Abstract: The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1- 5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.

Abstract: Essential oils of Citrus sinensis and Citrus latifolia have shown biological functions as antiseptics, anti-inflammatories, antioxidants, antifungal and antimutagenic, so the evaluation of their antibacterial capacity, by themselves or in combination with standard antibiotics, presents an alternative for infection treatment. Flow cytometry opens the door for the design of faster and more accurate measurement of antibacterial activity. We use a SYTO9/PI staining system on E. coli ATCC 25922 to determine antibacterial activity by counting live and dead cells through flow cytometry. We found that dual staining showed highly variable results due to wavelength overlapping and instead we used fluorochrome individual staining that highly correlated with viable counts. Chloramphenicol and cefotaxime treatments did not present a dose-response behavior, rendered diffuse readings and/or gave filament formation on fluorescence microscopy. Amikacin was a better comparison standard because it presented a dose-response behavior. Essential oils had low antibacterial activity as compared to amikacin, with a maximum of 10% and 20% for C. latifolia and C. sinensis, respectively. Combinations of essential oils with antibiotic resulted in an unforeseen strong inhibition of amikacin activity. Although a low antibacterial activity was found, a series of standardization steps are proposed for antibacterial activity measurement by flow cytometry.

Abstract: SUMMARY: The global response to the COVID-19 pandemic has led to a rapid increase of scientific literature on this deadly disease. Extracting knowledge from biomedical literature and integrating it with relevant information from curated biological databases is essential to gain insight into COVID-19 etiology, diagnosis, and treatment. We used Semantic Web technology RDF to integrate COVID-19 knowledge mined from literature by iTextMine, PubTator, and SemRep with relevant biological databases and formalized the knowledge in a standardized and computable COVID-19 Knowledge Graph (KG). We published the COVID-19 KG via a SPARQL endpoint to support federated queries on the Semantic Web and developed a knowledge portal with browsing and searching interfaces. We also developed a RESTful API to support programmatic access and provided RDF dumps for download. AVAILABILITY AND IMPLEMENTATION: The COVID-19 Knowledge Graph is publicly available under CC-BY 4.0 license at https://research.bioinformatics.udel.edu/covid19kg/.

Abstract: Multiplications, mutations and dysregulation of the alpha synuclein gene (SNCA) are associated with the demise of dopaminergic neurons and are considered to play important roles in the pathogenesis of familial and sporadic forms of Parkinson's disease. Regulation of SNCA expression might thus be an appropriate target for treatment. We aimed to identify specific modulators of SNCA transcription, generated CRISPR/Cas9 modified SNCA-GFP-luciferase (LUC) genomic fusion- and control cell lines and screened a library of 1649 bioactive compounds, including the FDA approved drugs. We found no inhibitors but three selective activators which increased SNCA mRNA and protein levels.

Abstract: After ingestion, consumed drugs and their metabolites are incorporated into hair, which has a long detection window, ranging up to months. Therefore, in addition to conventional blood and urine analyses, hair analysis can provide useful information on long-term drug exposure. Meta-bolite-to-drug (MD) ratios are helpful in interpreting hair results, as they provide useful information on drug metabolism and can be used to distinguish drug use from external contamination, which is otherwise a limitation in hair analysis. Despite this, the MD ratios of a wide range of pharmaceuticals have scarcely been explored. This review aims to provide an overview of MD ratios in hair in a range of pharmaceuticals of interest to forensic toxicology, such as antipsychotic drugs, antidepressant drugs, benzodiazepines, common opiates/opioids, etc. The factors influencing the ratio were evaluated. MD ratios of 41 pharmaceuticals were reported from almost 100 studies. MD ratios below 1 were frequently reported, indicating higher concentrations of the parent pharmaceutical than of its metabolite in hair, but wide-ranging MD ratios of the majority of pharmaceuticals were found. Intra- and interindividual differences and compound properties were variables possibly contributing to this. This overview presents guidance for future comparison and evaluation of MD ratios of pharmaceuticals.

Abstract: Antimicrobial peptides (AMPs) are widespread in multicellular organisms. These structurally diverse molecules are produced as the first line of defense against pathogens such as bacteria, viruses, fungi, and parasites. Also known as host defense peptides in higher eukaryotic organisms, AMPs display immunomodulatory and anticancer activities. During the last 30 years, technological advances have boosted the research on antimicrobial peptides, which have also attracted great interest as an alternative to tackling the antimicrobial resistance scenario mainly provoked by some bacterial and fungal pathogens. However, the introduction of natural AMPs in clinical trials faces challenges such as proteolytic digestion, short half-lives, and cytotoxicity upon systemic and oral application. Therefore, some strategies have been implemented to improve the properties of AMPs aiming to be used as effective therapeutic agents. In the present review, we summarize the discovery path of AMPs, focusing on preclinical development, recent advances in chemical optimization and peptide delivery systems, and their introduction into the market.

Abstract: BACKGROUND: Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype-phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms' models and examined the performance of different machine approaches for that task. RESULTS: We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models' differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems' performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance. CONCLUSIONS: Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale.

Abstract: Insulin-resistance (IR) is one of the most important precursors of type 2 diabetes (T2D). Recent evidence suggests an association of depression with the onset of T2D. Accumulating evidence shows that depression and T2D share common biological origins, and DNA methylation examination might reveal the link between lifestyle, disease risk, and potential therapeutic targets for T2D. Here we hypothesize that integrative mining of IR and depression cohort data will facilitate predictive biomarkers identification for T2D. We utilized a newly proposed method to extract gene-level information from probe level data on genome-wide DNA methylation array. We identified a set of genes associated with IR and depression in clinical cohorts. By overlapping the IR-related nutraceutical-gene network with depression networks, we identified a common subnetwork centered with Vitamin D Receptor (VDR) gene. Preliminary clinical validation of gene methylation set in a small cohort of T2D patients and controls was established using the Sequenome matrix-assisted laser desorption ionization-time flight mass spectrometry. A set of sites in the promoter regions of VDR showed a significant difference between T2D patients and controls. Using a logistic regression model, the optimal prediction performance of these sites was AUC = 0.902and an odds ratio = 19.76. Thus, monitoring the methylation status of specific VDR promoter region might help stratify the high-risk individuals who could potentially benefit from vitamin D dietary supplementation. Our results highlight the link between IR and depression, and the DNA methylation analysis might facilitate the search for their shared mechanisms in the etiology of T2D.

Abstract: PURPOSE: The field of oncology is expanding rapidly. New trials are opening as an increasing number of therapeutic agents are being investigated before they can become approved therapies. Aggregate views of these data, particularly data associated with diseases, biomarkers, and drugs, can be helpful in understanding the trends in current research as well as existing gaps in cancer care. METHODS: In this paper, we performed a landscape analysis for breast cancer and acute myeloid leukemia related trials with structured, curated data from clinical trials using the My Cancer Genome clinical trial knowledgebase. RESULTS: We have performed detailed analytics on breast cancer (N = 1,128) and acute myeloid leukemia trial sets (N = 483) to highlight the top biomarkers, drug classes, and drugs-thereby supporting a full view of biomarkers, biomarker groups, and drugs that are currently being explored in these respective diseases. CONCLUSION: Analysis and data visualization of the cancer clinical trial landscape can inform strategic planning for new trial designs and trial activation at a particular site.

Abstract: NDEx, the Network Data Exchange (https://www.ndexbio.org) is a web-based resource where users can find, store, share and publish network models of any type and size. NDEx is integrated with Cytoscape, the widely used desktop application for network analysis and visualization. NDEx and Cytoscape are the pillars of the Cytoscape Ecosystem, a diverse environment of resources, tools, applications and services for network biology workflows. In this article, we introduce researchers to NDEx and highlight how it can simplify common tasks in network biology workflows as well as streamline publication and access to). Finally, we show how NDEx can be used programmatically via Python with the 'ndex2' client library, and point readers to additional examples for other popular programming languages such as JavaScript and R. (c) 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Getting started with NDEx Basic Protocol 2: Using NDEx and Cytoscape in a publication-oriented workflow Basic Protocol 3: Manipulating networks in NDEx via Python.