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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.

Narratives are paradigmatic examples of natural language, where nouns represent a proxy of information. Functional magnetic resonance imaging (fMRI) studies revealed the recruitment of temporal cortices during noun processing and the existence of a noun-specific network at rest. Yet, it is unclear whether, in narratives, changes in noun density influence the brain functional connectivity, so that the coupling between regions correlates with information load. We acquired fMRI activity in healthy individuals listening to a narrative with noun density changing over time and measured whole-network and node-specific degree and betweenness centrality. Network measures were correlated with information magnitude with a time-varying approach. Noun density correlated positively with the across-regions average number of connections and negatively with the average betweenness centrality, suggesting the pruning of peripheral connections as information decreased. Locally, the degree of the bilateral anterior superior temporal sulcus (aSTS) was positively associated with nouns. Importantly, aSTS connectivity cannot be explained by changes in other parts of speech (e.g., verbs) or syllable density. Our results indicate that the brain recalibrates its global connectivity as a function of the information conveyed by nouns in natural language. Also, using naturalistic stimulation and network metrics, we corroborate the role of aSTS in noun processing.

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.

Malignant pleural mesothelioma (MPM) is a cancer mainly caused by asbestos fiber inhalation, characterized by an extremely long latency and poor prognosis. Recently, researchers have focused on testing the diagnostic ability of several biomarkers. Gamma-Glutamyltransferase (GGT) has been demonstrated to be the sum of several GGT sub-fractions activity, classified based on their molecular weight in big-GGT, medium-GGT, small-GGT, and free-GGT. This work aims to evaluate whether specific GGT fractional enzymatic activity patterns could be helpful in MPM diagnosis. We analyzed blood samples from 175 workers previously exposed to asbestos, 157 non-exposed healthy subjects, and 37 MPM patients through a molecular exclusion chromatographic method. We found a specific profile of GGT fractions activity, significantly associated with MPM, resulting in an increase in b-, m- activity, along with an evident, yet not significant, decrease in f-activity. Receiver-operating characteristic (ROC) analysis showed that the best Area Under Curve (AUC) value resulted from the combined index b/f (0.679, 95% CI: 0.582–0.777). Combining the b-/f-GGT activity with the levels of serum mesothelin-related protein (SMRP; another promising MPM biomarker) improved the diagnostic accuracy, increasing the AUC value to 0.875 (95% CI: 0.807–0.943, p = <0.0001). Since MPM has a specific pattern of GGT enzymatic activity, we could hypothesize that GGT fractions play different specific biochemical roles. The improvement in the diagnostic power given by the combination of these two biomarkers confirms that the strategy of biomarkers combination might be a better approach for MPM diagnosis.

This was a prospective, multicenter study designed to evaluate the utility of MDCT in the diagnosis of coronary artery disease (CAD) in patients scheduled for elective coronary angiography (CA) using different MDCT systems from different manufacturers. Twenty national sites prospectively enrolled 367 patients between July 2004 and June 2006. Computed tomography (CT) was performed using a standardized/optimized scan protocol for each type of MDCT system (≥16 slices) and compared with quantitative CA performed within 2 weeks of MDCT. A total of 284 patients (81%) were studied by 16-slice MDCT systems, while 66 patients (19%) by 64-slice MDCT scanners. The primary analysis was on-site/off-site evaluation of the negative predictive value (NPV) on a per-patient basis. Secondary analyses included on-site evaluation on a per-artery and per-segment basis. On-site evaluation included 327 patients (CAD prevalence 58%). NPV, positive predictive value (PPV), sensitivity, specificity, and diagnostic accuracy (DA) were 0.91 (95% CI 0.85–0.95), 0.91 (95% CI 0.86–0.95), 0.94 (95% CI 0.89–0.97), 0.88 (95% CI 0.81–0.93), and 0.91 (95% CI 0.88–0.94), respectively. Off-site analysis included 295 patients (CAD prevalence 56%). NPV, PPV, sensitivity, specificity, and DA were 0.73 (95% CI 0.65–0.79), 0.93 (95% CI 0.87–0.97), 0.73 (95% CI 0.65–0.79), 0.93 (95% CI 0.87–0.97), and 0.82 (95% CI 0.77–0.86), respectively. The results of this study demonstrate the utility of MDCT in excluding significant CAD even when conducted by centers with varying degrees of expertise and using different MDCT machines.

Feasibility assessment and planning of thoracic endovascular aortic repair (TEVAR) require computed tomography (CT)-based analysis of geometric aortic features to identify adequate landing zones (LZs) for endograft deployment. However, no consensus exists on how to take the necessary measurements from CT image data. We trained and applied a fully automated pipeline embedding a convolutional neural network (CNN), which feeds on 3D CT images to automatically segment the thoracic aorta, detects proximal landing zones (PLZs), and quantifies geometric features that are relevant for TEVAR planning. For 465 CT scans, the thoracic aorta and pulmonary arteries were manually segmented; 395 randomly selected scans with the corresponding ground truth segmentations were used to train a CNN with a 3D U-Net architecture. The remaining 70 scans were used for testing. The trained CNN was embedded within computational geometry processing pipeline which provides aortic metrics of interest for TEVAR planning. The resulting metrics included aortic arch centerline radius of curvature, proximal landing zones (PLZs) maximum diameters, angulation, and tortuosity. These parameters were statistically analyzed to compare standard arches vs. arches with a common origin of the innominate and left carotid artery (CILCA). The trained CNN yielded a mean Dice score of 0.95 and was able to generalize to 9 pathological cases of thoracic aortic aneurysm, providing accurate segmentations. CILCA arches were characterized by significantly greater angulation (p = 0.015) and tortuosity (p = 0.048) in PLZ 3 vs. standard arches. For both arch configurations, comparisons among PLZs revealed statistically significant differences in maximum zone diameters (p < 0.0001), angulation (p < 0.0001), and tortuosity (p < 0.0001). Our tool allows clinicians to obtain objective and repeatable PLZs mapping, and a range of automatically derived complex aortic metrics.

Objectives: It was the aim of this study to assess the pathophysiological, prognostic role of aortic regurgitation (AR) in the ‘mixed pictures' of degenerative aortic valve stenoinsufficiency (ASI) by a multimarker clinical approach. Methods: We enrolled 112 consecutive surgical patients: 19 with pure valve stenosis (PAS), 39 with mild regurgitation, 29 with severe regurgitation, and 25 controls with annulo-ectatic AR. All underwent complete echocardiography, carotid ultrasound and aortic/coronary multislice computed tomography calcium score evaluation. We determined tissue semiquantitative osteopontin, metalloproteinases (MMPs), tissue inhibitors of MMPs (TIMPs) and circulating brain natriuretic peptide. We evaluated major adverse cardiac events and cardiovascular early, long-term mortality after bioprosthetic valve implantation. Results: Tissue calcification, carotid and coronary atherosclerotic disease were prevalent in PAS versus ASI and AR patients. The multislice computed tomography calcium score (Agatston) was comparable between PAS and ASI (PAS 3,507.3 + 2,442.6; mild AR 4,270.7 + 2,213.5; severe AR 3,568.5 + 1,823.4), but much lower in AR (1,247.8 + 2,708.6). In ASI, a plasma/tissue ‘profibrotic' MMP/TIMP balance prevailed, with circulating and echocardiographic indices of myocardial dysfunction. Percentages of major adverse cardiac events and early, long-term mortality were higher in ASI. Conclusions: In ASI, different, still unknown, genetic and dysplastic factors could work synergically with cardiovascular risk factors, determining a much more adverse myocardial and valve remodeling, resulting in worse clinical outcome.

Background/Objectives: This study compares the power of the radiofrequency (RF) signal reflected from the media layer (media power) of the common carotid artery (CCA) and the CCA stiffness between individuals with and without type 2 diabetes mellitus (T2DM). It also evaluates the associations of CCA media power with plasma glucose and lipid levels, as well as carotid stiffness. Methods: A total of 540 individuals, 115 with and 425 without T2DM (273 males, mean age = 64 ± 8 years) were studied using RF-based tracking of the right CCA. The following parameters were measured: CCA media thickness, luminal diameter, wall tensile stress (WTS), local pulse wave velocity (PWV), and media power. Results: Compared to the non-diabetic individuals, the T2DM patients had significantly higher CCA media thickness (652 ± 122 vs. 721 ± 138 microns, p < 0.005), luminal diameter (6.12 ± 0.78 vs. 6.86 ± 0.96 mm, p < 0.0005), media power (36.1 ± 4.8 vs. 39.3 ± 4.6, p < 0.0001), and PWV (7.65 ± 1.32 vs. 8.40 ± 1.89 m/s; p < 0.01), but comparable WTS (32.7 ± 10.4 vs. 33.1 ± 10.7 kPa; p = 0.25). In the entire population, CCA media power was independently associated with male sex, pulse pressure, current smoking, and T2DM; when T2DM was not included in the model, triglycerides emerged as an independent determinant of media power. The CCA PWV was independently associated with age, pulse pressure, media power, and T2DM. Conclusions: Our findings suggest the presence of structural changes in the arterial media of T2DM patients, leading to carotid stiffening and remodeling, aiming to preserve WTS. T2DM-related changes in arterial wall composition may be driven by high plasma triglyceride levels, which have previously been associated with both arterial stiffening and the incidence of CV events.

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.