UNASSIGNED: Coronaviruses (CoVs) belong to the RNA viruses family. The viruses in this family are known to cause mild respiratory disease in humans. The origin of the novel SARS-COV2 virus that caused the coronavirus-19 disease (COVID-19) is the Wuhan city in China from where it disseminated to cause a global pandemic. Although lungs are the predominant target organ for Coronavirus Disease-19 (COVID-19), since its outbreak, the disease is known to affect heart, blood vessels, kidney, intestine, liver and brain. This review aimed to summarize the catastrophic impacts of Coronavirus disease-19 on heart and liver along with its mechanisms of pathogenesis.
UNASSIGNED: The information used in this review was obtained from relevant articles published on PubMed, Google Scholar, Google, WHO website, CDC and other sources. Key searching statements and phrases related to COVID-19 were used to retrieve information. Original research articles, review papers, research letters and case reports were used as a source of information.
UNASSIGNED: Besides causing severe lung injury, COVID-19 has also been reported to affect and cause dysfunction of many other organs. COVID-19 infection can affect people by downregulating membrane-bound active angiotensin-converting enzyme (ACE). People who have deficient ACE2 expression are more vulnerable to COVID-19 infection. The patients\' pre-existing co-morbidities are major risk factors that predispose individuals to severe COVID-19.
UNASSIGNED: The disease severity and its broad spectrum phenotype is a result of combined direct and indirect pathogenic factors. Therefore, protocols that harmonize many therapeutic preferences should be the best alternatives to de-escalate the disease and obviate deaths caused as a result of multiple organ damage and dysfunction induced by the disease.

Physiological determinants of drug dosing (PDODD) are a promising approach for precision dosing. This study investigates the alterations of PDODD in diseases and evaluates a variational autoencoder (VAE) artificial intelligence model for PDODD. The PDODD panel contained 20 biomarkers, and 13 renal, hepatic, diabetes, and cardiac disease status variables. Demographic characteristics, anthropometric measurements (body weight, body surface area, waist circumference), blood (plasma volume, albumin), renal (creatinine, glomerular filtration rate, urine flow, and urine albumin to creatinine ratio), and hepatic (R-value, hepatic steatosis index, drug-induced liver injury index), blood cell (systemic inflammation index, red cell, lymphocyte, neutrophils, and platelet counts) biomarkers, and medical questionnaire responses from the National Health and Nutrition Examination Survey (NHANES) were included. The tabular VAE (TVAE) generative model was implemented with the Synthetic Data Vault Python library. The joint distributions of the generated data vs. test data were compared using graphical univariate, bivariate, and multidimensional projection methods and distribution proximity measures. The PDODD biomarkers related to disease progression were altered as expected in renal, hepatic, diabetes, and cardiac diseases. The continuous PDODD panel variables generated by the TVAE satisfactorily approximated the distribution in the test data. The TVAE-generated distributions of some discrete variables deviated from the test data distribution. The age distribution of TVAE-generated continuous variables was similar to the test data. The TVAE algorithm demonstrated potential as an AI model for continuous PDODD and could be useful for generating virtual populations for clinical trial simulations.

Biological sex is an important modifier of physiology and influences pathobiology in many diseases. While heart disease is the number one cause of death worldwide in both men and women, sex differences exist at the organ and cellular scales, affecting clinical presentation, diagnosis, and treatment. In this Review, we highlight baseline sex differences in cardiac structure, function, and cellular signaling and discuss the contribution of sex hormones and chromosomes to these characteristics. The heart is a remarkably plastic organ and rapidly responds to physiological and pathological cues by modifying form and function. The nature and extent of cardiac remodeling in response to these stimuli are often dependent on biological sex. We discuss organ- and molecular-level sex differences in adaptive physiological remodeling and pathological cardiac remodeling from pressure and volume overload, ischemia, and genetic heart disease. Finally, we offer a perspective on key future directions for research into cardiac sex differences.

Hepatocellular carcinoma (HCC) stands as a significant contributor to cancer-related mortality globally. While the acute and often fatal manifestations of locally advanced HCC primarily present within the abdomen, it is crucial to recognize that the respiratory and circulatory systems can also fall victim due to the liver\'s unique anatomical position within the body. Here, we present the case of a 63-year-old male recently diagnosed with locally advanced HCC with vascular invasion. Shortly after receiving target therapy and focal radiotherapy, the patient developed repeated secondary infections and a persistent diaphragmatic defect. As the necrotic tissue invaded the pleural space, subsequent tumor-to-bronchial and tumor-to-cardiac fistulas emerged, resulting in an abnormal connection between the respiratory and cardiovascular systems, leading to massive air emboli in circulation. This report highlights the risk of supradiaphragmatic complications in HCC patients with post-treatment secondary infections, particularly in patients predisposed to developing diaphragmatic defects.

Heterozygous de novo mutations in the Activity-Dependent Neuroprotective Homeobox (ADNP) gene underlie Helsmoortel-Van der Aa syndrome (HVDAS). Most of these mutations are situated in the last exon and we previously demonstrated escape from nonsense-mediated decay by detecting mutant ADNP mRNA in patient blood. In this study, wild-type and ADNP mutants are investigated at the protein level and therefore optimal detection of the protein is required. Detection of ADNP by means of western blotting has been ambiguous with reported antibodies resulting in non-specific bands without unique ADNP signal. Validation of an N-terminal ADNP antibody (Aviva Systems) using a blocking peptide competition assay allowed to differentiate between specific and non-specific signals in different sample materials, resulting in a unique band signal around 150 kDa for ADNP, above its theoretical molecular weight of 124 kDa. Detection with different C-terminal antibodies confirmed the signals at an observed molecular weight of 150 kDa. Our antibody panel was subsequently tested by immunoblotting, comparing parental and homozygous CRISPR/Cas9 endonuclease-mediated Adnp knockout cell lines and showed disappearance of the 150 kDa signal, indicative for intact ADNP. By means of both a GFPSpark and Flag-tag N-terminally fused to a human ADNP expression vector, we detected wild-type ADNP together with mutant forms after introduction of patient mutations in E. coli expression systems by site-directed mutagenesis. Furthermore, we were also able to visualize endogenous ADNP with our C-terminal antibody panel in heterozygous cell lines carrying ADNP patient mutations, while the truncated ADNP mutants could only be detected with epitope-tag-specific antibodies, suggesting that addition of an epitope-tag possibly helps stabilizing the protein. However, western blotting of patient-derived hiPSCs, immortalized lymphoblastoid cell lines and post-mortem patient brain material failed to detect a native mutant ADNP protein. In addition, an N-terminal immunoprecipitation-competent ADNP antibody enriched truncating mutants in overexpression lysates, whereas implementation of the same method failed to enrich a possible native mutant protein in immortalized patient-derived lymphoblastoid cell lines. This study aims to shape awareness for critical assessment of mutant ADNP protein analysis in Helsmoortel-Van der Aa syndrome.

Heart disease is the world\'s leading cause of death. Diagnostic models based on electrocardiograms (ECGs) are often limited by the scarcity of high-quality data and issues of data imbalance. To address these challenges, we propose a conditional generative adversarial network (CECG-GAN). This strategy enables the generation of samples that closely approximate the distribution of ECG data. Additionally, CECG-GAN addresses waveform jitter, slow processing speeds, and dataset imbalance issues through the integration of a transformer architecture. We evaluated this approach using two datasets: MIT-BIH and CSPC2020. The experimental results demonstrate that CECG-GAN achieves outstanding performance metrics. Notably, the percentage root mean square difference (PRD) reached 55.048, indicating a high degree of similarity between generated and actual ECG waveforms. Additionally, the Fréchet distance (FD) was approximately 1.139, the root mean square error (RMSE) registered at 0.232, and the mean absolute error (MAE) was recorded at 0.166.

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The \'CMRxRecon\' dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community.

Cardiopathy has become one of the predominant global causes of death. The timely identification of different types of heart diseases significantly diminishes mortality risk and enhances the efficacy of treatment. However, fast and efficient recognition necessitates continuous monitoring, encompassing not only specific clinical conditions but also diverse lifestyles. Consequently, an increasing number of studies are striving to automate and progress in the identification of different cardiopathies. Notably, the assessment of electrocardiograms (ECGs) is crucial, given that it serves as the initial diagnostic test for patients, proving to be both the simplest and the most cost-effective tool. This research employs a customized architecture of Convolutional Neural Network (CNN) to forecast heart diseases by analyzing the images of both three bands of electrodes and of each single electrode signal of the ECG derived from four distinct patient categories, representing three heart-related conditions as well as a spectrum of healthy controls. The analyses are conducted on a real dataset, providing noteworthy performance (recall greater than 80% for the majority of the considered diseases and sometimes even equal to 100%) as well as a certain degree of interpretability thanks to the understanding of the importance a band of electrodes or even a single ECG electrode can have in detecting a specific heart-related pathology.

Cardiovascular diseases (CVDs), particularly heart failure, are major contributors to early mortality globally. Heart failure poses a significant public health problem, with persistently poor long-term outcomes and an overall unsatisfactory prognosis for patients. Conventionally, treatments for heart failure have focused on lowering blood pressure; however, the development of more potent therapies targeting hemodynamic parameters presents challenges, including tolerability and safety risks, which could potentially restrict their clinical effectiveness. Adenosine has emerged as a key mediator in CVDs, acting as a retaliatory metabolite produced during cellular stress via ATP metabolism, and works as a signaling molecule regulating various physiological processes. Adenosine functions by interacting with different adenosine receptor (AR) subtypes expressed in cardiac cells, including A1AR, A2AAR, A2BAR, and A3AR. In addition to A1AR, A3AR has a multifaceted role in the cardiovascular system, since its activation contributes to reducing the damage to the heart in various pathological states, particularly ischemic heart disease, heart failure, and hypertension, although its role is not as well documented compared to other AR subtypes. Research on A3AR signaling has focused on identifying the intricate molecular mechanisms involved in CVDs through various pathways, including Gi or Gq protein-dependent signaling, ATP-sensitive potassium channels, MAPKs, and G protein-independent signaling. Several A3AR-specific agonists, such as piclidenoson and namodenoson, exert cardioprotective impacts during ischemia in the diverse animal models of heart disease. Thus, modulating A3ARs serves as a potential therapeutic approach, fueling considerable interest in developing compounds that target A3ARs as potential treatments for heart diseases.

During mammalian heart development, the clustered genes encoding peptide hormones, Natriuretic Peptide A (NPPA; ANP) and B (NPPB; BNP), are transcriptionally co-regulated and co-expressed predominately in the atrial and ventricular trabecular cardiomyocytes. After birth, expression of NPPA and a natural antisense transcript NPPA-AS1 becomes restricted to the atrial cardiomyocytes. Both NPPA and NPPB are induced by cardiac stress and serve as markers for cardiovascular dysfunction or injury. NPPB gene products are extensively used as diagnostic and prognostic biomarkers for various cardiovascular disorders. Membrane-localized guanylyl cyclase receptors on many cell types throughout the body mediate the signaling of the natriuretic peptide ligands through the generation of intracellular cGMP, which interacts with and modulates the activity of cGMP-activated kinase and other enzymes and ion channels. The natriuretic peptide system plays a fundamental role in cardio-renal homeostasis, and its potent diuretic and vasodilatory effects provide compensatory mechanisms in cardiac pathophysiological conditions and heart failure. In addition, both peptides, but also CNP, have important intracardiac actions during heart development and homeostasis independent of the systemic functions. Exploration of the intracardiac functions may provide new leads for the therapeutic utility of natriuretic peptide-mediated signaling in heart diseases and rhythm disorders. Here, we review recent insights into the regulation of expression and intracardiac functions of NPPA and NPPB during heart development, homeostasis, and disease.