Design and use of a Denoising Convolutional Autoencoder for reconstructing electrocardiogram signals at super resolution.

Electrocardiogram signals play a pivotal role in cardiovascular diagnostics, providing essential information on electrical hearth activity. However, inherent noise and limited resolution can hinder an accurate interpretation of the recordings. In this paper an advanced Denoising Convolutional Autoencoder designed to process electrocardiogram signals, generating super-resolution reconstructions is proposed; this is followed by in-depth analysis of the enhanced signals.

Elevated whole blood viscosity is associated with an impaired insulin-stimulated myocardial glucose metabolism.

Background: Increased whole blood viscosity (WBV) was associated with impaired peripheral glucose metabolism, type 2 diabetes, and cardiovascular disease (CVD). Impaired myocardial glucose metabolism is a risk factor for CVD. Whether an increased WBV is associated with impaired myocardial glucose metabolism is still undefined.

AI-enabled pipeline for virus detection, validation, and SNP discovery from next-generation sequencing data.

Background And Aims: The rapid and accurate detection of viruses and the discovery of single nucleotide polymorphisms (SNPs) are critical for disease management and understanding viral evolution. This study presents a pipeline for virus detection, validation, and SNP discovery from next-generation sequencing (NGS) data. The pipeline processes raw sequencing data to identify viral sequences with high accuracy and sensitivity by integrating state-of-the-art bioinformatics tools with artificial intelligence.

Non parametric differential network analysis: a tool for unveiling specific molecular signatures.

Background: The rewiring of molecular interactions in various conditions leads to distinct phenotypic outcomes. Differential network analysis (DINA) is dedicated to exploring these rewirings within gene and protein networks. Leveraging statistical learning and graph theory, DINA algorithms scrutinize alterations in interaction patterns derived from experimental data.

Understanding complex systems through differential causal networks.

In the evolving landscape of data science and computational biology, Causal Networks (CNs) have emerged as a robust framework for modelling causal relationships among elements of complex systems derived from experimental data. CNs can efficiently model causal relationships emerging in a single system while comparing multiple systems, allowing to understand rewiring in different cells, tissues, and physiological states with a deeper perspective. Despite the existence of network models, namely differential networks, that have been used to compare coexpression and correlation structures, causality needs to be introduced in differential analysis to robustly provide direction to the edges of such networks, in order to better understand the flows of information, and also to better intervene in their functioning, for example for agricultural or pharmacological purposes.

A Methodology to Measure Glucose Metabolism by Quantitative Analysis of PET Images.

Positron emission tomography (PET) with F-18 fluorodeoxyglucose (FDG) tracer is the standard clinical technique to measure myocardial and vessel metabolism and viability and to investigate the metabolic syndrome associated with cardiovascular diseases. The quantitative analysis of PET images allows one to study the cardiovascular physiological processes, by extracting quantitative parameters from the analysis of the tracer kinetic. Here, we propose a new methodology to quantify and evaluate the evolution of glucose metabolism inside the myocardium and the large vascular structures over time.

Identification of key genes and pathways involved in T-DM1-resistance in OE-19 esophageal cancer cells through bioinformatics analysis.

Introduction: Esophageal Cancer (EC) ranks among the most common malignancies worldwide. Most EC patients acquire drug resistance to chemotherapy either intrinsically or acquired after T-DM1 treatment, which shows that increasing or decreasing the expression of particular genes might influence chemotherapeutic sensitivity or resistance. Therefore, gaining a deeper understanding of the altered expression of genes involved in EC drug resistance and developing new therapeutic methods are essential targets for continued advancement in EC therapy.

Bridging the Gap: A Comparative Analysis of Healthcare Quality Perceptions Between the Older People and Healthcare Providers.

In our study aimed at improving the healthcare system for the aging population, we compared healthcare quality evaluations between 96 older individuals and 30 healthcare providers in Split-Dalmatia County (Croatia). Using nonparametric analyses such as the Mann-Whitney and Wilcoxon tests on Likert scale questionnaire scores, we found most participants highlighted challenges such as healthcare affordability, long wait times, reliance on private care, poor public transportation, and insufficient rural healthcare services. Healthcare providers rated these quality indicators even more negatively.

Leveraging graph neural networks for supporting automatic triage of patients.

Patient triage is crucial in emergency departments, ensuring timely and appropriate care based on correctly evaluating the emergency grade of patient conditions. Triage methods are generally performed by human operator based on her own experience and information that are gathered from the patient management process. Thus, it is a process that can generate errors in emergency-level associations.

Sex-specific differences in myocardial glucose metabolic rate in non-diabetic, pre-diabetic and type 2 diabetic subjects.

Background: Evidence has shown that women with type 2 diabetes (T2DM) have a higher excess risk for cardiovascular disease (CVD) than men with T2DM. Subjects with either T2DM or prediabetes exhibit myocardial insulin resistance, but it is still unsettled whether sex-related differences in myocardial insulin resistance occur in diabetic and prediabetic subjects.

Methods: We aimed to evaluate sex-related differences in myocardial glucose metabolic rate (MRGlu), assessed using dynamic PET with F-FDG combined with euglycemic-hyperinsulinemic clamp, in subjects with normal glucose tolerance (NGT; n = 20), prediabetes (n = 11), and T2DM (n = 26).

Machine learning pipeline to analyze clinical and proteomics data: experiences on a prostate cancer case.

Proteomic-based analysis is used to identify biomarkers in blood samples and tissues. Data produced by devices such as mass spectrometry requires platforms to identify and quantify proteins (or peptides). Clinical information can be related to mass spectrometry data to identify diseases at an early stage.

The Omicron XBB.1 Variant and Its Descendants: Genomic Mutations, Rapid Dissemination and Notable Characteristics.

The SARS-CoV-2 virus, which is a major threat to human health, has undergone many mutations during the replication process due to errors in the replication steps and modifications in the structure of viral proteins. The XBB variant was identified for the first time in Singapore in the fall of 2022. It was then detected in other countries, including the United States, Canada, and the United Kingdom.

REHABS: An Innovative and User-Friendly Device for Rehabilitation.

Rehabilitation is a complex set of interventions involving the assessment, management, and treatment of injuries. It aims to support and facilitate an individual's recovery process by restoring a physiological function, e.g.

Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variables.

Background: Prostate Cancer (PCa) represents the second leading cause of cancer-related death in men. Prostate-specific antigen (PSA) serum testing, currently used for PCa screening, lacks the necessary sensitivity and specificity. New non-invasive diagnostic tools able to discriminate tumoral from benign conditions and aggressive (AG-PCa) from indolent forms of PCa (NAG-PCa) are required to avoid unnecessary biopsies.

Adjunctive use of oral MAF is associated with no disease progression or mortality in hospitalized patients with COVID-19 pneumonia: The single-arm COral-MAF1 prospective trial.

Based on a growing body of evidence that a dysregulated innate immune response mediated by monocytes/macrophages plays a key role in the pathogenesis of COVID-19, a clinical trial was conducted to investigate the therapeutic potential and safety of oral macrophage activating factor (MAF) plus standard of care (SoC) in the treatment of hospitalized patients with COVID-19 pneumonia. Ninety-seven hospitalized patients with confirmed COVID-19 pneumonia were treated with oral MAF and a vitamin D3 supplement, in combination with SoC, in a single-arm, open label, multicentre, phase II clinical trial. The primary outcome measure was a reduction in an intensive care unit transfer rate below 13% after MAF administration.

Blood concentrations of anterior pituitary hormones in drug-naïve people with first-episode psychosis: A systematic review and meta-analysis.

Introduction: The role of anterior pituitary hormones - i.e., adrenocorticotropic hormone (ACTH), luteinizing and follicle stimulating hormones (LH and FSH), growth hormone (GH), prolactin (PRL), and thyroid-stimulating hormone (TSH) - in early schizophrenia and psychoses unclear.

Strategies and Trends in COVID-19 Vaccination Delivery: What We Learn and What We May Use for the Future.

Vaccination has been the most effective way to control the outbreak of the COVID-19 pandemic. The numbers and types of vaccines have reached considerable proportions, even if the question of vaccine procedures and frequency still needs to be resolved. We have come to learn the necessity of defining vaccination distribution strategies with regard to COVID-19 that could be used for any future pandemics of similar gravity.

Aligning Cross-Species Interactomes for Studying Complex and Chronic Diseases.

Neurodegenerative diseases (NDs) are a group of complex disorders characterized by the progressive degeneration and dysfunction of neurons in the central nervous system. NDs encompass many conditions, including Alzheimer's disease and Parkinson's disease. Alzheimer's disease (AD) is a complex disease affecting almost forty million people worldwide.

A systems biology approach to pathogenesis of gastric cancer: gene network modeling and pathway analysis.

Background: Gastric cancer (GC) ranks among the most common malignancies worldwide. This study aimed to find critical genes/pathways in GC pathogenesis.

Methods: Gene interactions were analyzed, and the protein-protein interaction network was drawn.

SARS-CoV-2 protein structure and sequence mutations: Evolutionary analysis and effects on virus variants.

The structure and sequence of proteins strongly influence their biological functions. New models and algorithms can help researchers in understanding how the evolution of sequences and structures is related to changes in functions. Recently, studies of SARS-CoV-2 Spike (S) protein structures have been performed to predict binding receptors and infection activity in COVID-19, hence the scientific interest in the effects of virus mutations due to sequence, structure and vaccination arises.