Hepcidin as a Biomarker of Cardiorenal Syndrome

A comprehensive review of acute cardio-renal syndrome: need for novel biomarkers

Cardiorenal syndrome represents a wide-spectrum disorder involving the heart and kidneys as the primary affected organs. India has an increasingly high burden of acute CRS, coinciding with the rise in global statistics. Up to 2022, approximately 46.1% of all cardiorenal patients have been diagnosed with acute CRS in India. Acute CRS involves a sudden deterioration of kidney functionalities, referred to as acute kidney injury (AKI) in acute heart failure patients. The pathophysiology of CRS involves hyperactivation of the sympathetic nervous system (SNS) and the renin-angiotensin-aldosterone system (RAAS) following acute myocardial stress. The pathological phenotype of acute CRS is associated with perturbed inflammatory, cellular, and neurohormonal markers in circulation. These complications increase the risk of mortality in clinically diagnosed acute CRS patients, making it a worldwide healthcare burden. Hence, effective diagnosis and early prevention are crucial to prevent the progression of CRS in AHF patients. Present biomarkers, such as serum creatinine (sCr), cystatin C (CysC), glomerular filtration rate (GFR), blood urea nitrogen (BUN), serum and/or urine neutrophil gelatinase-associated lipocalin (NGAL), B-type natriuretic peptide (BNP), and NT-proBNP, are clinically used to diagnose AKI stages in CRS patients but are limitedly sensitive to the early detection of the pathology. Therefore, the need for protein biomarkers is emerging for early intervention in CRS progression. Here, we summarized the cardio-renal nexus in acute CRS, with an emphasis on the present clinicopathological biomarkers and their limitations. The objective of this review is to highlight the need for novel proteomic biomarkers that will curb the burgeoning concern and direct future research trials.

Cardiorenal Syndrome: New Pathways and Novel Biomarkers

Cardiorenal syndrome (CRS) is a multi-organ disease characterized by the complex interaction between heart and kidney during acute or chronic injury. The pathogenesis of CRS involves metabolic, hemodynamic, neurohormonal, and inflammatory mechanisms, and atherosclerotic degeneration. In the process of better understanding the bi-directional pathophysiological aspects of CRS, the need to find precise and easy-to-use markers has also evolved. Based on the new pathophysiological standpoints and an overall vision of the CRS, the literature on renal, cardiac, metabolic, oxidative, and vascular circulating biomarkers was evaluated. Though the effectiveness of different extensively applied biomarkers remains controversial, evidence for several indicators, particularly when combined, has increased in recent years. From new aspects of classic biomarkers to microRNAs, this review aimed at a 360-degree analysis of the pathways that balance the kidney and the heart physiologies. In this delicate system, different markers and their combination can shed light on the diagnosis, risk, and prognosis of CRS.

Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning

In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network's behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.