Labeling galectin-3 for the assessment of myocardial infarction in rats

Background: Galectin-3 is a beta-galactoside-binding lectin expressed in most of tissues in normal conditions and overexpressed in myocardium from early stages of heart failure (HF). It is an established biomarker associated with extracellular matrix (ECM) turnover during myocardial remodeling. The aim of this study is to test the ability of I-123-galectin-3 (IG3) to assess cardiac remodeling in a model of myocardial infarction (MI) using imaging techniques. Methods: Recombinant galectin-3 was labeled with iodine-123 and in vitro binding assays were conducted to test I-123-galectin-3 ability to bind to ECM targets. For in vivo studies, a rat model of induced-MI was used. Animals were subjected to magnetic resonance and micro-SPETC/micro-CT imaging two (2 W-MI) or four (4 W-MI) weeks after MI. Sham rats were used as controls. Pharmacokinetic, biodistribution, and histological studies were also performed after intravenous administration of IG3. Results: In vitro studies revealed that IG3 shows higher binding affinity (measured as counts per minute, cpm) (p < 0.05) to laminin (2.45 +/- 1.67 cpm), fibronectin (4.72 +/- 1.95 cpm), and collagen type I (1.88 +/- 0.53 cpm) compared to bovine serum albumin (BSA) (0.88 +/- 0.31 cpm). Myocardial quantitative IG3 uptake (\%ID/g) was higher (p < 0.01) in the infarct of 2 W-MI rats (0.15 +/- 0.04\%) compared to control (0.05 +/- 0.03\%). IG3 infarct uptake correlates with the extent of scar (r(s) = 1, p = 0.017). Total collagen deposition in the infarct (percentage area) was higher (p < 0.0001) at 2 W-MI (24.2 +/- 5.1\%) and 4 W-MI (30.4 +/- 7.5\%) compared to control (1.9 +/- 1.1\%). However, thick collagen content in the infarct (square micrometer stained) was higher at 4 W-MI (20.5 +/- 11.2 mu m(2)) compared to control (4.7 +/- 2.0 mu m(2), p < 0.001) and 2 W-MI (10.6 +/- 5.1 mu m(2), p < 0.05). Conclusions: This study shows, although preliminary, enough data to consider IG3 as a potential contrast agent for imaging of myocardial interstitial changes in rats after MI. Labeling strategies need to be sought to improve in vivo IG3 imaging, and if proven, galectin-3 might be used as an imaging tool for the assessment and treatment of MI patients.This work was supported by Centro Nacional de Investigaciones Cardiovasculares (CNIC) through the Cardio-Image program (TA and CPM).S

Estimated Worldwide Mortality Attributed to Secondhand Tobacco Smoke Exposure, 1990-2016

Importance: The World Health Organization estimates that the 1 billion individuals who smoke worldwide contribute to the 880 000 secondhand smoke (SHS)-related deaths among individuals who do not smoke each year. A better understanding of the scale of harm of SHS to those who do not smoke could increase awareness of the consequences of smoking and help to design measures to protect individuals who do not smoke, especially children. Objective: To calculate the number of individuals who smoke associated with the death of 1 individual who died of SHS exposure both on a global scale and in various World Bank regions. Design, Setting, and Participants: In this cross-sectional epidemiologic assessment, data from Our World in Data were used to tabulate the number of individuals who smoke in each country and number of premature deaths related to SHS in that country from 1990 to 2016. The mean number of cigarettes consumed in all countries was also included in analyses. Data were collected for the following World Bank regions: North America, Latin America and the Caribbean, Europe and Central Asia, the Middle East and North Africa, sub-Saharan Africa, South Asia, and East Asia and the Pacific from 1990 and 2016. Statistical analysis was conducted in July 2019. Exposure: Secondhand smoke. Main Outcomes and Measures: The pack-year index, calculated as the number of pack-years associated with the death of 1 individual who does not smoke but was exposed to SHS, and the SHS index, calculated as the number of individuals who smoked for 24 years (ie, the mean duration of smoking) associated with the death of 1 individual who does not smoke. Results: Globally, the SHS index changed favorably, from 31.3 (95% CI, 30.6-32.0) individuals who smoked associated with the death of 1 individual who did not smoke in 1990 to 52.3 (95% CI, 51.2-53.5) individuals who smoked in 2016. There was a wide regional variation in the 2016 secondhand smoke index, from 42.6 (95% CI, 41.6-43.5) individuals who smoked in the Middle East and North Africa to 85.7 (95% CI, 83.8-87.7) individuals who smoked in North America. Worldwide, the pack-year index also changed favorably from 751.9 (95% CI, 736.3-770.7) pack-years associated with 1 death in 1990 to 1255.9 (95% CI, 1227.2-1284.4) pack-years in 2016. Conclu

Automatic segmentation of multiple cardiovascular structures from cardiac computed tomography angiography images using deep learning.

OBJECTIVES:To develop, demonstrate and evaluate an automated deep learning method for multiple cardiovascular structure segmentation. BACKGROUND:Segmentation of cardiovascular images is resource-intensive. We design an automated deep learning method for the segmentation of multiple structures from Coronary Computed Tomography Angiography (CCTA) images. METHODS:Images from a multicenter registry of patients that underwent clinically-indicated CCTA were used. The proximal ascending and descending aorta (PAA, DA), superior and inferior vena cavae (SVC, IVC), pulmonary artery (PA), coronary sinus (CS), right ventricular wall (RVW) and left atrial wall (LAW) were annotated as ground truth. The U-net-derived deep learning model was trained, validated and tested in a 70:20:10 split. RESULTS:The dataset comprised 206 patients, with 5.130 billion pixels. Mean age was 59.9 ± 9.4 yrs., and was 42.7% female. An overall median Dice score of 0.820 (0.782, 0.843) was achieved. Median Dice scores for PAA, DA, SVC, IVC, PA, CS, RVW and LAW were 0.969 (0.979, 0.988), 0.953 (0.955, 0.983), 0.937 (0.934, 0.965), 0.903 (0.897, 0.948), 0.775 (0.724, 0.925), 0.720 (0.642, 0.809), 0.685 (0.631, 0.761) and 0.625 (0.596, 0.749) respectively. Apart from the CS, there were no significant differences in performance between sexes or age groups. CONCLUSIONS:An automated deep learning model demonstrated segmentation of multiple cardiovascular structures from CCTA images with reasonable overall accuracy when evaluated on a pixel level

Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.

Background Rapid coronary plaque progression (RPP) is associated with incident cardiovascular events. To date, no method exists for the identification of individuals at risk of RPP at a single point in time. This study integrated coronary computed tomography angiography-determined qualitative and quantitative plaque features within a machine learning (ML) framework to determine its performance for predicting RPP. Methods and Results Qualitative and quantitative coronary computed tomography angiography plaque characterization was performed in 1083 patients who underwent serial coronary computed tomography angiography from the PARADIGM (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging) registry. RPP was defined as an annual progression of percentage atheroma volume ≥1.0%. We employed the following ML models: model 1, clinical variables; model 2, model 1 plus qualitative plaque features; model 3, model 2 plus quantitative plaque features. ML models were compared with the atherosclerotic cardiovascular disease risk score, Duke coronary artery disease score, and a logistic regression statistical model. 224 patients (21%) were identified as RPP. Feature selection in ML identifies that quantitative computed tomography variables were higher-ranking features, followed by qualitative computed tomography variables and clinical/laboratory variables. ML model 3 exhibited the highest discriminatory performance to identify individuals who would experience RPP when compared with atherosclerotic cardiovascular disease risk score, the other ML models, and the statistical model (area under the receiver operating characteristic curve in ML model 3, 0.83 [95% CI 0.78-0.89], versus atherosclerotic cardiovascular disease risk score, 0.60 [0.52-0.67]; Duke coronary artery disease score, 0.74 [0.68-0.79]; ML model 1, 0.62 [0.55-0.69]; ML model 2, 0.73 [0.67-0.80]; all P&lt;0.001; statistical model, 0.81 [0.75-0.87], P=0.128). Conclusions Based on a ML framework, quantitative atherosclerosis characterization has been shown to be the most important feature when compared with clinical, laboratory, and qualitative measures in identifying patients at risk of RPP

The Heart of the World

Cardiovascular diseases (CVDs) are the leading cause of mortality globally. Of the 20.5 million CVD-related deaths in 2021, approximately 80% occurred in low- and middle-income countries. Using data from the Global Burden of Disease Study, NCD Risk Factor Collaboration, NCD Countdown initiative, WHO Global Health Observatory, and WHO Global Health Expenditure database, we present the burden of CVDs, associated risk factors, their association with national health expenditures, and an index of critical policy implementation. The Central Europe, Eastern Europe, and Central Asia region face the highest levels of CVD mortality globally. Although CVD mortality levels are generally lower in women than men, this is not true in almost 30% of countries in the North Africa and Middle East and Sub-Saharan regions. Raised blood pressure remains the leading global CVD risk factor, contributing to 10.8 million deaths in 2019. The regions with the highest proportion of countries achieving the maximum score for the WHF Policy Index were South Asia, Central Europe, Eastern Europe, and Central Asia, and the High-Income regions. The Sub-Saharan Africa region had the highest proportion of countries scoring two or less. Policymakers must assess their country’s risk factor profile to craft effective strategies for CVD prevention and management. Fundamental strategies such as the implementation of National Tobacco Control Programmes, ensuring the availability of CVD medications, and establishing specialised units within health ministries to tackle non-communicable diseases should be embraced in all countries. Adequate healthcare system funding is equally vital, ensuring reasonable access to care for all communities