HB-EGF Plasmatic Level Contributes to the Development of Early Risk Prediction Nomogram for Severe COVID-19 Cases.

(1) Background: Heparin-Binding Epidermal Growth Factor-like Growth Factor (HB-EGF) is involved in wound healing, cardiac hypertrophy, and heart development processes. Recently, circulant HB-EGF was reported upregulated in severely hospitalized COVID-19 patients. However, the clinical correlations of HB-EGF plasma levels with COVID-19 patients' characteristics have not been defined yet. In this study, we assessed the plasma HB-EGF correlations with the clinical and paraclinical patients' data, evaluated its predictive clinical value, and built a risk prediction model for severe COVID-19 cases based on the resulting significant prognostic markers. (2) Methods: Our retrospective study enrolled 75 COVID-19 patients and 17 control cases from May 2020 to September 2020. We quantified plasma HB-EGF levels using the sandwich ELISA technique. Correlations between HB-EGF plasma levels with clinical and paraclinical patients' data were calculated using two-tailed Spearman and Point-Biserial tests. Significantly upregulated parameters for severe COVID-19 cases were identified and selected to build a multivariate logistic regression prediction model. The clinical significance of the prediction model was assessed by risk prediction nomogram and decision curve analyses. (3) Results: HB-EGF plasma levels were significantly higher in the severe COVID-19 subgroup compared to the controls (p = 0.004) and moderate cases (p = 0.037). In the severe COVID-19 group, HB-EGF correlated with age (p = 0.028), pulse (p = 0.016), dyspnea (p = 0.014) and prothrombin time (PT) (p = 0.04). The multivariate risk prediction model built on seven identified risk parameters (age p = 0.043, HB-EGF p = 0.0374, Fibrinogen p = 0.009, PT p = 0.008, Creatinine p = 0.026, D-Dimers p = 0.024 and delta miR-195 p < 0.0001) identifies severe COVID-19 with AUC = 0.9556 (p < 0.0001). The decision curve analysis revealed that the nomogram model is clinically relevant throughout a wide threshold probability range. (4) Conclusions: Upregulated HB-EGF plasma levels might serve as a prognostic factor for severe COVID-19 and help build a reliable risk prediction nomogram that improves the identification of high-risk patients at an early stage of COVID-19.

Plasma miR-19b, miR-34a and miR-146a expression in patients with type 2 diabetes mellitus and cataract: A pilot study.

Cataract is among the most common ocular complications in diabetes mellitus (DM). While microRNA (miRNA) dysregulations in DM have been previously reported, consensus is still lacking concerning miRNA expression in cataract. Furthermore, the miRNA profile in diabetic cataract patients remains largely unexplored, and data on plasma expression levels are limited. Our study aimed to assess the plasma levels of three distinct miRNA species (hsa-miR-19b, hsa-miR-34a, and hsa-miR-146a) implicated in the development of cataract and/or DM. We investigated the circulating miRNA expression in DM patients diagnosed with cataract, compared to a non-DM cataract group. We employed qRT-PCR for relative quantification experiments and subsequently conducted a correlation analysis between miRNA expression levels and clinical characteristics. Our findings reveal that hsa-miR-34a and hsa-miR-146a are differentially expressed in the two cohorts. However, no significant correlation was observed between the clinical variables and miRNA levels. In summary, our results suggest a potential role for hsa-miR-34a and hsa-miR-146a in the biology of diabetic cataract.

Clinical Variables Influence the Ability of miR-101, miR-150, and miR-21 to Predict Ventricular Remodeling after ST-Elevation Myocardial Infarction.

Left ventricle remodeling (LVR) after acute myocardial infarction (MI) leads to impairment of both systolic and diastolic function, a significant contributor to heart failure (HF). Despite extensive research in the field, predicting post-MI LVR and HF is still a challenge. Several circulant microRNAs have been proposed as LVR predictors; however, their clinical value is controversial. Here, we used real-time quantitative PCR to quantify the plasma levels of hsa-miR-101, hsa-miR-150, and hsa-miR-21 on the first day of hospital admission of MI patients with ST-elevation (STEMI). We analyzed their correlation to the patient's clinical and paraclinical variables and evaluated their ability to discriminate between post-MI LVR and non-LVR. We show that, despite being excellent MI discriminators, none of these microRNAs can distinguish between LVR and non-LVR patients. Furthermore, we found that diabetes mellitus (DM), Hb level, and the number of erythrocytes significantly influence all three plasma microRNA levels. This suggests that plasma microRNAs' diagnostic and prognostic value in STEMI patients should be reevaluated and interpreted in the context of associated pathologies.

Plasma hsa-miR-22-3p Might Serve as an Early Predictor of Ventricular Function Recovery after ST-Elevation Acute Myocardial Infarction.

Left ventricle remodeling (LVR) after acute myocardial infarction (aMI) leads to impairment of both systolic and diastolic function, a major contributor to heart failure (HF). Despite extensive research, predicting post-aMI LVR and HF is still a challenge. Several circulant microRNAs have been proposed as LVR predictors; however, their clinical value is controversial. Here, we used real-time quantitative polymerase chain reaction (qRT-PCR) to quantify hsa-miR-22-3p (miR-22) plasma levels on the first day of hospital admission of ST-elevation aMI (STEMI) patients. We analyzed miR-22 correlation to the patients' clinical and paraclinical variables and evaluated its ability to discriminate between post-aMI LVR and non-LVR. We show that miR-22 is an excellent aMI discriminator and can distinguish between LVR and non-LVR patients. The discriminative performance of miR-22 significantly improves the predictive power of a multiple logistic regression model based on four continuous variables (baseline ejection fraction and end-diastolic volume, CK-MB, and troponin). Furthermore, we found that diabetes mellitus, hematocrit level, and the number of erythrocytes significantly influence its levels. These data suggest that miR-22 might be used as a predictor of ventricular function recovery in STEMI patients.

Plasma miR-195-5p predicts the severity of Covid-19 in hospitalized patients.

Predicting the clinical course of Covid-19 is a challenging task, given the multi-systemic character of the disease and the paucity of minimally invasive biomarkers of disease severity. Here, we evaluated the early (first two days post-admission) level of circulating hsa-miR-195-5p (miR-195, a known responder to viral infections and SARS-CoV-2 interactor) in Covid-19 patients and assessed its potential as a biomarker of disease severity. We show that plasma miR-195 correlates with several clinical and paraclinical parameters, and is an excellent discriminator between the severe and mild forms of the disease. Our Gene Ontology analysis of miR-195 targets differentially expressed in Covid-19 indicates a strong impact on cardiac mitochondria homeostasis, suggesting a possible role in long Covid and chronic fatigue syndrome (CFS) syndromes.

Identification of Genomic Variants of SARS-CoV-2 Using Nanopore Sequencing.

Background and Objectives: SARS-CoV-2 is the first global threat and life-changing event of the twenty-first century. Although efficient treatments and vaccines have been developed, due to the virus's ability to mutate in key regions of the genome, whole viral genome sequencing is needed for efficient monitoring, evaluation of the spread, and even the adjustment of the molecular diagnostic assays. Materials and Methods: In this study, Nanopore and Ion Torrent sequencing technologies were used to detect the main SARS-CoV-2 circulating strains in Timis County, Romania, between February 2021 and May 2022. Results: We identified 22 virus lineages belonging to seven clades: 20A, 20I (Alpha, V1), 21B (Kappa), 21I (Delta), 21J (Delta), 21K (Omicron), and 21L (Omicron). Conclusions: Results obtained with both methods are comparable, and we confirm the utility of Nanopore sequencing in large-scale epidemiological surveillance due to the lower cost and reduced time for library preparation.

Gene Network Analysis of the Transcriptome Impact of SARS-CoV-2 Interacting MicroRNAs in COVID-19 Disease.

According to the World Health Organization (WHO), as of June 2022, over 536 million confirmed COVID-19 disease cases and over 6.3 million deaths had been globally reported. COVID-19 is a multiorgan disease involving multiple intricated pathological mechanisms translated into clinical, biochemical, and molecular changes, including microRNAs. MicroRNAs are essential post-transcriptional regulators of gene expression, being involved in the modulation of most biological processes. In this study, we characterized the biological impact of SARS-CoV-2 interacting microRNAs differentially expressed in COVID-19 disease by analyzing their impact on five distinct tissue transcriptomes. To this end, we identified the microRNAs' predicted targets within the list of differentially expressed genes (DEGs) in tissues affected by high loads of SARS-CoV-2 virus. Next, we submitted the tissue-specific lists of the predicted microRNA-targeted DEGs to gene network functional enrichment analysis. Our data show that the upregulated microRNAs control processes such as mitochondrial respiration and cytokine and cell surface receptor signaling pathways in the heart, lymph node, and kidneys. In contrast, downregulated microRNAs are primarily involved in processes related to the mitotic cell cycle in the heart, lung, and kidneys. Our study provides the first exploratory, systematic look into the biological impact of the microRNAs associated with COVID-19, providing a new perspective for understanding its multiorgan physiopathology.

Plasma hsa-mir-19b is a potential LevoDopa therapy marker.

Parkinson's disease (PD) is the second most common neurodegenerative disorder among the elderly, the diagnostic and prognostic of which is based mostly on clinical signs. LevoDopa replacement is the gold standard therapy for PD, as it ameliorates the motor symptoms. However, it does not affect the progression of the disease and its long-term use triggers severe complications. There are no bona fide biomarkers for monitoring the patients' response to LevoDopa and predicting the efficacy of levodopa treatment. Here, we have combined qPCR microRNA array screening with analysis of validated miRs in naïve versus Levodopa-treated PD patients. We have identified plasma miR-19b as a possible biomarker for LevoDopa therapy and validated this result in human differentiated dopaminergic neurons exposed to LevoDopa. In silico analysis suggests that the LevoDopa-induced miR-19b regulates ubiquitin-mediated proteolysis.

Uncovering New Drug Properties in Target-Based Drug-Drug Similarity Networks.

Despite recent advances in bioinformatics, systems biology, and machine learning, the accurate prediction of drug properties remains an open problem. Indeed, because the biological environment is a complex system, the traditional approach-based on knowledge about the chemical structures-can not fully explain the nature of interactions between drugs and biological targets. Consequently, in this paper, we propose an unsupervised machine learning approach that uses the information we know about drug-target interactions to infer drug properties. To this end, we define drug similarity based on drug-target interactions and build a weighted Drug-Drug Similarity Network according to the drug-drug similarity relationships. Using an energy-model network layout, we generate drug communities associated with specific, dominant drug properties. DrugBank confirms the properties of 59.52% of the drugs in these communities, and 26.98% are existing drug repositioning hints we reconstruct with our DDSN approach. The remaining 13.49% of the drugs seem not to match the dominant pharmacologic property; thus, we consider them potential drug repurposing hints. The resources required to test all these repurposing hints are considerable. Therefore we introduce a mechanism of prioritization based on the betweenness/degree node centrality. Using betweenness/degree as an indicator of drug repurposing potential, we select Azelaic acid and Meprobamate as a possible antineoplastic and antifungal, respectively. Finally, we use a test procedure based on molecular docking to analyze Azelaic acid and Meprobamate's repurposing.

Role of carotenoids and retinoids during heart development.

The nutritional requirements of the developing embryo are complex. In the case of dietary vitamin A (retinol, retinyl esters and provitamin A carotenoids), maternal derived nutrients serve as precursors to signaling molecules such as retinoic acid, which is required for embryonic patterning and organogenesis. Despite variations in the composition and levels of maternal vitamin A, embryonic tissues need to generate a precise amount of retinoic acid to avoid congenital malformations. Here, we summarize recent findings regarding the role and metabolism of vitamin A during heart development and we survey the association of genes known to affect retinoid metabolism or signaling with various inherited disorders. A better understanding of the roles of vitamin A in the heart and of the factors that affect retinoid metabolism and signaling can help design strategies to meet nutritional needs and to prevent birth defects and disorders associated with altered retinoid metabolism. This article is part of a Special Issue entitled Carotenoids recent advances in cell and molecular biology edited by Johannes von Lintig and Loredana Quadro.

Nanoscale Delivery Systems: Actual and Potential Applications in the Natural Products Industry.

BACKGROUND: Compounds and extracts derived from natural sources continue to stand in the spotlight of drug design owing to their versatile interaction with enzymes, receptors and metabolic pathways. Nanomedicine offers an operative tool for the efficient delivery of natural products, in terms of increased bioavailability, targeting, and controlled release while protecting active constituents against physico-chemical alterations. The interest of the scientific community in the field of nanosized delivery of natural compounds is demonstrated by the exponential growth of the publications in this field. AIM: Beyond the presentation of successful examples of nanoscale delivery systems containing natural products, the scope of this review is to point out the yet underexplored capacities of this field with relevance for the pharmaceutical and nutraceutical market. REVIEW: Departing from a short presentation of plant-derived natural products and strategies to obtain nanoformulations, the current work discusses nanoparticulate drug delivery systems targeting diseases of various organs and systems: skin, central nervous system, skeletal tissue, cardiovascular apparatus, and diabetes. CONCLUSIONS: While notable progress has been achieved in the preparation of nanomedicines containing selected dietary polyphenols, works dealing with crude extracts or standardized fractions are much less frequent. In fact, most of the plants with solidly documented therapeutic properties and registered in pharmacopoeias still wait to benefit from advances in the field of nanotechnology. At least for some of them, adequate nanoformulation shall contribute to their removal from the group of dietary supplements and pharmaceutical preparations with suboptimal bioavailability and efficacy.