flow cardiac magnetic resonance, a technique that, by 3-dimensional flow assessment over time (4-dimensional), allows not only quantification of flows but also wall shear stress. In this case, increased wall shear stress was observed in the conduit's acute angle (kinking) as well as at the distal anastomosis level. The authors postulate that increased wall shear stress could help identify and risk stratify adult congenital heart disease who could develop vascular complications in the future. ( Level of Difficulty: Intermediate. )., (© 2019 The Authors.)

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.

We propose and test a novel approach for direct parametric image reconstruction of dynamic PET data. We present a theoretical description of the problem of PET direct parametric maps estimation as an inference problem, from a probabilistic point of view, and we derive a simple iterative algorithm, based on the Iterated Conditional Mode (ICM) framework, which exploits the simplicity of a two-step optimization and the efficiency of an analytic method for estimating kinetic parameters from a nonlinear compartmental model. The resulting method is general enough to be flexible to an arbitrary choice of the kinetic model, and unlike many other solutions, it is capable to deal with nonlinear compartmental models without the need for linearization. We tested its performance on a two-tissue compartment model, including an analytical solution to the kinetic parameters evaluation, based on an auxiliary parameter set, with the aim of reducing computation errors and approximations. The new method is tested on simulated and clinical data. Simulation analysis led to the conclusion that the proposed algorithm gives a good estimation of the kinetic parameters in any noise condition. Furthermore, the application of the proposed method to clinical data gave promising results for further studies.

Aims: Percutaneous left atrial appendage occlusion (LAAO) with the Amplatzer Cardiac Plug (ACP) emerged as a valid alternative in patients with a formal contraindication to oral anticoagulant therapy. Transoesophageal echocardiography (TEE), cardiac computed tomography angiography (CCTA), intracardiac echocardiography (ICE), and conventional cardiac angiography (CCA) are used to evaluate LAA diameters. The aim of our study was to compare pre- and intraprocedural imaging techniques in determining the correct selection of the device size, with a retrospective evaluation of the results obtained at post-procedural CCTA follow-up., Methods and Results: Between September 2009 and July 2013, 66 consecutive patients underwent to LAAO with the ACP at our institution. Preoperative LAA evaluation was realized with TEE, CCTA, ICE, and CCA. Fifty-eight (58) patients underwent to post-procedural CCTA to confirm the LAA complete exclusion, the number and extent of the residual leaks, and the positioning of the device. LAA diameters measured by CCTA correlate with the diameters obtained with CCA and ICE, but they are sized slightly larger than the others. TEE has a lower correlation with every other imaging method and a likely tendency to underestimate. The distribution of the leaks and the positioning of the device in post-procedural CCTA show no substantial differences between the devices used greater or equal to the one selected with CCTA in terms of LAA exclusion., Conclusion: The sizing of the device decided using CCTA in the phase of maximum LAA expansion reduces the risk of high-flow leaks and device malposition due to undersizing., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2015. For permissions please email: journals.permissions@oup.com.)

Unlabelled: This article explores how one can lower the injected (18)F-FDG dose while maintaining validity in comparing standardized uptake values (SUVs) between studies. Variations of the SUV within each lesion were examined at different acquisition times., Methods: Our protocol was approved by either the Human Investigation Committee or the Institutional Review Board. All 120 PET datasets were acquired continuously for 180 s per bed position in list mode and were reconstructed to obtain 30-, 60-, 90-, 120-, 150-, and 180-s-per-bed-position PET images with registration to a single set of nondiagnostic CT images. Qualitative assessment of the images was performed separately for correlation. The SUV measurements of each lesion were computed and normalized to the 180-s acquisition values to create a stabilization factor. These stabilization factors were used to demonstrate a predictable trend of stabilization over time. The variances of the stabilization factors over the entire dataset, composed of several tumor types over a range of sizes, were compared for each time point with the corresponding 150-s time point using a 2-sided F test, which has similar values to the 180-s time point., Results: The variance of the data decreased with increasing acquisition time and with increasing dose but leveled off for sufficiently long acquisitions., Conclusion: Through the statistical analysis of SUVs for increasing acquisition times and visual evaluation of the plots, we developed and hereby propose an algorithm that can be used to seek the maximum reduction in administered (18)F-FDG dose while preserving the validity of SUV comparisons., (© 2014 by the Society of Nuclear Medicine and Molecular Imaging, Inc.)

Coeliac disease is a small intestinal disorder, induced by ingestion of gluten in genetically predisposed individuals. Coeliac disease has been strongly linked to human leukocyte antigens (HLA) located on chromosome 6, with almost 100 % of coeliac disease sufferers carrying either a HLA-DQ2 or HLA-DQ8 heterodimer, with the majority carrying HLA-DQ2 encoded by the DQA1*05:01/05:05, DQB1*02:01/02:02 alleles, whereas the remaining carry the HLA-DQ8 encoded by the DQA1*03:01, DQB1*03:02 alleles. In this work, we present the development of a multiplex electrochemical genosensor array of 36 electrodes, housed within a dedicated microfluidic platform and using a total of 10 sequence-specific probes for rapid medium-high resolution HLA-DQ2/DQ8 genotyping. An evaluation of the selectivity of the designed probes was carried out with the target sequences and 44 potentially interfering alleles, including single base mismatch differentiations; good selectivity was demonstrated. The performance of the electrochemical genosensor array was validated, analyzing real human samples for the presence of HLA-DQ2/DQ8 alleles, and compared with those obtained using laboratory-based HLA typing, and an excellent correlation was obtained.