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.

Cardiovascular diseases (CVDs) are characterized by abnormalities in the heart, blood vessels, and blood flow. CVDs comprise a diverse set of health issues. There are several types of CVDs like stroke, endothelial dysfunction, thrombosis, atherosclerosis, plaque instability and heart failure. Identification of a new drug for heart disease takes longer duration and its safety efficacy test takes even longer duration of research and approval. This chapter explores drug repurposing, nano-therapy, and plant-based treatments for managing CVDs from existing drugs which saves time and safety issues with testing new drugs. Existing drugs like statins, ACE inhibitor, warfarin, beta blockers, aspirin and metformin have been found to be useful in treating cardiac disease. For better drug delivery, nano therapy is opening new avenues for cardiac research by targeting interleukin (IL), TNF and other proteins by proteome interactome analysis. Nanoparticles enable precise delivery to atherosclerotic plaques, inflammation areas, and damaged cardiac tissues. Advancements in nano therapeutic agents, such as drug-eluting stents and drug-loaded nanoparticles are transforming CVDs management. Plant-based treatments, containing phytochemicals from Botanical sources, have potential cardiovascular benefits. These phytochemicals can mitigate risk factors associated with CVDs. The integration of these strategies opens new avenues for personalized, effective, and minimally invasive cardiovascular care. Altogether, traditional drugs, phytochemicals along with nanoparticles can revolutionize the future cardiac health care by identifying their signaling pathway, mechanism and interactome analysis.

Drosophila melanogaster has been used as a model system to identify and characterize genetic contributions to development, homeostasis, and to investigate the molecular determinants of numerous human diseases. While there exist many differences at the genetic, structural, and molecular level, many signalling components and cellular machineries are conserved between Drosophila and humans. For this reason, Drosophila can and has been used extensively to model, and study human pathologies. The extensive genetic resources available make this model system a powerful one. Over the years, the sophisticated and rapidly expanding Drosophila genetic toolkit has provided valuable novel insights into the contribution of genetic components to human diseases. The activity of Notch signalling is crucial during development and conserved across the Metazoa and has been associated with many human diseases. Here we highlight examples of mechanisms involving Notch signalling that have been elucidated from modelling human diseases in Drosophila melanogaster that include neurodegenerative diseases, congenital diseases, several cancers, and cardiac disorders.

Cardioembolism accounts globally for around 25% of ischemic strokes and is more often associated with higher rates of morbidity and mortality. Potential sources of cardioembolism into the intracranial circulation include paradoxic embolism, dysrhythmias, structural heart disease, and valvular heart disease. To identify the etiology of a patient\'s ischemic stroke, thorough investigation of the intracardiac structures, assessment of dysrhythmias, and consideration of high-risk events such as cardiac surgery are crucial. Treatment after cardioembolic stroke can be personalized based on the underlying cardioembolic source to minimize the risk of recurrent cerebral ischemic events.

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.

This report describes a male in his late 20s who presented with a 2-month history of recurrent haemoptysis and chest pain. A chronic infection, such as tuberculosis, was suspected. He had undergone surgical resection of an intrapericardial hydatid cyst in the past. His blood investigations showed peripheral eosinophilia, and his chest X-ray showed a cystic oval lesion in the left upper zone. A CT pulmonary angiogram revealed filling defects in the bilateral segmental and subsegmental arteries with a cystic lesion in the left upper lobe. Further workup, including bronchoalveolar lavage culture and MRI of the thorax, confirmed the diagnosis of a hydatid cyst of pulmonary echinococcosis. This case illustrates the presentation of multisystemic echinococcosis in a young male with no other risk factors, initially treated with surgical resection and antihelminthic therapy. The disease later recurred, which required prolonged medications, which brought the patient into remission.

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.