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.

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.

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.

Diagnostic performance of deep learning algorithm for analysis of computed tomography myocardial perfusion.

PURPOSE: To evaluate the diagnostic accuracy of a deep learning (DL) algorithm predicting hemodynamically significant coronary artery disease (CAD) by using a rest dataset of myocardial computed tomography perfusion (CTP) as compared to invasive evaluation. METHODS: One hundred and twelve consecutive symptomatic patients scheduled for clinically indicated invasive coronary angiography (ICA) underwent CCTA plus static stress CTP and ICA with invasive fractional flow reserve (FFR) for stenoses ranging between 30 and 80%. Subsequently, a DL algorithm for the prediction of significant CAD by using the rest dataset (CTP-DLrest) and stress dataset (CTP-DLstress) was developed. The diagnostic accuracy for identification of significant CAD using CCTA, CCTA + CTP stress, CCTA + CTP-DLrest, and CCTA + CTP-DLstress was measured and compared. The time of analysis for CTP stress, CTP-DLrest, and CTP-DLStress was recorded. RESULTS: Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and area under the curve (AUC) of CCTA alone and CCTA + CTPStress were 100%, 33%, 100%, 54%, 63%, 67% and 86%, 89%, 89%, 86%, 88%, 87%, respectively. Patient-specific sensitivity, specificity, NPV, PPV, accuracy, and AUC of CCTA + DLrest and CCTA + DLstress were 100%, 72%, 100%, 74%, 84%, 96% and 93%, 83%, 94%, 81%, 88%, 98%, respectively. All CCTA + CTP stress, CCTA + CTP-DLRest, and CCTA + CTP-DLStress significantly improved detection of hemodynamically significant CAD compared to CCTA alone (p < 0.01). Time of CTP-DL was significantly lower as compared to human analysis (39.2 ± 3.2 vs. 379.6 ± 68.0 s, p < 0.001). CONCLUSION: Evaluation of myocardial ischemia using a DL approach on rest CTP datasets is feasible and accurate. This approach may be a useful gatekeeper prior to CTP stress..

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.

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.

Multiomic Approaches to Uncover the Complexities of Dystrophin-Associated Cardiomyopathy.

Despite major progress in treating skeletal muscle disease associated with dystrophinopathies, cardiomyopathy is emerging as a major cause of death in people carrying dystrophin gene mutations that remain without a targeted cure even with new treatment directions and advances in modelling abilities. The reasons for the stunted progress in ameliorating dystrophin-associated cardiomyopathy (DAC) can be explained by the difficulties in detecting pathophysiological mechanisms which can also be efficiently targeted within the heart in the widest patient population. New perspectives are clearly required to effectively address the unanswered questions concerning the identification of authentic and effectual readouts of DAC occurrence and severity. A potential way forward to achieve further therapy breakthroughs lies in combining multiomic analysis with advanced preclinical precision models. This review presents the fundamental discoveries made using relevant models of DAC and how omics approaches have been incorporated to date.

Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation.

Mitral valve prolapse (MVP) associated with severe mitral regurgitation is a debilitating disease with no pharmacological therapies available. MicroRNAs (miRNA) represent an emerging class of circulating biomarkers that have never been evaluated in MVP human plasma. Our aim was to identify a possible miRNA signature that is able to discriminate MVP patients from healthy subjects (CTRL) and to shed light on the putative altered molecular pathways in MVP. We evaluated a plasma miRNA profile using Human MicroRNA Card A followed by real-time PCR validations. In addition, to assess the discriminative power of selected miRNAs, we implemented a machine learning analysis. MiRNA profiling and validations revealed that miR-140-3p, 150-5p, 210-3p, 451a, and 487a-3p were significantly upregulated in MVP, while miR-223-3p, 323a-3p, 340-5p, and 361-5p were significantly downregulated in MVP compared to CTRL (p ≤ 0.01). Functional analysis identified several biological processes possible linked to MVP. In addition, machine learning analysis correctly classified MVP patients from CTRL with high accuracy (0.93) and an area under the receiving operator characteristic curve (AUC) of 0.97. To the best of our knowledge, this is the first study performed on human plasma, showing a strong association between miRNAs and MVP. Thus, a circulating molecular signature could be used as a first-line, fast, and cheap screening tool for MVP identification.

Diabetes Induces a Transcriptional Signature in Bone Marrow-Derived CD34+ Hematopoietic Stem Cells Predictive of Their Progeny Dysfunction.

Hematopoietic stem/progenitor cells (HSPCs) participate in cardiovascular (CV) homeostasis and generate different types of blood cells including lymphoid and myeloid cells. Diabetes mellitus (DM) is characterized by chronic increase of pro-inflammatory mediators, which play an important role in the development of CV disease, and increased susceptibility to infections. Here, we aimed to evaluate the impact of DM on the transcriptional profile of HSPCs derived from bone marrow (BM). Total RNA of BM-derived CD34+ stem cells purified from sternal biopsies of patients undergoing coronary bypass surgery with or without DM (CAD and CAD-DM patients) was sequenced. The results evidenced 10566 expressed genes whose 79% were protein-coding genes, and 21% non-coding RNA. We identified 139 differentially expressed genes (p-value < 0.05 and |log2 FC| > 0.5) between the two comparing groups of CAD and CAD-DM patients. Gene Set Enrichment Analysis (GSEA), based on Gene Ontology biological processes (GO-BP) terms, led to the identification of fourteen overrepresented biological categories in CAD-DM samples. Most of the biological processes were related to lymphocyte activation, chemotaxis, peptidase activity, and innate immune response. Specifically, HSPCs from CAD-DM patients displayed reduced expression of genes coding for proteins regulating antibacterial and antivirus host defense as well as macrophage differentiation and lymphocyte emigration, proliferation, and differentiation. However, within the same biological processes, a consistent number of inflammatory genes coding for chemokines and cytokines were up-regulated. Our findings suggest that DM induces transcriptional alterations in HSPCs, which are potentially responsible of progeny dysfunction.

Evaluation of Oxford Nanopore MinION RNA-Seq Performance for Human Primary Cells.

Transcript sequencing is a crucial tool for gaining a deep understanding of biological processes in diagnostic and clinical medicine. Given their potential to study novel complex eukaryotic transcriptomes, long-read sequencing technologies are able to overcome some limitations of short-read RNA-Seq approaches. Oxford Nanopore Technologies (ONT) offers the ability to generate long-read sequencing data in real time via portable protein nanopore USB devices. This work aimed to provide the user with the number of reads that should be sequenced, through the ONT MinION platform, to reach the desired accuracy level for a human cell RNA study. We sequenced three cDNA libraries prepared from poly-adenosine RNA of human primary cardiac fibroblasts. Since the runs were comparable, they were combined in a total dataset of 48 million reads. Synthetic datasets with different sizes were generated starting from the total and analyzed in terms of the number of identified genes and their expression levels. As expected, an improved sensitivity was obtained, increasing the sequencing depth, particularly for the non-coding genes. The reliability of expression levels was assayed by (i) comparison with PCR quantifications of selected genes and (ii) by the implementation of a user-friendly multiplexing method in a single run.

GARS: Genetic Algorithm for the identification of a Robust Subset of features in high-dimensional datasets.

BACKGROUND: Feature selection is a crucial step in machine learning analysis. Currently, many feature selection approaches do not ensure satisfying results, in terms of accuracy and computational time, when the amount of data is huge, such as in 'Omics' datasets. RESULTS: Here, we propose an innovative implementation of a genetic algorithm, called GARS, for fast and accurate identification of informative features in multi-class and high-dimensional datasets. In all simulations, GARS outperformed two standard filter-based and two 'wrapper' and one embedded' selection methods, showing high classification accuracies in a reasonable computational time. CONCLUSIONS: GARS proved to be a suitable tool for performing feature selection on high-dimensional data. Therefore, GARS could be adopted when standard feature selection approaches do not provide satisfactory results or when there is a huge amount of data to be analyzed.

Plasma phospholipid dysregulation in patients with cystathionine-ß synthase deficiency.

BACKGROUND & AIMS: Patients with cystathionine ß-synthase deficiency (CBSD) exhibit high circulating levels of homocysteine and enhanced lipid peroxidation. We have characterized the plasma lipidome in CBSD patients and related lipid abnormalities with reactions underlying enhanced homocysteine levels. METHODS AND RESULTS: Using an ultra-high-performance liquid chromatography-electrospray ionization-quadrupole-time of flight-mass spectrometry method, plasma lipids were determined with an untargeted lipidomics approach in 11 CBSD patients and 11 matched healthy subjects (CTRL). Compared to CTRL, CBSD patients had a higher medium and long-chain polyunsaturated fatty acids (PUFA) content in phosphatidylethanolamine (PE) and lysophosphatidylethanolamine (LPE) species (p < 0.02), and depletion of phosphatidylcholine (PC; p = 0.02) and of lysophosphatidylcholine (LPC; p = 0.003) species containing docosahexaenoic acid (DHA), suggesting impaired phosphatidylethanolamine-N-methyltransferase (PEMT) activity. PEMT converts PE into PC using methyl group by S-adenosylmethionine (SAM) thus converted in S-adenosylhomocysteine (SAH). Whole blood SAM and SAH concentrations by liquid chromatography tandem mass spectrometry were 1.4-fold (p = 0.015) and 5.3-fold (p = 0.003) higher in CBSD patients than in CTRL. A positive correlation between SAM/SAH and PC/PE ratios (r = 0.520; p = 0.019) was found. CONCLUSIONS: A novel biochemical abnormality in CBSD patients consisting in depletion of PC and LPC species containing DHA and accumulation of PUFA in PE and LPE species is revealed by this lipidomic approach. Changes in plasma SAM and SAH concentrations are associated with such phospholipid dysregulation. Given the key role of DHA in thrombosis prevention, depletion of PC species containing DHA in CBSD patients provides a new direction to understand the poor cardiovascular outcome of patients with homocystinuria.

Melanocortin-1 Receptor Positively Regulates Human Artery Endothelial Cell Migration.

BACKGROUND/AIMS: Melanocortin receptors (MCRs) belong to a hormonal signalling pathway with multiple homeostatic and protective actions. Microvascular and umbilical vein endothelial cells (ECs) express components of the melanocortin system, including the type 1 receptor (MC1R), playing a role in modulating inflammation and vascular tone. Since ECs exhibit a remarkable heterogeneity, we investigated whether human artery ECs express any functional MCR and whether its activation affects cell migration. METHODS: We used reverse transcription real-time PCR to examine the expression of melanocortin system components in primary human artery ECs. We assessed MC1R protein expression and activity by western blot, immunohistochemistry, cAMP production, and intracellular Ca²âº mobilization assays. We performed gap closure and scratch tests to examine cell migration after stimulation with alpha-melanocyte-stimulating hormone (α-MSH), the receptor highest-affinity natural ligand. We assessed differential time-dependent transcriptional changes in migrating cells by microarray analysis. RESULTS: We showed that human aortic ECs (HAoECs) express a functionally active MC1R. Unlike microvascular ECs, arterial cells did not express the α-MSH precursor proopiomelanocortin, nor produced the hormone. MC1R engagement with a single pulse of α-MSH accelerated HAoEC migration both in the directional migration assay and in the scratch wound healing test. This was associated with an enhancement in Ca²âº signalling and inhibition of cAMP elevation. Time-course genome-wide expression analysis in HAoECs undergoing directional migration allowed identifying dynamic co-regulation of genes involved in extracellular matrix-receptor interaction, vesicle-mediated trafficking, and metal sensing - which have all well-established influences on EC motility -, without affecting the balance between pro- and anticoagulant genes. CONCLUSION: Our work broadens the knowledge on peripherally expressed MC1R. These results indicate that the receptor is constitutively expressed by arterial ECs and provide evidence of a novel homeostatic function for MC1R, whose activation may participate in preventing/healing endothelial dysfunction or denudation in macrovascular arteries.

DaMiRseq-an R/Bioconductor package for data mining of RNA-Seq data: normalization, feature selection and classification.

Summary: RNA-Seq is becoming the technique of choice for high-throughput transcriptome profiling, which, besides class comparison for differential expression, promises to be an effective and powerful tool for biomarker discovery. However, a systematic analysis of high-dimensional genomic data is a demanding task for such a purpose. DaMiRseq offers an organized, flexible and convenient framework to remove noise and bias, select the most informative features and perform accurate classification. Availability and implementation: DaMiRseq is developed for the R environment (R ≥ 3.4) and is released under GPL (≥2) License. The package runs on Windows, Linux and Macintosh operating systems and is freely available to non-commercial users at the Bioconductor open-source, open-development software project repository (https://bioconductor.org/packages/DaMiRseq/). In compliance with Bioconductor standards, the authors ensure stable package maintenance through software and documentation updates. Contact: luca.piacentini@ccfm.it. Supplementary information: Supplementary data are available at Bioinformatics online.

Direct screening of plasma circulating microRNAs.

Circulating microRNAs (miRNAs) are considered as reliable candidates for biomarker discovery. RNA-Sequencing has become the most suitable technique to accurately quantify the miRNAome. However, RNA-Sequencing relies on several technical passages before reaching the final-end. HTG EdgeSeq technology, thanks to the abrogation of RNA extraction step, allows productivity enhancement by reducing the number of hands-on steps, the time for sample preparation and, therefore, the costs. We found a strong correlation between qPCR and dPCR with HTG (Pearson's coefficient of 0.93 and 0.94, respectively). In conclusion, we showed that HTG EdgeSeq, performed on human plasma specimens without RNA extraction, is reliable, allows the simultaneous screening of more than 2,000 miRNAs, and thus, it could be applied to biomarker discovery in large cohorts.

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.

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.

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.