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Context: Type 2 diabetes (DM2) is an emerging disease in the pediatric population. DM2 is associated with metabolic-associated fatty liver disease (MAFLD). High-density lipoproteins (HDLs) are lipoproteins that are believed to have atheroprotective properties that reduce the risk of cardiovascular disease (CVD). Current evidence suggests that the physicochemical and functional features of HDLs may play a key role in the pathogenesis of atherosclerosis., Objective: We aimed to assess the impact of MAFLD on cholesterol efflux capacity (CEC) in adolescents with DM2., Design: A cross-sectional study., Setting: Attention clinic for Children with Diabetes of the Hospital Infantil de México Federico Gómez., Patients or Other Participants: This study included a total of 70 adolescents, 47 of which had DM2 and 23 were healthy individuals., Interventions: The presence of MAFLD was determined by MR spectroscopy with proton density fat fraction. We compared the distribution of HDL subtypes (HDL2b, HDL2a, HDL3a, HDL3b, and HDL3c) and the chemical composition of HDLs (total protein, triglycerides, phospholipids, cholesteryl esters, and free cholesterol). HDL functionality was determined by the CEC, measuring the fluorescent cholesterol efflux from J774 macrophage cells., Main Outcome Measures: We were expecting to observe a decrease in HDL efflux capacity in adolescents with type 2 diabetes and MAFLD., Results: In our study, we observed a prevalence of MAFLD in 66% of adolescents with DM2, similar to that reported in other international studies (60%-80%). In the population with DM2 and MAFLD, we did not observe a decrease in CEC. Initially we found a slight elevation of CEC in adolescents with DM2, however, with the increase in liver fat, a little decrease is observed, which could explain a probable metabolic phenomenon, since the physicochemical composition and distribution of the particles is associated with the percentage of liver fat. A positive correlation between the percentage of liver fat and the concentration of HDL2b ( p  = 0.011), HDL2a ( p  = 0.014) and average particle size ( p  = 0.011) and the proportion of triglycerides inside the particles ( p  = 0.007). Likewise, negative correlation were found with the percentage of liver fat, cholesterol esters ( p  = 0.010) and free cholesterol of the particles ( p  < 0.001). We observed a positive correlation between CEC and the percentage of triglycerides ( p  = 0.007), and a negative correlation with the percentage of cholesterol esters ( p  = 0.05) inside the HDL's particles., Conclusions: In this group of adolescents with DM2, the presence of MAFLD was not associated with CEC; however, it is associated with abnormalities in the distribution and lipid composition of HDL particles. The momentum generated by the original proposal for MAFLD in the adult population and following the recommendations for pediatric MAFLD will be a step forward in helping to study the impact of MAFLD on the atheroprotective properties of HDL in the pediatric population., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Orozco Morales, Medina Urrutia, Tamayo, Reyes Barrera, Galarza, Juárez Rojas, Dies Suarez, Méndez Sánchez, Díaz Orozco, Velázquez-López and Medina Bravo.)

The orbit is an important structure within the skull that houses the eye, optic nerve, and extraocular muscles. It also contains adipose/fat tissue, which provides a protective cushion for these components. Inflammatory orbital disease can affect any or all components of the orbit, often arising from various underlying pathologic conditions, including autoimmune, infectious, and vascular diseases. Typical signs and symptoms of orbital inflammation include swelling, redness, pain, discomfort, and potential loss of function. The role of orbital fat in the pathogenesis of inflammatory orbital diseases has not been fully explored. This review aims to provide a comprehensive description of orbital fat, its relevance and the potential role in inflammatory diseases of the orbit, and the use of radiologic imaging studies for evaluating this fat depot in cases of as inflammatory orbital diseases. Additionally, this review discusses the various procedures available for the treatment and management of these conditions. A range of interventions, including pharmacotherapy and surgical procedures, will be evaluated as promising therapeutic options. This review also explores the characteristics and potential applications of orbital fat-derived stem cells, with an emphasis on their regenerative abilities and anti-inflammatory effects. Understanding the role of orbital fat and its contribution to inflammatory orbital diseases is essential for optimizing diagnostic and treatment strategies., Competing Interests: Declarations. Conflict of Interest: The authors (Prince Dadson, Peter Ngum, Luis Eduardo Juarez-Orozco, Michael Ntodie, Piotr Loba) declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethical Approval: This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors., (© 2024. The Author(s).)

Background: We developed an explainable deep-learning (DL)-based classifier to identify flow-limiting coronary artery disease (CAD) by O-15 H 2 O perfusion positron emission tomography computed tomography (PET/CT) and coronary CT angiography (CTA) imaging. The classifier uses polar map images with numerical data and visualizes data findings., Methods: A DLmodel was implemented and evaluated on 138 individuals, consisting of a combined image-and data-based classifier considering 35 clinical, CTA, and PET variables. Data from invasive coronary angiography were used as reference. Performance was evaluated with clinical classification using accuracy (ACC), area under the receiver operating characteristic curve (AUC), F1 score (F1S), sensitivity (SEN), specificity (SPE), precision (PRE), net benefit, and Cohen's Kappa. Statistical testing was conducted using McNemar's test., Results: The DL model had a median ACC = 0.8478, AUC = 0.8481, F1S = 0.8293, SEN = 0.8500, SPE = 0.8846, and PRE = 0.8500. Improved detection of true-positive and false-negative cases, increased net benefit in thresholds up to 34%, and comparable Cohen's kappa was seen, reaching similar performance to clinical reading. Statistical testing revealed no significant differences between DL model and clinical reading., Conclusions: The combined DL model is a feasible and an effective method in detection of CAD, allowing to highlight important data findings individually in interpretable manner., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objectives: To evaluate cost-effective pharmacological treatment in adult kidney transplant recipients from the perspective of the Colombian health system., Methods: A decision tree model for the induction phase and a Markov model for the maintenance phase were built. A review of the clinical literature was conducted to extract probabilities, and the life-years were used as the outcome. Costs were calculated using the administrative databases. The evaluating treatment schemes are organized by groups of evidence with direct comparisons., Results: In the induction phase, anti-thymocyte immunoglobulin+ methylprednisolone is dominant, more effective, and less expensive, compared with basiliximab+methylprednisolone. In the maintenance phase, azathioprine (AZA) is dominant in contrast to mycophenolate mofetil (MFM) both with cyclosporine (CIC)+ corticosteroids (CE); CIC is dominant relative to sirolimus (SIR) and tacrolimus (TAC) (both with MFM+CE or AZA+CE), and TAC is dominant compared with SIR (in addition with MFM+CE or mycophenolate sodium [MFS]+CE); MFM is dominant in relation to MFS and everolimus, and SIR is more effective MFM but it does not exceed the threshold (in sum with TAC+CE); MFS and MFM are dominant relative to everolimus, and SIR is more effective than MFM, but it does not exceed the threshold (in addiction with CIC+CE); MFM is dominant in relation to TAC (in sum with SIR+CE), and CIC+AZA+CE is dominant in relation to TAC+MFM+CE., Conclusions: The base-case results for all evidence groups are consistent with the different sensitivity analyses., Competing Interests: Author Disclosures Author disclosure forms can be accessed below in the Supplemental Material section., (Copyright © 2024 International Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)

Objectives: Social preference values of health states are a fundamental input for the preparation of studies in health economics. Several countries have undertaken studies to obtain these values. Our objective was to conduct a structured and systematic literature review of articles that calculates this set of representative values at the national level in low- and middle-income countries (LMICs)., Methods: In this systematic review, we searched the Embase, MEDLINE, Ovid, SciELO, and LILACS databases, among others, for studies published up to June 2022 that estimated nationally representative health states preferences values for LMICs. We summarized the information qualitatively and assessed the risk of bias in each article using the consensus-based standards for selecting health measurement instruments checklist tool., Results: Of the 23 663 articles identified, 35 studies were eligible for inclusion. The studies were from 19 countries in Latin-American, Europe, Africa, and Asia. No studies were found for low-income countries. The most commonly applied generic instrument for measuring health-related quality of life was the 5-level version of EQ-5D and 3-level version of EQ-5D. Preference was given to face-to-face administration of these instruments. The sociodemographic variables with the most significant negative correlation versus utility were older adults, marital status (widowed or divorced), and low educational level and income., Conclusions: Worldwide, there have been few studies that have estimated, in a nationally representative manner, the social values of health states preferences in LMICs. We consider the local estimate of this set of societal values relevant for any society to improve decision making in allocating resources in health budgets., (Copyright © 2023 International Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)

Background: Machine Learning (ML) allows integration of the numerous variables delivered by cardiac PET/CT, while traditional survival analysis can provide explainable prognostic estimates from a restricted number of input variables. We implemented a hybrid ML-and-survival analysis of multimodal PET/CT data to identify patients who developed myocardial infarction (MI) or death in long-term follow up., Methods: Data from 739 intermediate risk patients who underwent coronary CT and selectively stress 15 O-water-PET perfusion were analyzed for the occurrence of MI and all-cause mortality. Images were evaluated segmentally for atherosclerosis and absolute myocardial perfusion through 75 variables that were integrated through ML into an ML-CCTA and an ML-PET score. These scores were then modeled along with clinical variables through Cox regression. This hybridized model was compared against an expert interpretation-based and a calcium score-based model., Results: Compared with expert- and calcium score-based models, the hybridized ML-survival model showed the highest performance (CI .81 vs .71 and .64). The strongest predictor for outcomes was the ML-CCTA score., Conclusion: Prognostic modeling of PET/CT data for the long-term occurrence of adverse events may be improved through ML imaging score integration and subsequent traditional survival analysis with clinical variables. This hybridization of methods offers an alternative to traditional survival modeling of conventional expert image scoring and interpretation., (© 2023. The Author(s).)

Gallstone disease and metabolic dysfunction-associated fatty liver disease (MAFLD) share numerous common risk factors and progression determinants in that they both manifest as organ-specific consequences of metabolic dysfunction. Nevertheless, the precise molecular mechanisms underlying fibrosis development in cholecystectomized MAFLD patients remain inadequately defined. This study aimed to investigate the involvement of farnesoid X receptor 1 (FXR1) and fibroblast growth factor receptor 4 (FGFR4) in the progression of fibrosis in cholecystectomized MAFLD patients. A meticulously characterized cohort of 12 patients diagnosed with MAFLD, who had undergone liver biopsies during programmed cholecystectomies, participated in this study. All enrolled patients underwent a follow-up regimen at 1, 3, and 6 months post-cholecystectomy, during which metabolic biochemical markers were assessed, along with elastography, which served as indirect indicators of fibrosis. Additionally, the hepatic expression levels of FGFR4 and FXR1 were quantified using quantitative polymerase chain reaction (qPCR). Our findings revealed a robust correlation between hepatic FGFR4 expression and various histological features, including the steatosis degree (r = 0.779, p = 0.023), ballooning degeneration (r = 0.764, p = 0.027), interphase inflammation (r = 0.756, p = 0.030), and steatosis activity score (SAS) (r = 0.779, p = 0.023). Conversely, hepatic FXR1 expression did not exhibit any significant correlations with these histological features. In conclusion, our study highlights a substantial correlation between FGFR4 expression and histological liver damage, emphasizing its potential role in lipid and glucose metabolism. These findings suggest that FGFR4 may play a crucial role in the progression of fibrosis in cholecystectomized MAFLD patients. Further research is warranted to elucidate the exact mechanisms through which FGFR4 influences metabolic dysfunction and fibrosis in this patient population.

Three different cuts of meat samples: inside skirt, knuckles, and sirloin were picture captioned on the first and fifth day after purchase. From each type of meat cut, ten pictures were taken at the beginning and the end of the studied shelf life, obtaining 60 different images. The images were taken under control variables in a black acrylic cabin. In addition to the original images, we proportionate another set of 60 processed images. The latter were obtained after color calibration and meat segmentation. All these images could be used for future experiments where the color in meat should be analyzed., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. However, it is essential to mention that the cabin described in the methodology has started a registration process as a utility model., (© 2023 The Authors.)

(1) Background: The CT-based attenuation correction of SPECT images is essential for obtaining accurate quantitative images in cardiovascular imaging. However, there are still many SPECT cameras without associated CT scanners throughout the world, especially in developing countries. Performing additional CT scans implies troublesome planning logistics and larger radiation doses for patients, making it a suboptimal solution. Deep learning (DL) offers a revolutionary way to generate complementary images for individual patients at a large scale. Hence, we aimed to generate linear attenuation coefficient maps from SPECT emission images reconstructed without attenuation correction using deep learning. (2) Methods: A total of 384 SPECT myocardial perfusion studies that used 99m Tc-sestamibi were included. A DL model based on a 2D U-Net architecture was trained using information from 312 patients. The quality of the generated synthetic attenuation correction maps (ACMs) and reconstructed emission values were evaluated using three metrics and compared to standard-of-care data using Bland-Altman plots. Finally, a quantitative evaluation of myocardial uptake was performed, followed by a semi-quantitative evaluation of myocardial perfusion. (3) Results: In a test set of 66 test patients, the ACM quality metrics were MSSIM = 0.97 ± 0.001 and NMAE = 3.08 ± 1.26 (%), and the reconstructed emission quality metrics were MSSIM = 0.99 ± 0.003 and NMAE = 0.23 ± 0.13 (%). The 95% limits of agreement (LoAs) at the voxel level for reconstructed SPECT images were: [-9.04; 9.00]%, and for the segment level, they were [-11; 10]%. The 95% LoAs for the Summed Stress Score values between the images reconstructed were [-2.8, 3.0]. When global perfusion scores were assessed, only 2 out of 66 patients showed changes in perfusion categories. (4) Conclusion: Deep learning can generate accurate attenuation correction maps from non-attenuation-corrected cardiac SPECT images. These high-quality attenuation maps are suitable for attenuation correction in myocardial perfusion SPECT imaging and could obviate the need for additional imaging in standalone SPECT scanners.

Glycoprotein IIb/IIIa inhibitors are an adjuvant therapy for the treatment of patients with acute coronary syndromes. The main adverse reactions are bleeding and thrombocytopenia in 1-2% of cases. A 66-year-old woman arrived at the emergency department with ST-elevation MI. The catheterisation lab was busy, so she received thrombolytic therapy. Coronary angiography revealed a 90% stenosis in the middle segment of the left anterior descending artery and Thrombolysis in MI 2 flow. Subsequent percutaneous coronary intervention showed abundant thrombus and a coronary dissection and it was necessary to insert five drug-eluting stents. Non-fractionated heparin and a tirofiban infusion were used. After the percutaneous coronary intervention, she developed severe thrombocytopenia, haematuria and gingivorrhagia, for which infusion of tirofiban was suspended. In follow-up, no major bleeding or subsequent haemorrhagic complications were identified. It is crucial to distinguish between heparin-induced thrombocytopenia and thrombocytopenia caused by other drugs. A high level of suspicion should be employed in these cases., Competing Interests: Disclosure: The authors have no conflicts of interest to declare. Consent: Written informed consent was obtained from all individuals included in the study. Ethics: This document was reviewed by the local bioethics committee and was approved., (Copyright © 2023, Radcliffe Cardiology.)

Background: Advanced cardiac imaging with positron emission tomography (PET) is a powerful tool for the evaluation of known or suspected cardiovascular disease. Deep learning (DL) offers the possibility to abstract highly complex patterns to optimize classification and prediction tasks., Methods and Results: We utilized DL models with a multi-task learning approach to identify an impaired myocardial flow reserve (MFR <2.0 ml/g/min) as well as to classify cardiovascular risk traits (factors), namely sex, diabetes, arterial hypertension, dyslipidemia and smoking at the individual-patient level from PET myocardial perfusion polar maps using transfer learning. Performance was assessed on a hold-out test set through the area under receiver operating curve (AUC). DL achieved the highest AUC of 0.94 [0.87-0.98] in classifying an impaired MFR in reserve perfusion polar maps. Fine-tuned DL for the classification of cardiovascular risk factors yielded the highest performance in the identification of sex from stress polar maps (AUC = 0.81 [0.73, 0.88]). Identification of smoking achieved an AUC = 0.71 [0.58, 0.85] from the analysis of rest polar maps. The identification of dyslipidemia and arterial hypertension showed poor performance and was not statistically significant., Conclusion: Multi-task DL for the evaluation of quantitative PET myocardial perfusion polar maps is able to identify an impaired MFR as well as cardiovascular risk traits such as sex, smoking and possibly diabetes at the individual-patient level., (© 2022. The Author(s).)

BACKGROUND Metabolic dysfunction-associated fatty liver disease (MAFLD) is now the term used for hepatic steatosis in patients who are overweight or obese, have type 2 diabetes mellitus (T2DM), or evidence of metabolic dysregulation. The prevalence of MAFLD among morbidly obese subjects is 65-93%. Hepatic dendritic cells (hDCs) are antigen-presenting cells that induce T cell-mediated immunity. MAFLD pathogenesis involves numerous immune cell-mediated inflammatory processes, while the particular role of hDCs is yet to be well defined. This study aimed to identify hDCs in liver biopsies from 128 patients with MAFLD associated with obesity. MATERIAL AND METHODS In this cross-sectional study, 128 liver biopsies from 128 patients with MAFLD (diagnosed as presence of hepatic steatosis, plus T2DM, metabolic dysregulation or overweight/obesity) were collected and assessed for CD11c⁺ immunoreactivity degree (CD11c as dendritic cell biomarker), through antigen retrieval, reaction with CD11c antibodies (primary), and marking with diaminobenzidine chromogen. RESULTS Among the 128 patients with MAFLD, 64 (50%) had MAFLD and fibrosis and 72 (56.2%) positively expressed hDCs (CD11c⁺). Among morbidly obese patients, 49 (64.5%) positively expressed hDCs (CD11c⁺) in liver tissue; from patients with obesity grade I- grade II (GI-II), 18 (54.5%) positively expressed hDCs (CD11c⁺) in liver tissue; and from non-obese patients with MAFLD, 5 (26.3%) positively expressed hDCs (CD11c⁺) in liver tissue. CONCLUSIONS hDC expression increases significantly in morbidly obese patients with MAFLD compared with non-obese patients, independent of the degree of fibrosis, suggesting the role of adaptive changes within hDCs in the perpetuation of inflammatory insults in chronic liver diseases.

Purpose: To estimate the interobserver agreement of the Carimas software package (SP) on global, regional, and segmental levels for the most widely used myocardial perfusion PET tracer-Rb-82., Materials and Methods: Rest and stress Rb-82 PET scans of 48 patients with suspected or known coronary artery disease (CAD) were analyzed in four centers using the Carimas SP. We considered values to agree if they simultaneously had an intraclass correlation coefficient (ICC) > 0.75 and a difference < 20% of the median across all observers., Results: The median values on the segmental level were 1.08 mL/min/g for rest myocardial blood flow (MBF), 2.24 mL/min/g for stress MBF, and 2.17 for myocardial flow reserve (MFR). For the rest MBF and MFR, all the values at all the levels fulfilled were in excellent agreement. For stress MBF, at the global and regional levels, all the 24 comparisons showed excellent agreement. Only 1 out of 102 segmental comparisons (seg. 14) was over the adequate agreement limit-23.5% of the median value (ICC = 0.95)., Conclusion: Interobserver agreement for Rb-82 PET myocardial perfusion quantification analyzed with Carimas is good at any LV segmentation level-global, regional, and segmental. It is good for all the estimates-rest MBF, stress MBF, and MFR., (© 2022. The Author(s).)

Purpose of Review: As machine learning-based artificial intelligence (AI) continues to revolutionize the way in which we analyze data, the field of nuclear cardiology provides fertile ground for the implementation of these complex analytics. This review summarizes and discusses the principles regarding nuclear cardiology techniques and AI, and the current evidence regarding its performance and contribution to the improvement of risk prediction in cardiovascular disease. There is a growing body of evidence on the experimentation with and implementation of machine learning-based AI on nuclear cardiology studies both concerning SPECT and PET technology for the improvement of risk-of-disease (classification of disease) and risk-of-events (prediction of adverse events) estimations. These publications still report objective divergence in methods either utilizing statistical machine learning approaches or deep learning with varying architectures, dataset sizes, and performance. Recent efforts have been placed into bringing standardization and quality to the experimentation and application of machine learning-based AI in cardiovascular imaging to generate standards in data harmonization and analysis through AI. Machine learning-based AI offers the possibility to improve risk evaluation in cardiovascular disease through its implementation on cardiac nuclear studies. AI in improving risk evaluation in nuclear cardiology. * Based on the 2019 ESC guidelines., (© 2022. The Author(s).)

Background: Alterations in the anatomic and biomechanical properties of the ascending aorta (AAo) can give rise to various vascular pathologies. The aim of the current study is to gain additional insights in the biology of the AAo size and function., Methods: We developed an AI based analysis pipeline for the segmentation of the AAo, and the extraction of AAO parameters. We then performed genome-wide association studies of AAo maximum area, AAo minimum area and AAo distensibility in up to 37,910 individuals from the UK Biobank. Variants that were significantly associated with AAo phenotypes were used as instrumental variables in Mendelian randomization analyses to investigate potential causal relationships with coronary artery disease, myocardial infarction, stroke and aneurysms., Findings: Genome-wide association studies revealed a total of 107 SNPs in 78 loci. We annotated 101 candidate genes involved in various biological processes, including connective tissue development (THSD4 and COL6A3). Mendelian randomization analyses showed a causal association with aneurysm development, but not with other vascular diseases., Interpretation: We identified 78 loci that provide insights into mechanisms underlying AAo size and function in the general population and provide genetic evidence for their role in aortic aneurysm development., Competing Interests: Declaration of interests Niek Verweij was employed by Regeneron Pharmaceuticals. Jan Walter Benjamins, Ming Wai Yeung, Yordi J. van de Vegte, M. Abdullah Said, Thijs van der Linden, Daan Ties, Luis E. Juarez-Orozco and Pim van der Harst declare that they have no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Nutraceuticals are defined as products isolated or purified from foods that are generally sold in medicinal or dosage forms not usually associated with food which is demonstrated to have a physiological benefit or provide protection against chronic disease. In this context, the products offered should be rigorously evaluated by international regulatory agencies. More recently, nutraceuticals have been proposed as a potential preventive and therapeutic option in the assessment of chronic diseases, mainly by altering the microbiome composition. However, the current lack of conclusive evidence supporting the "healthy" or "normal" microbiome, along with the dysbiosis concept paradigm, could be both contributing to the lack of homogeneous results. These issues may be solved in the next years with the use of emergent technologies in the individual's microbiome assessment and its fluctuations in time or related to many factors, such as nutraceuticals. Additionally, future research assessing the independent association between the dysbiosis modification and any "potential" nutraceutical product (including bioactive ingredient or chemical compound in food) is going to enlarge the currently reduced "established nutraceuticals" group. In this work we have assessed the nutraceutical's potential role as a microbiome-targeted manipulation therapy, and the gut-liver axis involved in the digestive diseases' pathogenesis and progression, including the chronic liver diseases. Moreover, microbiome targeted nutraceuticals that show consistent results might be further included in clinical research and trials in the therapeutic assessment of chronic diseases. Finally, the indication of these quality microbiome-targeted nutraceuticals will undoubtedly carry health benefits for individuals.

Non-alcoholic fatty liver disease (NAFLD) affects almost a quarter of the world's population and is the most common cause of chronic liver disease in children and adolescents. The recent proposal to replace the terminology of NAFLD with metabolic-associated fatty liver disease (MAFLD) aims to reflect the pathophysiology and risk factors for this disease. Importantly, the risk factors for MAFLD may be prenatal, such as genetic factors, or postnatal, such as obesity and insulin resistance. MAFLD is increasingly recognized in children and adolescents. Early diagnosis and identification of high-risk individuals with type 2 diabetes mellitus and metabolic syndrome is important. The diagnosis and management of MAFLD in children and adolescents should follow international clinical guidelines, such as those from the American Diabetes Association (ADA) and the International Society for Pediatric and Adolescent Diabetes (ISPAD). Current guidelines recommend lifestyle and dietary modifications, exercise, screening, individualized patient assessment, and multidisciplinary patient management. This review assesses the revised terminology and discusses the epidemiology, risk factors, pathophysiology, diagnosis, and prevention of MAFLD in children and adolescents worldwide and in Mexico, and also considers the implications for public health.

Background: Standard computed tomography angiography (CTA) outputs a myriad of interrelated variables in the evaluation of suspected coronary artery disease (CAD). But an important proportion of obstructive lesions does not cause significant myocardial ischemia. Nowadays, machine learning (ML) allows integration of numerous variables through complex interdependencies that optimize classification and prediction at the individual level. We evaluated ML performance in integrating CTA and clinical variables to identify patients that demonstrate myocardial ischemia through PET and those who ultimately underwent early revascularization., Methods and Results: 830 patients with CTA and selective PET were analyzed. Nine clinical and 58 CTA variables were integrated through ensemble-boosting ML to identify patients with ischemia and those who underwent early revascularization. ML performance was compared against expert CTA interpretation, calcium score and clinical variables. While ML using all CTA variables achieved an AUC = 0.85, it was outperformed by expert CTA interpretation (AUC = 0.87, p < 0.01 for comparison), comparable to ML integration of CTA variables with clinical variables. However, the best performance was achieved by ML integration of expert CTA interpretation and clinical variables for both dependent variables (AUCs = 0.91 and 0.90, p < 0.001)., Conclusions: Machine learning integration of diagnostic CTA and clinical data may improve identification of patients with myocardial ischemia and those requiring early revascularization at the individual level. This could potentially aid in sparing the need for subsequent advanced imaging and better identifying patients in ultimate need for revascularization. While ML integrating all CTA variables did not outperform expert CTA interpretation, ML data integration from different sources consistently improves diagnostic performance., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Aims: Automated interpretation of electrocardiograms (ECGs) using deep neural networks (DNNs) has gained much attention recently. While the initial results have been encouraging, limited attention has been paid to whether such results can be trusted, which is paramount for their clinical implementation. This study aims to systematically investigate uncertainty estimation techniques for automated classification of ECGs using DNNs and to gain insight into its utility through a clinical simulation., Methods and Results: On a total of 526 656 ECGs from three different datasets, six different methods for estimation of aleatoric and epistemic uncertainty were systematically investigated. The methods were evaluated based on ranking, calibration, and robustness against out-of-distribution data. Furthermore, a clinical simulation was performed where increasing uncertainty thresholds were applied to achieve a clinically acceptable performance. Finally, the correspondence between the uncertainty of ECGs and the lack of interpretational agreement between cardiologists was estimated. Results demonstrated the largest benefit when modelling both epistemic and aleatoric uncertainty. Notably, the combination of variational inference with Bayesian decomposition and ensemble with auxiliary output outperformed the other methods. The clinical simulation showed that the accuracy of the algorithm increased as uncertain predictions were referred to the physician. Moreover, high uncertainty in DNN-based ECG classification strongly corresponded with a lower diagnostic agreement in cardiologist's interpretation ( P  < 0.001)., Conclusion: Uncertainty estimation is warranted in automated DNN-based ECG classification and its accurate estimation enables intermediate quality control in the clinical implementation of deep learning. This is an important step towards the clinical applicability of automated ECG diagnosis using DNNs., (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.)

[Figure: see text].

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.

Aims: Estimation of pre-test probability (PTP) of disease in patients with suspected coronary artery disease (CAD) is a common challenge. Due to decreasing prevalence of obstructive CAD in patients referred for diagnostic testing, the European Society of Cardiology suggested a new PTP (2019-ESC-PTP) model. The aim of this study was to validate that model., Methods and Results: Symptomatic patients referred for coronary computed tomography angiography (CTA) due to suspected CAD in a geographical uptake area of 3.3 million inhabitants were included. The reference standard was a combined endpoint of CTA and invasive coronary angiography (ICA) with obstructive CAD defined at ICA as a ≥50% diameter stenosis or fractional flow reserve ≤0.80 when performed. The 2019-ESC-PTP, 2013-ESC-PTP, and CAD Consortium basic PTP scores were calculated based on age, sex, and symptoms. Of the 42 328 identified patients, coronary stenosis was detected in 8.8% using the combined endpoint. The 2019-ESC-PTP and CAD Consortium basic scores classified substantially more patients into the low PTP groups (PTP < 15%) than did the 2013-ESC-PTP (64% and 65% vs. 16%, P < 0.001). Using the combined endpoint as reference, calibration of the 2019-ESC-PTP model was superior to the 2013-ESC-PTP and CAD Consortium basic score., Conclusion: The new 2019-ESC-PTP model is well calibrated and superior to the previously recommended models in predicting obstructive stenosis detected by a combined endpoint of CTA and ICA., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2020. For permissions, please email: journals.permissions@oup.com.)

Background: It is thought that heart failure (HF) patients may benefit from the evaluation of mechanical (dys)synchrony, and an independent inverse relationship between myocardial perfusion and ventricular synchrony has been suggested. We explore the relationship between quantitative myocardial perfusion and synchrony parameters when accounting for the presence and extent of fixed perfusion defects in patients with chronic HF., Methods: We studied 98 patients with chronic HF who underwent rest and stress Nitrogen-13 ammonia PET. Multivariate analyses of covariance were performed to determine relevant predictors of synchrony (measured as bandwidth, standard deviation, and entropy)., Results: In our population, there were 43 (44%) women and 55 men with a mean age of 71 ± 9.6 years. The SRS was the strongest independent predictor of mechanical synchrony variables (p < .01), among other considered predictors including: age, sex, body mass index, smoking, diabetes mellitus, dyslipidemia, hypertension, rest myocardial blood flow (MBF), and myocardial perfusion reserve (MPR). Results were similar when considering stress MBF instead of MPR., Conclusions: The existence and extent of fixed perfusion defects, but not the quantitative PET myocardial perfusion parameters (sMBF and MPR), constitute a significant independent predictor of ventricular mechanical synchrony in patients with chronic HF.

Background and Aims: Computed tomography (CT)-derived adipose tissue radiodensity represents a potential noninvasive surrogate marker for lipid deposition and obesity-related metabolic disease risk. We studied the effects of bariatric surgery on CT-derived adipose radiodensities in abdominal and femoral areas and their relationships to circulating metabolites in morbidly obese patients., Methods and Results: We examined 23 morbidly obese women who underwent CT imaging before and 6 months after bariatric surgery. Fifteen healthy non-obese women served as controls. Radiodensities of the abdominal subcutaneous (SAT) and visceral adipose tissue (VAT), and the femoral SAT, adipose tissue masses were measured in all participants. Circulating metabolites were measured by NMR. At baseline, radiodensities of abdominal fat depots were lower in the obese patients as compared to the controls. Surprisingly, radiodensity of femoral SAT was higher in the obese as compared to the controls. In the abdominal SAT depot, radiodensity strongly correlated with SAT mass (r = -0.72, p < 0.001). After surgery, the radiodensities of abdominal fat increased significantly (both p < 0.01), while femoral SAT radiodensity remained unchanged. Circulating ApoB/ApoA-I, leucine, valine, and GlycA decreased, while glycine levels significantly increased as compared to pre-surgical values (all p < 0.05). The increase in abdominal fat radiodensity correlated negatively with the decreased levels of ApoB/ApoA-I ratio, leucine and GlycA (all p < 0.05). The increase in abdominal SAT density was significantly correlated with the decrease in the fat depot mass (r = -0.66, p = 0.002)., Conclusion: Higher lipid content in abdominal fat depots, and lower content in femoral subcutaneous fat, constitute prominent pathophysiological features in morbid obesity. Further studies are needed to clarify the role of non-abdominal subcutaneous fat in the pathogenesis of obesity., Clinical Trial Registration Number: NCT01373892., Competing Interests: Declaration of Competing Interest No potential conflicts of interest related to the article were reported., (Copyright © 2020 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.)

Background: OSA conveys worse clinical outcomes in patients with coronary artery disease. The STOP-BANG score is a simple tool that evaluates the risk of OSA and can be added to the large number of clinical variables and scores that are obtained during the management of patients with myocardial infarction (MI). Currently, machine learning (ML) is able to select and integrate numerous variables to optimize prediction tasks., Research Question: Can the integration of STOP-BANG score with clinical data and scores through ML better identify patients who experienced an in-hospital cardiovascular event after acute MI?, Study Design and Methods: This is a prospective observational cohort study of 124 patients with acute MI of whom the STOP-BANG score classified 34 as low (27.4%), 30 as intermediate (24.2%), and 60 as high (48.4%) OSA-risk patients who were followed during hospitalization. ML implemented feature selection and integration across 47 variables (including STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction) to identify those patients who experienced an in-hospital cardiovascular event (ie, death, ventricular arrhythmias, atrial fibrillation, recurrent angina, reinfarction, stroke, worsening heart failure, or cardiogenic shock) after definitive MI treatment. Receiver operating characteristic curves were used to compare ML performance against STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction, independently., Results: There were an increasing proportion of cardiovascular events across the low, intermediate, and high OSA risk groups (P = .005). ML selected 7 accessible variables (ie, Killip class, leukocytes, GRACE score, c reactive protein, oxygen saturation, STOP-BANG score, and N-terminal prohormone of B-type natriuretic peptide); their integration outperformed all comparators (area under the curve, 0.83 [95% CI, 0.74-0.90]; P < .01)., Interpretation: The integration of the STOP-BANG score into clinical evaluation (considering Killip class, GRACE score, and simple laboratory values) of subjects who were admitted for an acute MI because of ML can significantly optimize the identification of patients who will experience an in-hospital cardiovascular event., (Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Background: We explored agreement in the quantification of myocardial perfusion by cross-comparison of implemented software packages (SPs) in three distinguishable patient profile populations., Methods: We studied 91 scans of patients divided into 3 subgroups based on their semi-quantitative perfusion findings: patients with normal perfusion, with reversible perfusion defects, and with fixed perfusion defects. Rest myocardial blood flow (MBF), stress MBF, and myocardial flow reserve (MFR) were obtained with QPET, SyngoMBF, and Carimas. Agreement between SPs was considered adequate when a pairwise standardized difference was found to be < 0.20 and its corresponding intraclass correlation coefficient was ≥ 0.75., Results: In patients with normal perfusion, two out of three comparisons of global stress MBF quantifications were outside the limits of agreement. In ischemic patients, all comparisons of global stress MBF and MFR were outside the limits of established agreement. In patients with fixed perfusion defects, all SP comparisons of perfusion quantifications were within the limit of agreement. Regionally, agreement of these perfusion estimates was mostly found for the left anterior descending artery vascular territory., Conclusion: Reversible defects demonstrated the worst agreement in global stress MBF and MFR and discrepancies showed to be regional dependent. Reproducibility between SPs should not be assumed.

Background Myocardial infarction is an important cause of morbidity and mortality in both men and women. Atypical or the absence of symptoms, more prevalent among women, may contribute to unrecognized myocardial infarctions and missed opportunities for preventive therapies. The aim of this research is to investigate sex-based differences of undiagnosed myocardial infarction in the general population. Methods and Results In the Lifelines Cohort Study, all individuals ≥18 years with a normal baseline ECG were followed from baseline visit till first follow-up visit (≈5 years, n=97 203). Individuals with infarct-related changes between baseline and follow-up ECGs were identified. The age- and sex-specific incidence rates were calculated and sex-specific cardiac symptoms and predictors of unrecognized myocardial infarction were determined. Follow-up ECG was available after a median of 3.8 (25th and 75th percentile: 3.0-4.6) years. During follow-up, 198 women experienced myocardial infarction (incidence rate 1.92 per 1000 persons-years) compared with 365 men (incidence rate 3.30; P <0.001 versus women). In 59 (30%) women, myocardial infarction was unrecognized compared with 60 (16%) men ( P <0.001 versus women). Individuals with unrecognized myocardial infarction less often reported specific cardiac symptoms compared with individuals with recognized myocardial infarction. Predictors of unrecognized myocardial infarction were mainly hypertension, smoking, and higher blood glucose level. Conclusions A substantial proportion of myocardial infarctions are unrecognized, especially in women. Opportunities for secondary preventive therapies remain underutilized if myocardial infarction is unrecognized.

Background: A significant number of variables are obtained when characterizing patients suspected with myocardial ischemia or at risk of MACE. Guidelines typically use a handful of them to support further workup or therapeutic decisions. However, it is likely that the numerous available predictors maintain intrinsic complex interrelations. Machine learning (ML) offers the possibility to elucidate complex patterns within data to optimize individual patient classification. We evaluated the feasibility and performance of ML in utilizing simple accessible clinical and functional variables for the identification of patients with ischemia or an elevated risk of MACE as determined through quantitative PET myocardial perfusion reserve (MPR)., Methods: 1,234 patients referred to Nitrogen-13 ammonia PET were analyzed. Demographic (4), clinical (8), and functional variables (9) were retrieved and input into a cross-validated ML workflow consisting of feature selection and modeling. Two PET-defined outcome variables were operationalized: (1) any myocardial ischemia (regional MPR < 2.0) and (2) an elevated risk of MACE (global MPR < 2.0). ROC curves were used to evaluate ML performance., Results: 16 features were included for boosted ensemble ML. ML achieved an AUC of 0.72 and 0.71 in identifying patients with myocardial ischemia and with an elevated risk of MACE, respectively. ML performance was superior to logistic regression when the latter used the ESC guidelines risk models variables for both PET-defined labels (P < .001 and P = .01, respectively)., Conclusions: ML is feasible and applicable in the evaluation and utilization of simple and accessible predictors for the identification of patients who will present myocardial ischemia and an elevated risk of MACE in quantitative PET imaging.

Science and technology are modifying medicine at a dizzying pace. Although access in our country to the benefits of innovations in the area of devices, data storage and artificial intelligence are still very restricted, the advance of digital medicine offers the opportunity to solve some of the biggest problems faced by medical practice and public health in Mexico. The potential areas where digital medicine can be disruptive are accessibility to quality medical care, centralization of specialties in large cities, dehumanization of medical treatment, lack of resources to access evidence-supported treatments, and among others. This review presents some of the advances that are guiding the new revolution in medicine, discusses the potential barriers to implementation, and suggest crucial elements for the path of incorporation of digital medicine in Mexico., (Copyright: © 2020 Permanyer.)

Science and technology are modifying medicine at a dizzying pace. Although access in our country to the benefits of innovations in the area of devices, data storage and artificial intelligence is still very restricted, the advance of digital medicine offers the opportunity to solve some of the biggest problems faced by medical practice and public health in Mexico. The potential areas where digital medicine can be disruptive are: accessibility to quality medical care, centralization of specialties in large cities, dehumanization of medical treatment, lack of resources to access evidence-supported treatments, among others. This review presents some of the advances that are guiding the new revolution in medicine, discusses the potential and potential barriers to implementation, and suggests crucial elements for the path of incorporation of digital medicine in Mexico., (Copyright: © 2020 Permanyer.)

Noninvasive imaging modalities offer the possibility to dynamically evaluate cardiac motion during the cardiac cycle by means of ECG-gated acquisitions. Such motion characterization along with orientation, segmentation preprocessing, and ultimately, phase analysis, can provide quantitative estimates of ventricular mechanical synchrony. Current evidence on the role of mechanical synchrony evaluation is mainly available for echocardiography and gated single-photon emission computed tomography, but less is known about the utilization of gated positron emission tomography (PET). Although data available are sparse, there is indication that mechanical synchrony evaluation can be of diagnostic and prognostic values in patients with known or suspected coronary artery disease-related myocardial ischemia, prediction of response to cardiac resynchronization therapy, and estimation of risk for adverse cardiac events in patients' heart failure. As such, the evaluation of mechanical ventricular synchrony through phase analysis of gated acquisitions represents a value addition to modern cardiac PET imaging modality, which warrants further research and development in the evaluation of patients with cardiovascular disease.

Aims: To provide a pooled estimation of contemporary pre-test probabilities (PTPs) of significant coronary artery disease (CAD) across clinical patient categories, re-evaluate the utility of the application of diagnostic techniques according to such estimates, and propose a comprehensive diagnostic technique selection tool for suspected CAD., Methods and Results: Estimates of significant CAD prevalence across sex, age, and type of chest pain categories from three large-scale studies were pooled (n = 15 815). The updated PTPs and diagnostic performance profiles of exercise electrocardiogram, invasive coronary angiography, coronary computed tomography angiography (CCTA), positron emission tomography (PET), stress cardiac magnetic resonance (CMR), and SPECT were integrated to define the PTP ranges in which ruling-out CAD is possible with a post-test probability of <10% and <5%. These ranges were then integrated in a new colour-coded tabular diagnostic technique selection tool. The Bayesian relationship between PTP and the rate of diagnostic false positives was explored to complement the characterization of their utility. Pooled CAD prevalence was 14.9% (range = 1-52), clearly lower than that used in current clinical guidelines. Ruling-out capabilities of non-invasive imaging were good overall. The greatest ruling-out capacity (i.e. post-test probability <5%) was documented by CCTA, PET, and stress CMR. With decreasing PTP, the fraction of false positive findings rapidly increased, although a lower CAD prevalence partially cancels out such effect., Conclusion: The contemporary PTP of significant CAD across symptomatic patient categories is substantially lower than currently assumed. With a low prevalence of the disease, non-invasive testing can rarely rule-in the disease and focus should shift to ruling-out obstructive CAD. The large proportion of false positive findings must be taken into account when patients with low PTP are investigated., (Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: journals.permissions@oup.com.)

Background: Ventricular tachycardia (VT) is one of the main predictors of mortality in Chagas cardiomyopathy (CC). Although the substrate of sustained and nonsustained-VT (NS-VT) seems to be the same, little is known about the distribution of late enhancement (LE). Our aim was to compare the clinical findings and the amount and patterns of LE in Chagas disease according to the presence and type of VT., Methods and Results: Magnetic resonance imaging was performed in 54 Chagas seropositive patients: 8 indeterminate and 46 with CC of whom 15 were without VT, 13 with NS-VT, and 18 with sustained-VT (S-VT). There were 31 males (57%), mean age was 55.9 ± 12.2 years. LE was found in 87% of all patients and in 50%, 80%, and 100% of the indeterminate, without VT and VT groups, respectively. The percentage of LE increased progressively in the indeterminate, CC without VT, and CC with VT groups; without a significant difference between NS-VT and S-VT (0.93%, 15.2%, 23.2%, and 21.4%, respectively). The amount of LE increased with the functional class. LE in the basal and mid lateral wall was more frequent in VT, without difference between S-VT and NS-VT. The only predictor of VT was the percentage of LE, odds ratio (OR), 6.2; (95% confidence interval [CI], 3.7-28.4; P = .01) with a cutoff of Odds Ratio 17.1%., Conclusions: The amount of LE increases in relation to the clinical stage of the disease and its functional class in Chagas seropositive patients. The amount of LE was the main predictor of VT, without difference between S-VT and NS-VT., (© 2019 Wiley Periodicals, Inc.)

Driven by recent developments in computational power, algorithms and web-based storage resources, machine learning (ML)-based artificial intelligence (AI) has quickly gained ground as the solution for many technological and societal challenges. AI education has become very popular and is oversubscribed at Dutch universities. Major investments were made in 2018 to develop and build the first AI-driven hospitals to improve patient care and reduce healthcare costs. AI has the potential to greatly enhance traditional statistical analyses in many domains and has been demonstrated to allow the discovery of 'hidden' information in highly complex datasets. As such, AI can also be of significant value in the diagnosis and treatment of cardiovascular disease, and the first applications of AI in the cardiovascular field are promising. However, many professionals in the cardiovascular field involved in patient care, education or science are unaware of the basics behind AI and the existing and expected applications in their field. In this review, we aim to introduce the broad cardiovascular community to the basics of modern ML-based AI and explain several of the commonly used algorithms. We also summarise their initial and future applications relevant to the cardiovascular field.

Background: Machine learning (ML) allows the exploration and progressive improvement of very complex high-dimensional data patterns that can be utilised to optimise specific classification and prediction tasks, outperforming traditional statistical approaches. An enormous acceleration of ready-to-use tools and artificial intelligence (AI) applications, shaped by the emergence, refinement, and application of powerful ML algorithms in several areas of knowledge, is ongoing. Although such progress has begun to permeate the medical sciences and clinical medicine, implementation in cardiovascular medicine and research is still in its infancy., Objectives: To lay out the theoretical framework, purpose, and structure of a novel AI consortium., Methods: We have established a new Dutch research consortium, the CVON-AI, supported by the Netherlands Heart Foundation, to catalyse and facilitate the development and utilisation of AI solutions for existing and emerging cardiovascular research initiatives and to raise AI awareness in the cardiovascular research community. CVON-AI will connect to previously established CVON consortia and apply a cloud-based AI platform to supplement their planned traditional data-analysis approach., Results: A pilot experiment on the CVON-AI cloud was conducted using cardiac magnetic resonance data. It demonstrated the feasibility of the platform and documented excellent correlation between AI-generated ventricular function estimates as compared to expert manual annotations. The resulting AI solution was then integrated in a web application., Conclusion: CVON-AI is a new consortium meant to facilitate the implementation and raise awareness of AI in cardiovascular research in the Netherlands. CVON-AI will create an accessible cloud-based platform for cardiovascular researchers, demonstrate the clinical applicability of AI, optimise the analytical methodology of other ongoing CVON consortia, and promote AI awareness through education and training.

Purpose: The spinal subarachnoid space (SSAS) is vital for neural performance. Although models of spinal diseases and trauma are used frequently, no methods exist to obtain high-resolution myelograms in rodents. Thereby, our aim was to explore the feasibility of obtaining high-resolution micro-CT myelograms of rats by contrast-enhanced dual-energy (DE) and single-energy (SE) digital subtraction., Methods: Micro-CT contrast-enhanced DE and SE imaging protocols were implemented with live adult rats (total of 18 animals). For each protocol, contrast agents based on iodine (Iomeron® 400 and Fenestra® VC) and gold nanoparticles (AuroVist™ 15 nm) were tested. For DE, images at low- and high-energy settings were acquired after contrast injection; for SE, one image was acquired before and the other after contrast injection. Post-processing consisted of region of interest selection, image registration, weighted subtraction, and longitudinal alignment., Results: High-resolution myelograms were obtained with contrast-enhanced digital subtraction protocols. After qualitative and quantitative (contrast-to-noise ratio) analyses, we found that the SE acquisition protocol with Iomeron® 400 provides the best images. 3D contour renderings allowed visualization of SSAS and identification of some anatomical structures within it., Conclusion: This in vivo study shows the potential of SE contrast-enhanced myelography for imaging SSAS in rat. This approach yields high-resolution 3D images without interference from adjacent anatomical structures, providing an innovative tool for further assessment of studies involving rat SSAS.

Psychosocial stress is a risk factor for the development of depression. Recent evidence suggests that glial activation could contribute to the development of depressive-like behaviour. This study aimed to evaluate in vivo whether repeated social defeat (RSD) induces short- and long-term inflammatory and metabolic alterations in the brain through positron emission tomography (PET). Male Wistar rats ( n = 40) were exposed to RSD by dominant Long-Evans rats on five consecutive days. Behavioural and biochemical alterations were assessed at baseline, day 5/6 and day 24/25 after the RSD protocol. Glial activation ( 11 C-PK11195 PET) and changes in brain metabolism ( 18 F-FDG PET) were evaluated on day 6, 11 and 25 (short-term), and at 3 and 6 months (long-term). Defeated rats showed transient depressive- and anxiety-like behaviour, increased corticosterone and brain IL-1β levels, as well as glial activation and brain hypometabolism in the first month after RSD. During the third- and six-month follow-up, no between-group differences in any investigated parameter were found. Therefore, non-invasive PET imaging demonstrated that RSD induces transient glial activation and reduces brain glucose metabolism in rats. These imaging findings were associated with stress-induced behavioural changes and support the hypothesis that neuroinflammation could be a contributing factor in the development of depression.

Myocardial Bridging (MB) refers to the band of myocardium that abnormally overlies a segment of a coronary artery. This paper quantitatively evaluates the influence of MB of the left anterior descending artery (LAD) on myocardial perfusion of the entire left ventricle. We studied 131 consecutive patients who underwent hybrid rest/stress 13 N-ammonia positron emission tomography (PET) and coronary computed tomography angiography (CCTA) due to suspected myocardial ischemia. Patients with previous myocardial infarction and/or significant coronary artery disease (≥ 50% stenosis) were excluded. Myocardial perfusion measurements were compared between patients with and without LAD-MB. Additionally, we evaluated the relationship between anatomical characteristics (length and depth) of LAD-MB and myocardial perfusion measurements. 17 (13%) patients presented a single LAD-MB. Global myocardial perfusion reserve (MPR) was lower in patients with LAD-MB than in patients without LAD-MB (1.9 ± 0.5 vs. 2.3 ± 0.6, p < 0.01). Global stress myocardial blood flow (MBF) was similar in patients with and without LAD-MB (2.2 ± 0.4 vs. 2.3 ± 0.7 ml/g/min, p = 0.40). Global rest MBF was higher in patients with LAD-MB than in patients without LAD-MB (1.2 ± 0.3 vs. 1.0 ± 0.2 ml/g/min, p < 0.01). Global rest MBF, stress MBF, and MPR quantifications were similar in patients with superficial and deep LAD-MB (all p = NS). We did not find any correlation between length and global rest MBF, stress MBF nor MPR (r = - 0.14, p = 0.59; r = 0.44, p = 0.07; and r = 0.45, p = 0.07 respectively). Quantitative myocardial perfusion suggests that LAD-MB may be related to impaired perfusion reserve, an indicator of microvascular dysfunction. Anatomical characteristics of LAD-MB were not related to changes in myocardial perfusion.

Cerebral blood flow (CBF) SPECT is an interesting methodology to study brain connectivity in mild cognitive impairment (MCI) since it is accessible worldwide and can be used as a biomarker of neuronal injury in MCI. In CBF SPECT, connectivity is grounded in group-based correlation networks. Therefore, topological metrics derived from the CBF correlation network cannot be used to support diagnosis and prognosis individually. However, methods to extract the individual patient contribution to topological metrics of group-based correlation networks were developed although not yet applied to MCI patients. Here, we investigate whether the episodic memory of 24 amnestic MCI patients correlates with individual patient contributions to topological metrics of the CBF correlation network. We first compared topological metrics of the MCI group network with the network corresponding to 26 controls. Metrics that showed significant differences were then used for the individual patient contribution analysis. We found that the global network modularity was increased while global efficiency decreased in the MCI network compared to the control. Most importantly, we found that episodic memory inversely correlates with the patient contribution to the global network modularity, which highlights the potential of this approach to develop a CBF connectivity-based biomarker at the individual level., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Aims: To evaluate the prognostic value of quantitative myocardial perfusion imaging with positron emission tomography (PET) for adverse cardiovascular outcomes in patients with known or suspected coronary artery disease (CAD)., Methods and Results: A search in MEDLINE and Embase was conducted for studies that evaluated (i) myocardial perfusion in absolute terms with PET, (ii) prognostic value for the development of major adverse cardiovascular events (MACE), cardiac death, and/or all-cause mortality, and (iii) patients with known or suspected CAD. Studies were divided according to the radiotracer utilized and their included population (patients with and without previous infarction). Comprehensive description and a selected instance of pooling were performed. Eight studies (n = 6804) were analysed and documented clear variability in population, quantitative PET variables operationalization [stress myocardial blood flow (sMBF) and flow reserve (MFR)], statistical covariate structure, follow-up, and radiotracer utilized. MFR was independently associated with MACE in eight studies [range of adjusted hazard ratios (HRs): 1.19-2.93]. The pooling instance demonstrated that MFR significantly associates with the development of MACEs (HR: 1.92 [1.29, 2.84]; P = 0.001). sMBF was only associated with MACE in two studies that evaluated it, and only one study documented sMBF as a better predictor than MFR., Conclusion: This systematic review demonstrates the prognostic value of quantitative myocardial perfusion evaluated with PET, in the form of MFR and sMBF, for the development of major adverse cardiovascular outcomes in populations with known or suspected CAD. In the qualitative comparison, MFR seems to outperform sMBF as an independent prognostic factor. Evidence is still lacking for assessing quantitative PET for the occurrence of cardiac death and all-cause mortality. There is clear heterogeneity in predictor operationalization and study performances.

Aims: To determine the ranges of pre-test probability (PTP) of coronary artery disease (CAD) in which stress electrocardiogram (ECG), stress echocardiography, coronary computed tomography angiography (CCTA), single-photon emission computed tomography (SPECT), positron emission tomography (PET), and cardiac magnetic resonance (CMR) can reclassify patients into a post-test probability that defines (>85%) or excludes (<15%) anatomically (defined by visual evaluation of invasive coronary angiography [ICA]) and functionally (defined by a fractional flow reserve [FFR] ≤0.8) significant CAD., Methods and Results: A broad search in electronic databases until August 2017 was performed. Studies on the aforementioned techniques in >100 patients with stable CAD that utilized either ICA or ICA with FFR measurement as reference, were included. Study-level data was pooled using a hierarchical bivariate random-effects model and likelihood ratios were obtained for each technique. The PTP ranges for each technique to rule-in or rule-out significant CAD were defined. A total of 28 664 patients from 132 studies that used ICA as reference and 4131 from 23 studies using FFR, were analysed. Stress ECG can rule-in and rule-out anatomically significant CAD only when PTP is ≥80% (76-83) and ≤19% (15-25), respectively. Coronary computed tomography angiography is able to rule-in anatomic CAD at a PTP ≥58% (45-70) and rule-out at a PTP ≤80% (65-94). The corresponding PTP values for functionally significant CAD were ≥75% (67-83) and ≤57% (40-72) for CCTA, and ≥71% (59-81) and ≤27 (24-31) for ICA, demonstrating poorer performance of anatomic imaging against FFR. In contrast, functional imaging techniques (PET, stress CMR, and SPECT) are able to rule-in functionally significant CAD when PTP is ≥46-59% and rule-out when PTP is ≤34-57%., Conclusion: The various diagnostic modalities have different optimal performance ranges for the detection of anatomically and functionally significant CAD. Stress ECG appears to have very limited diagnostic power. The selection of a diagnostic technique for any given patient to rule-in or rule-out CAD should be based on the optimal PTP range for each test and on the assumed reference standard.

Background: Cardiac PET quantifies stress myocardial blood flow (MBF) and perfusion reserve (MPR), while ECG-gated datasets can measure components of ventricular function simultaneously. Stress MBF seems to outperform MPR in the detection of significant CAD. However, it is uncertain which perfusion measurement is more related to ventricular function. We hypothesized that stress MBF correlates with ventricular function better than MPR in patients studied for suspected myocardial ischemia., Methods: We studied 248 patients referred to a rest and adenosine-stress Nitrogen-13 ammonia PET. We performed a multivariate analysis using systolic function (left ventricular ejection fraction, LVEF), diastolic function (mean filling rate in diastole, MFR/3), and synchrony (Entropy) as the outcome variables, and stress MBF, MPR, and relevant covariates as the predictors. Secondarily, we repeated the analysis for the subgroup of patients with and without a previous myocardial infarction (MI)., Results: 166 male and 82 female patients (mean age 63 ± 11 and 67 ± 11 year, respectively) were included. 60% of the patients presented hypertension, 57% dyslipidemia, 21% type 2 diabetes mellitus, 45% smoking, and 34.7% a previous MI. Mean stress MBF was 1.99 ± 0.75 mL/g/min, MPR = 2.55 ± 0.89, LVEF = 61.6 ± 15%, MFR/3 = 1.12 ± 0.38 EDV/s, and Entropy = 45.6 ± 11.3%. There was a significant correlation between stress MBF (P < .001) and ventricular function. This was stronger than the one for MPR (P = .063). Sex, age, diabetes, and extent of previous MI were also significant predictors. Results were similar for the analyses of the 2 subgroups., Conclusion: Stress MBF is better correlated with ventricular function than MPR, as evaluated by Nitrogen-13 ammonia PET, independently from other relevant cardiovascular risk factors and clinical covariates. This relationship between coronary vasodilatory capacity and ventricular function is sustained across groups with and without a previous MI.