Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure

Understanding ayahuasca effects in major depressive disorder treatment through in vitro metabolomics and bioinformatics

Emerging insights from metabolomic-based studies of major depression disorder (MDD) are mainly related to biochemical processes such as energy or oxidative stress, in addition to neurotransmission linked to specific metabolite intermediates. Hub metabolites represent nodes in the biochemical network playing a critical role in integrating the information flow in cells between metabolism and signaling pathways. Limited technical-scientific studies have been conducted to understand the effects of ayahuasca (Aya) administration in the metabolism considering MDD molecular context. Therefore, this work aims to investigate an in vitro primary astrocyte model by untargeted metabolomics of two cellular subfractions: secretome and intracellular content after pre-defined Aya treatments, based on DMT concentration. Mass spectrometry (MS)-based metabolomics data revealed significant hub metabolites, which were used to predict biochemical pathway alterations. Branched-chain amino acid (BCAA) metabolism, and vitamin B6 and B3 metabolism were associated to Aya treatment, as "housekeeping" pathways. Dopamine synthesis was overrepresented in the network results when considering the lowest tested DMT concentration (1 µmol L^-1). Building reaction networks containing significant and differential metabolites, such as nicotinamide, L-DOPA, and L-leucine, is a useful approach to guide on dose decision and pathway selection in further analytical and molecular studies.

Artificial Intelligence and Cardiovascular Risk Prediction: All That Glitters is not Gold

Artificial intelligence (AI) is a broad term referring to any automated systems that need 'intelligence' to carry out specific tasks. During the last decade, AI-based techniques have been gaining popularity in a vast range of biomedical fields, including the cardiovascular setting. Indeed, the dissemination of cardiovascular risk factors and the better prognosis of patients experiencing cardiovascular events resulted in an increase in the prevalence of cardiovascular disease (CVD), eliciting the need for precise identification of patients at increased risk for development and progression of CVD. AI-based predictive models may overcome some of the limitations that hinder the performance of classic regression models. Nonetheless, the successful application of AI in this field requires knowledge of the potential pitfalls of the AI techniques, to guarantee their safe and effective use in daily clinical practice. The aim of the present review is to summarise the pros and cons of different AI methods and their potential application in the cardiovascular field, with a focus on the development of predictive models and risk assessment tools.

Probiotic-mediated p38 MAPK immune signaling prolongs the survival of Caenorhabditis elegans exposed to pathogenic bacteria

The host-microbiota cross-talk represents an important factor contributing to innate immune response and host resistance during infection. It has been shown that probiotic lactobacilli exhibit the ability to modulate innate immunity and enhance pathogen elimination. Here we showed that heat-inactivated probiotic strain Lactobacillus curvatus BGMK2-41 stimulates immune response and resistance of the Caenorhabditis elegans against Staphylococcus aureus and Pseudomonas aeruginosa. By employing qRT-PCR and western blot analysis we showed that heat-inactivated BGMK2-41 activated PMK-1/p38 MAPK immunity pathway which prolongs the survival of C. elegans exposed to pathogenic bacteria in nematode killing assays. The C. elegans pmk-1 mutant was used to demonstrate a mechanistic basis for the antimicrobial potential of BGMK2-41, showing that BGMK2-41 upregulated PMK-1/p38 MAPK dependent transcription of C-type lectins, lysozymes and tight junction protein CLC-1. Overall, this study suggests that PMK-1/p38 MAPK-dependent immune regulation by BGMK2-41 is essential for probiotic-mediated C. elegans protection against gram-positive and gram-negative bacteria and could be further explored for development of probiotics with the potential to increase resistance of the host towards pathogens.

Metabolomic Profiling of Adults with Congenital Heart Disease

Metabolomic analysis may provide an integrated assessment in genetically and pathologically heterogeneous populations. We used metabolomic analysis to gain mechanistic insight into the small and diverse population of adults with congenital heart disease (ACHD). Consecutive ACHD patients seen at a single institution were enrolled. Clinical variables and whole blood were collected at regular clinical visits. Stored plasma samples were analyzed for the concentrations of 674 metabolites and metabolic markers using mass spectrometry with internal standards. These samples were compared to 28 simultaneously assessed healthy non-ACHD controls. Principal component analysis and multivariable regression modeling were used to identify metabolites associated with clinical outcomes in ACHD. Plasma from ACHD and healthy control patients differed in the concentrations of multiple metabolites. Differences between control and ACHD were greater in number and in degree than those between ACHD anatomic groups. A metabolite cluster containing amino acids and metabolites of amino acids correlated with negative clinical outcomes across all anatomic groups. Metabolites in the arginine metabolic pathway, betaine, dehydroepiandrosterone, cystine, 1-methylhistidine, serotonin and bile acids were associated with specific clinical outcomes. Metabolic markers of disease may both be useful as biomarkers for disease activity and suggest etiologically related pathways as possible targets for disease-modifying intervention.

Application of machine learning in understanding atherosclerosis: Emerging insights

Biological processes are incredibly complex—integrating molecular signaling networks involved in multicellular communication and function, thus maintaining homeostasis. Dysfunction of these processes can result in the disruption of homeostasis, leading to the development of several disease processes including atherosclerosis. We have significantly advanced our understanding of bioprocesses in atherosclerosis, and in doing so, we are beginning to appreciate the complexities, intricacies, and heterogeneity atherosclerosi. We are also now better equipped to acquire, store, and process the vast amount of biological data needed to shed light on the biological circuitry involved. Such data can be analyzed within machine learning frameworks to better tease out such complex relationships. Indeed, there has been an increasing number of studies applying machine learning methods for patient risk stratification based on comorbidities, multi-modality image processing, and biomarker discovery pertaining to atherosclerotic plaque formation. Here, we focus on current applications of machine learning to provide insight into atherosclerotic plaque formation and better understand atherosclerotic plaque progression in patients with cardiovascular disease.

Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure

Machine learning and artificial intelligence are generating significant attention in the scientific community and media. Such algorithms have great potential in medicine for personalizing and improving patient care, including in the diagnosis and management of heart failure. Many physicians are familiar with these terms and the excitement surrounding them, but many are unfamiliar with the basics of these algorithms and how they are applied to medicine. Within heart failure research, current applications of machine learning include creating new approaches to diagnosis, classifying patients into novel phenotypic groups, and improving prediction capabilities. In this paper, we provide an overview of machine learning targeted for the practicing clinician and evaluate current applications of machine learning in the diagnosis, classification, and prediction of heart failure.

Collaborative AI and Laboratory Medicine integration in precision cardiovascular medicine

Artificial Intelligence (AI) is a broad term that combines computation with sophisticated mathematical models and in turn allows the development of complex algorithms which are capable to simulate human intelligence such as problem solving and learning. It is devised to promote a significant paradigm shift in the most diverse areas of medical knowledge. On the other hand, Cardiology is a vast field dealing with diseases relating to the heart, the circulatory system, and includes coronary heart disease, cerebrovascular disease, rheumatic heart disease and other conditions. AI has emerged as a promising tool in cardiovascular medicine which is aimed in augmenting the effectiveness of the cardiologist and to extend better quality to patients. It has the ability to support decision‑making and improve diagnostic and prognostic performance. Attempt has also been made to explore novel genotypes and phenotypes in existing cardiovascular diseases, improve the quality of patient care, to enablecost-effectiveness with reducereadmissionand mortality rates. Our review addresses the integration of AI and laboratory medicine as an accelerator of personalization care associated with the precision and the need of value creation services in cardiovascular medicine.