Non-stenotic fibro-calcific aortic valve as a predictor of myocardial infarction recurrence.

BACKGROUND: Patients with acute myocardial infarction (AMI) are at increased risk of recurrent cardiovascular events. Non-stenotic aortic valve fibro-calcific remodeling (AVSc), reflecting systemic damage, may serve as a new marker of risk. OBJECTIVES: To stratify subgroups of AMI patients with specific probabilities of recurrent AMI and to evaluate the importance of AVSc in this setting. METHODS: Consecutive AMI patients (n = 2530) were admitted at Centro Cardiologico Monzino (2010-2019) and followed up for 5 years. Patients were divided into study (n = 1070) and test (n = 966) cohorts. Topological data analysis (TDA) was used to stratify patient subgroups, while Kaplan-Meier and Cox regressions analyses were used to evaluate the significance of baseline characteristics. RESULTS: TDA identified 11 subgroups of AMI patients with specific baseline characteristics. Two subgroups showed the highest rate of reinfarction after 5 years from the indexed AMI with a combined hazard ratio (HR) of 3.8 (95%CI: 2.7-5.4) compared to the other subgroups. This was confirmed in the test cohort (HR = 3.1; 95%CI: 2.2-4.3). These two subgroups were mostly men, with hypertension and dyslipidemia, who exhibit higher prevalence of AVSc, higher levels of high-sensitive c-reactive protein and creatinine. In the year-by-year analysis, AVSc, adjusted for all confounders, showed an independent association with the increased risk of reinfarction (odds ratio of ∼2 at all time-points), in both the study and the test cohorts (all p < 0.01). CONCLUSIONS: AVSc is a crucial variable for identifying AMI patients at high risk of recurrent AMI and its presence should be considered when assessing the management of AMI patients. The inclusion of AVSc in risk stratification models may improve the accuracy of predicting the likelihood of recurrent AMI, leading to more personalized treatment decisions.

We wanted to understand the factors that make some acute myocardial infarction (AMI) patients more likely to experience recurrent infarction after leaving the hospital. Specifically, we asked whether a heart valve condition called non-stenotic aortic valve fibro-calcific remodeling (AVSc) could be a crucial factor. Our study used advanced data analysis techniques, including topological data analysis (TDA), to explore this question. We unveil that AVSc is indeed a significant predictor of recurrent infarction in AMI patients. Our findings suggest that the presence of aortic valve remodeling should be taken into account when assessing the risk of recurrent AMI and managing these patients.

EMID2 is a novel biotherapeutic for aggressive cancers identified by in vivo screening.

BACKGROUND: New drugs to tackle the next pathway or mutation fueling cancer are constantly proposed, but 97% of them are doomed to fail in clinical trials, largely because they are identified by cellular or in silico screens that cannot predict their in vivo effect. METHODS: We screened an Adeno-Associated Vector secretome library (> 1000 clones) directly in vivo in a mouse model of cancer and validated the therapeutic effect of the first hit, EMID2, in both orthotopic and genetic models of lung and pancreatic cancer. RESULTS: EMID2 overexpression inhibited both tumor growth and metastatic dissemination, consistent with prolonged survival of patients with high levels of EMID2 expression in the most aggressive human cancers. Mechanistically, EMID2 inhibited TGFß maturation and activation of cancer-associated fibroblasts, resulting in more elastic ECM and reduced levels of YAP in the nuclei of cancer cells. CONCLUSION: This is the first in vivo screening, precisely designed to identify proteins able to interfere with cancer cell invasiveness. EMID2 was selected as the most potent protein, in line with the emerging relevance of the tumor extracellular matrix in controlling cancer cell invasiveness and dissemination, which kills most of cancer patients.

Whole-Blood Transcriptome Unveils Altered Immune Response in Acute Myocardial Infarction Patients With Aortic Valve Sclerosis.

BACKGROUND: Aortic valve sclerosis (AVSc) presents similar pathogenetic mechanisms to coronary artery disease and is associated with short- and long-term mortality in patients with coronary artery disease. Evidence of AVSc-specific pathophysiological traits in acute myocardial infarction (AMI) is currently lacking. Thus, we aimed to identify a blood-based transcriptional signature that could differentiate AVSc from no-AVSc patients during AMI. METHODS: Whole-blood transcriptome of AVSc (n=44) and no-AVSc (n=66) patients with AMI was assessed by RNA sequencing on hospital admission. Feature selection, differential expression, and enrichment analyses were performed to identify gene expression patterns discriminating AVSc from no-AVSc and infer functional associations. Multivariable Cox regression analysis was used to estimate the hazard ratios of cardiovascular events in AVSc versus no-AVSc patients. RESULTS: This cross-sectional study identified a panel of 100 informative genes capable of distinguishing AVSc from no-AVSc patients with 94% accuracy. Further analysis revealed significant mean differences in 143 genes, of which 30 genes withstood correction for age and previous AMI or coronary interventions. Functional inference unveiled a significant association between AVSc and key biological processes, including acute inflammatory responses, type I IFN (interferon) response, platelet activation, and hemostasis. Notably, patients with AMI with AVSc exhibited a significantly higher incidence of adverse cardiovascular events during a 10-year follow-up period, with a full adjusted hazard ratio of 2.4 (95% CI, 1.3-4.5). CONCLUSIONS: Our findings shed light on the molecular mechanisms underlying AVSc and provide potential prognostic insights for patients with AMI with AVSc. During AMI, patients with AVSc showed increased type I IFN (interferon) response and earlier adverse cardiovascular outcomes. Novel pharmacological therapies aiming at limiting type I IFN response during or immediately after AMI might improve poor cardiovascular outcomes of patients with AMI with AVSc.

CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision Transformer: A clinically-inspired deep learning pipeline.

BACKGROUND AND OBJECTIVE: The standard non-invasive imaging technique used to assess the severity and extent of Coronary Artery Disease (CAD) is Coronary Computed Tomography Angiography (CCTA). However, manual grading of each patient's CCTA according to the CAD-Reporting and Data System (CAD-RADS) scoring is time-consuming and operator-dependent, especially in borderline cases. This work proposes a fully automated, and visually explainable, deep learning pipeline to be used as a decision support system for the CAD screening procedure. The pipeline performs two classification tasks: firstly, identifying patients who require further clinical investigations and secondly, classifying patients into subgroups based on the degree of stenosis, according to commonly used CAD-RADS thresholds. METHODS: The pipeline pre-processes multiplanar projections of the coronary arteries, extracted from the original CCTAs, and classifies them using a fine-tuned Multi-Axis Vision Transformer architecture. With the aim of emulating the current clinical practice, the model is trained to assign a per-patient score by stacking the bi-dimensional longitudinal cross-sections of the three main coronary arteries along channel dimension. Furthermore, it generates visually interpretable maps to assess the reliability of the predictions. RESULTS: When run on a database of 1873 three-channel images of 253 patients collected at the Monzino Cardiology Center in Milan, the pipeline obtained an AUC of 0.87 and 0.93 for the two classification tasks, respectively. CONCLUSION: According to our knowledge, this is the first model trained to assign CAD-RADS scores learning solely from patient scores and not requiring finer imaging annotation steps that are not part of the clinical routine.

Circulating Small Extracellular Vesicles Reflect the Severity of Myocardial Damage in STEMI Patients.

Circulating small extracellular vesicles (sEVs) contribute to inflammation, coagulation and vascular injury, and have great potential as diagnostic markers of disease. The ability of sEVs to reflect myocardial damage assessed by Cardiac Magnetic Resonance (CMR) in ST-segment elevation myocardial infarction (STEMI) is unknown. To fill this gap, plasma sEVs were isolated from 42 STEMI patients treated by primary percutaneous coronary intervention (pPCI) and evaluated by CMR between days 3 and 6. Nanoparticle tracking analysis showed that sEVs were greater in patients with anterior STEMI (p = 0.0001), with the culprit lesion located in LAD (p = 0.045), and in those who underwent late revascularization (p = 0.038). A smaller sEV size was observed in patients with a low myocardial salvage index (MSI, p = 0.014). Patients with microvascular obstruction (MVO) had smaller sEVs (p < 0.002) and lower expression of the platelet marker CD41-CD61 (p = 0.039). sEV size and CD41-CD61 expression were independent predictors of MVO/MSI (OR [95% CI]: 0.93 [0.87-0.98] and 0.04 [0-0.61], respectively). In conclusion, we provide evidence that the CD41-CD61 expression in sEVs reflects the CMR-assessed ischemic damage after STEMI. This finding paves the way for the development of a new strategy for the timely identification of high-risk patients and their treatment optimization.

The cardiopulmonary exercise test in the prognostic evaluation of patients with heart failure and cardiomyopathies: the long history of making a one-size-fits-all suit.

Cardiopulmonary exercise test (CPET) has become pivotal in the functional evaluation of patients with chronic heart failure (HF), supplying a holistic evaluation both in terms of exercise impairment degree and possible underlying mechanisms. Conversely, there is growing interest in investigating possible multiparametric approaches in order to improve the overall HF risk stratification. In such a context, in 2013, a group of 13 Italian centres skilled in HF management and CPET analysis built the Metabolic Exercise test data combined with Cardiac and Kidney Indexes (MECKI) score, based on the dynamic assessment of HF patients and on some other instrumental and laboratory parameters. Subsequently, the MECKI score, initially developed on a cohort of 2716 HF patients, has been extensively validated as well as challenged with the other multiparametric scores, achieving optimal results. Meanwhile, the MECKI score research group has grown over time, involving up to now a total of 27 centres with an available database accounting for nearly 8000 HF patients. This exciting joint effort from multiple HF Italian centres allowed to investigate different HF research field in order to deepen the mechanisms underlying HF, to improve the ability to identify patients at the highest risk as well as to analyse particular HF categories. Most recently, some of the participants of the MECKI score group started to join the forces in investigating a possible additive role of CPET assessment in the cardiomyopathy setting too. The present study tells the ten-year history of the MECKI score presenting the most important results achieved as well as those projects in the pipeline, this exciting journey being far to be concluded.

Patient-specific primary and pluripotent stem cell-derived stromal cells recapitulate key aspects of arrhythmogenic cardiomyopathy.

Primary cardiac mesenchymal stromal cells (C-MSCs) can promote the aberrant remodeling of cardiac tissue that characterizes arrhythmogenic cardiomyopathy (ACM) by differentiating into adipocytes and myofibroblasts. These cells' limitations, including restricted access to primary material and its manipulation have been overcome by the advancement of human induced pluripotent stem cells (hiPSCs), and their ability to differentiate towards the cardiac stromal population. C-MSCs derived from hiPSCs make it possible to work with virtually unlimited numbers of cells that are genetically identical to the cells of origin. We performed in vitro experiments on primary stromal cells (Primary) and hiPSC-derived stromal cells (hiPSC-D) to compare them as tools to model ACM. Both Primary and hiPSC-D cells expressed mesenchymal surface markers and possessed typical MSC differentiation potentials. hiPSC-D expressed desmosomal genes and proteins and shared a similar transcriptomic profile with Primary cells. Furthermore, ACM hiPSC-D exhibited higher propensity to accumulate lipid droplets and collagen compared to healthy control cells, similar to their primary counterparts. Therefore, both Primary and hiPSC-D cardiac stromal cells obtained from ACM patients can be used to model aspects of the disease. The choice of the most suitable model will depend on experimental needs and on the availability of human source samples.

Multi-omics in thoracic aortic aneurysm: the complex road to the simplification.

BACKGROUND: Thoracic aortic aneurysm (TAA) is a serious condition that affects the aorta, characterized by the dilation of its first segment. The causes of TAA (e.g., age, hypertension, genetic syndromes) are heterogeneous and contribute to the weakening of the aortic wall. This complexity makes treating this life-threatening aortopathy challenging, as there are currently no etiological therapy available, and pharmacological strategies, aimed at avoiding surgical aortic replacement, are merely palliative. Recent studies on novel therapies for TAA have focused on identifying biological targets and etiological mechanisms of the disease by using advanced -omics techniques, including epigenomics, transcriptomics, proteomics, and metabolomics approaches. METHODS: This review presents the latest findings from -omics approaches and underscores the importance of integrating multi-omics data to gain more comprehensive understanding of TAA. RESULTS: Literature suggests that the alterations in TAA mediators frequently involve members of pro-fibrotic process (i.e., TGF-ß signaling pathways) or proteins associated with cell/extracellular structures (e.g., aggrecans). Further analyses often reported the importance in TAA of processes as inflammation (PCR, CD3, leukotriene compounds), oxidative stress (chromatin OXPHOS, fatty acids), mitochondrial respiration and glycolysis/gluconeogenesis (e.g., PPARs and HIF1a). Of note, more recent metabolomics studies added novel molecular markers to the list of TAA-specific detrimental mediators (proteoglycans). CONCLUSION: It is increasingly clear that integrating data from different -omics branches, along with clinical data, is essential as well as complicated both to reveal hidden relevant information and to address complex diseases such as TAA. Importantly, recent progresses in metabolomics highlighted novel potential and unprecedented marks in TAA diagnosis and therapy.

Omics Analyses of Stromal Cells from ACM Patients Reveal Alterations in Chromatin Organization and Mitochondrial Homeostasis.

Arrhythmogenic cardiomyopathy (ACM) is a genetic disorder characterized by ventricular arrhythmias, contractile dysfunctions and fibro-adipose replacement of myocardium. Cardiac mesenchymal stromal cells (CMSCs) participate in disease pathogenesis by differentiating towards adipocytes and myofibroblasts. Some altered pathways in ACM are known, but many are yet to be discovered. We aimed to enrich the understanding of ACM pathogenesis by comparing epigenetic and gene expression profiles of ACM-CMSCs with healthy control (HC)-CMSCs. Methylome analysis identified 74 differentially methylated nucleotides, most of them located on the mitochondrial genome. Transcriptome analysis revealed 327 genes that were more expressed and 202 genes that were less expressed in ACM- vs. HC-CMSCs. Among these, genes implicated in mitochondrial respiration and in epithelial-to-mesenchymal transition were more expressed, and cell cycle genes were less expressed in ACM- vs. HC-CMSCs. Through enrichment and gene network analyses, we identified differentially regulated pathways, some of which never associated with ACM, including mitochondrial functioning and chromatin organization, both in line with methylome results. Functional validations confirmed that ACM-CMSCs exhibited higher amounts of active mitochondria and ROS production, a lower proliferation rate and a more pronounced epicardial-to-mesenchymal transition compared to the controls. In conclusion, ACM-CMSC-omics revealed some additional altered molecular pathways, relevant in disease pathogenesis, which may constitute novel targets for specific therapies.

Ischemic wound revascularization by the stromal vascular fraction relies on host-donor hybrid vessels.

Nonhealing wounds place a significant burden on both quality of life of affected patients and health systems. Skin substitutes are applied to promote the closure of nonhealing wounds, although their efficacy is limited by inadequate vascularization. The stromal vascular fraction (SVF) from the adipose tissue is a promising therapy to overcome this limitation. Despite a few successful clinical trials, its incorporation in the clinical routine has been hampered by their inconsistent results. All these studies concluded by warranting pre-clinical work aimed at both characterizing the cell types composing the SVF and shedding light on their mechanism of action. Here, we established a model of nonhealing wound, in which we applied the SVF in combination with a clinical-grade skin substitute. We purified the SVF cells from transgenic animals to trace their fate after transplantation and observed that it gave rise to a mature vascular network composed of arteries, capillaries, veins, as well as lymphatics, structurally and functionally connected with the host circulation. Then we moved to a human-in-mouse model and confirmed that SVF-derived endothelial cells formed hybrid human-mouse vessels, that were stabilized by perivascular cells. Mechanistically, SVF-derived endothelial cells engrafted and expanded, directly contributing to the formation of new vessels, while a population of fibro-adipogenic progenitors stimulated the expansion of the host vasculature in a paracrine manner. These data have important clinical implications, as they provide a steppingstone toward the reproducible and effective adoption of the SVF as a standard care for nonhealing wounds.

A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images.

BACKGROUND AND OBJECTIVE: In patients with suspected Coronary Artery Disease (CAD), the severity of stenosis needs to be assessed for precise clinical management. An automatic deep learning-based algorithm to classify coronary stenosis lesions according to the Coronary Artery Disease Reporting and Data System (CAD-RADS) in multiplanar reconstruction images acquired with Coronary Computed Tomography Angiography (CCTA) is proposed. METHODS: In this retrospective study, 288 patients with suspected CAD who underwent CCTA scans were included. To model long-range semantic information, which is needed to identify and classify stenosis with challenging appearance, we adopted a token-mixer architecture (ConvMixer), which can learn structural relationship over the whole coronary artery. ConvMixer consists of a patch embedding layer followed by repeated convolutional blocks to enable the algorithm to learn long-range dependences between pixels. To visually assess ConvMixer performance, Gradient-Weighted Class Activation Mapping (Grad-CAM) analysis was used. RESULTS: Experimental results using 5-fold cross-validation showed that our ConvMixer can classify significant coronary artery stenosis (i.e., stenosis with luminal narrowing ≥50%) with accuracy and sensitivity of 87% and 90%, respectively. For CAD-RADS 0 vs. 1-2 vs. 3-4 vs. 5 classification, ConvMixer achieved accuracy and sensitivity of 72% and 75%, respectively. Additional experiments showed that ConvMixer achieved a better trade-off between performance and complexity compared to pyramid-shaped convolutional neural networks. CONCLUSIONS: Our algorithm might provide clinicians with decision support, potentially reducing the interobserver variability for coronary artery stenosis evaluation.

Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment.

BACKGROUND: Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED). METHODS: In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi42(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability. RESULTS: In the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF - 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s). CONCLUSIONS: Experimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.

Application of AI in cardiovascular multimodality imaging.

Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.

Spectrum of Rare and Common Genetic Variants in Arrhythmogenic Cardiomyopathy Patients.

Arrhythmogenic cardiomyopathy (ACM) is a rare inherited disorder, whose genetic cause is elusive in about 50-70% of cases. ACM presents a variable disease course which could be influenced by genetics. We performed next-generation sequencing on a panel of 174 genes associated with inherited cardiovascular diseases on 82 ACM probands (i) to describe and classify the pathogenicity of rare variants according to the American College of Medical Genetics and Genomics both for ACM-associated genes and for genes linked to other cardiovascular genetic conditions; (ii) to assess, for the first time, the impact of common variants on the ACM clinical disease severity by genotype-phenotype correlation and survival analysis. We identified 15 (likely) pathogenic variants and 66 variants of uncertain significance in ACM-genes and 4 high-impact variants in genes never associated with ACM (ABCC9, APOB, DPP6, MIB1), which deserve future consideration. In addition, we found 69 significant genotype-phenotype associations between common variants and clinical parameters. Arrhythmia-associated polymorphisms resulted in an increased risk of arrhythmic events during patients' follow-up. The description of the genetic framework of our population and the observed genotype-phenotype correlation constitutes the starting point to address the current lack of knowledge in the genetics of ACM.

Whole-Blood Transcriptional Profiles Enable Early Prediction of the Presence of Coronary Atherosclerosis and High-Risk Plaque Features at Coronary CT Angiography.

Existing tools to estimate cardiovascular (CV) risk have sub-optimal predictive capacities. In this setting, non-invasive imaging techniques and omics biomarkers could improve risk-prediction models for CV events. This study aimed to identify gene expression patterns in whole blood that could differentiate patients with severe coronary atherosclerosis from subjects with a complete absence of detectable coronary artery disease and to assess associations of gene expression patterns with plaque features in coronary CT angiography (CCTA). Patients undergoing CCTA for suspected coronary artery disease (CAD) were enrolled. Coronary stenosis was quantified and CCTA plaque features were assessed. The whole-blood transcriptome was analyzed with RNA sequencing. We detected highly significant differences in the circulating transcriptome between patients with high-degree coronary stenosis (≥70%) in the CCTA and subjects with an absence of coronary plaque. Notably, regression analysis revealed expression signatures associated with the Leaman score, the segment involved score, the segment stenosis score, and plaque volume with density <150 HU at CCTA. This pilot study shows that patients with significant coronary stenosis are characterized by whole-blood transcriptome profiles that may discriminate them from patients without CAD. Furthermore, our results suggest that whole-blood transcriptional profiles may predict plaque characteristics.

Reduction of Cardiac Fibrosis by Interference With YAP-Dependent Transactivation.

BACKGROUND: Conversion of cardiac stromal cells into myofibroblasts is typically associated with hypoxia conditions, metabolic insults, and/or inflammation, all of which are predisposing factors to cardiac fibrosis and heart failure. We hypothesized that this conversion could be also mediated by response of these cells to mechanical cues through activation of the Hippo transcriptional pathway. The objective of the present study was to assess the role of cellular/nuclear straining forces acting in myofibroblast differentiation of cardiac stromal cells under the control of YAP (yes-associated protein) transcription factor and to validate this finding using a pharmacological agent that interferes with the interactions of the YAP/TAZ (transcriptional coactivator with PDZ-binding motif) complex with their cognate transcription factors TEADs (TEA domain transcription factors), under high-strain and profibrotic stimulation. METHODS: We employed high content imaging, 2-dimensional/3-dimensional culture, atomic force microscopy mapping, and molecular methods to prove the role of cell/nuclear straining in YAP-dependent fibrotic programming in a mouse model of ischemia-dependent cardiac fibrosis and in human-derived primitive cardiac stromal cells. We also tested treatment of cells with Verteporfin, a drug known to prevent the association of the YAP/TAZ complex with their cognate transcription factors TEADs. RESULTS: Our experiments suggested that pharmacologically targeting the YAP-dependent pathway overrides the profibrotic activation of cardiac stromal cells by mechanical cues in vitro, and that this occurs even in the presence of profibrotic signaling mediated by TGF-ß1 (transforming growth factor beta-1). In vivo administration of Verteporfin in mice with permanent cardiac ischemia reduced significantly fibrosis and morphometric remodeling but did not improve cardiac performance. CONCLUSIONS: Our study indicates that preventing molecular translation of mechanical cues in cardiac stromal cells reduces the impact of cardiac maladaptive remodeling with a positive effect on fibrosis.

The Impact of COVID-19 Pandemic on Scientific Research: an Upcoming New Wave?

The coronavirus disease (COVID)-19 pandemic continues to have an impact on health care. A potential new wave can be foreseen concerning the impact of the pandemic on medical research and literature. We focused our attention on journals belonging to "Medicine, General and Internal" Clarivate™ category and "Q1" journal impact factor quartile. We found that since January 2020, 9621 papers regarding COVID-19 have been published in these journals. This occurred at the expense of non-COVID-19-related scientific papers as most journals did not increase the total number of their published articles. Thus, our analysis may outlook a new potential scientific wave related to COVID-19, in addition to the clinical ones, possibly delaying the improvement in the quality of care for other diseases in the next years.

Sex-Specific Cell Types and Molecular Pathways Indicate Fibro-Calcific Aortic Valve Stenosis.

Background: Aortic stenosis (AS) is the most common valve disorder characterized by fibro-calcific remodeling of leaflets. Recent evidence indicated that there is a sex-related difference in AS development and progression. Fibrotic remodeling is peculiar in women's aortic valves, while men's leaflets are more calcified. Our study aimed to assess aortic valve fibrosis (AVF) in a severe AS cohort using non-invasive diagnostic tools and determine whether sex-specific pathological pathways and cell types are associated with severe AS. Materials and Methods: We have included 28 men and 28 women matched for age with severe AS who underwent echocardiography and cardiac contrast-enhanced computed tomography (CT) before intervention. The calcium and fibrosis volumes were assessed and quantified using the ImageJ thresholding method, indexed calcium and fibrosis volume were calculated by dividing the volume by the aortic annular area. For a deeper understanding of molecular mechanisms characterizing AS disorder, differentially expressed genes and functional inferences between women and men's aortic valves were carried out on a publicly available microarray-based gene expression dataset (GSE102249). Cell types enrichment analysis in stenotic aortic valve tissues was used to reconstruct the sex-specific cellular composition of stenotic aortic valves. Results: In agreement with the literature, our CT quantifications showed that women had significantly lower aortic valve calcium content compared to men, while fibrotic tissue composition was significantly higher in women than men. The expression profiles of human stenotic aortic valves confirm sex-dependent processes. Pro-fibrotic processes were prevalent in women, while pro-inflammatory ones, linked to the immune response system, were enhanced in men. Cell-type enrichment analysis showed that mesenchymal cells were over-represented in AS valves of women, whereas signatures for monocytes, macrophages, T and B cells were enriched men ones. Conclusions: Our data provide the basis that the fibro-calcific process of the aortic valve is sex-specific, both at gene expression and cell type level. The quantification of aortic valve fibrosis by CT could make it possible to perform population-based studies and non-invasive assessment of novel therapies to reduce or halt sex-related calcific aortic valve stenosis (CAVS) progression, acting in an optimal window of opportunity early in the course of the disease.