In Hausdorff locally convex topological vector spaces, the Krein-Milman theorem allow us to describe compact convex sets as the closed convex hull of their extreme points. This coupled with measure-theoretic inequalities from the theory of probability, gives us a theoretical proof of where the optima of certain programs can be located. We aim to extend this results to abstract convexities in more general settings.

Test

Test

Test

*Note: Mol2Net conference is associated to different MDPI journals special issues guest edited by Mol2Net Conference Committee members. This is an strategy to increase the online post-publication visibility of papers and conference, promote post-publication brainstorming discussion, and increase authors feedback. This association implies that our conference perform post-publication indexing of selected papers already published in MDPI journals with the consent of the issue editors. We publish free-of-cost these post-publication summaries. They include a shortened title, corresponding author info, and paper cover pdf file. The cover pdf file contains paper first page with all authors, abstract, full reference , and link to original papers.

Reference: This is a Mol2Net conference post-publication cover for a paper published in the special issue Complex Networks, Bio-Molecular Systems, and Machine Learning, Edited by: Dr. H González-Díaz. Visit the link to see original paper. Reference: Int. J. Mol. Sci. 2021, 22(19), 10518; https://doi.org/10.3390/ijms221910518

Abstract. The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), responsible for the coronavirus disease of 2019 (COVID-19) pandemic, has affected and continues to affect millions of people across the world. Patients with essential arterial hypertension and renal complications are at particular risk of the fatal course of this infection. In our study, we have modeled the selected processes in a patient with essential hypertension and chronic kidney disease (CKD) suffering from COVID-19, emphasizing the function of the renin-angiotensin-aldosterone (RAA) system. The model has been built in the language of Petri nets theory. Using the systems approach, we have analyzed how COVID-19 may affect the studied organism, and we have checked whether the administration of selected anti-hypertensive drugs (angiotensin-converting enzyme inhibitors (ACEIs) and/or angiotensin receptor blockers (ARBs)) may impact the severity of the infection. Besides, we have assessed whether these drugs effectively lower blood pressure in the case of SARS-CoV-2 infection affecting essential hypertensive patients. Our research has shown that neither the ACEIs nor the ARBs worsens the course infection. However, when assessing the treatment of hypertension in the active SARS-CoV-2 infection, we have observed that ARBs might not effectively reduce blood pressure; they may even have the slightly opposite effect. On the other hand, we have confirmed the effectiveness of arterial hypertension treatment in patients receiving ACEIs. Moreover, we have found that the simultaneous use of ARBs and ACEIs averages the effects of taking both drugs, thus leading to only a slight decrease in blood pressure. We are a way from suggesting that ARBs in all hypertensive patients with COVID-19 are ineffective, but we have shown that research in this area should still be continued.

Test

Dear colleagues worldwide welcome to a joint call of CHEMBIOINFO-08: Cheminformatics, Chemometrics, Bioinformatics, Computational & Quantum Chemistry Congress München, Germany-Cambridge, UK-Chapel Hill, USA, 2022 . CHEMBIONFO is an inter-university trans-Atlantic Chem-Bioinformatics, Computational Chemistry, and Computational Biology congress series. From the America's side the event is organized by professors of University of North Carolina (UNC Chape Hill), Chape Hill, NC, USA, and professors of North Carolina Central University (NCCU), Durham, NC, USA. From Europe's side the event is organized by professors and researchers of Helmholtz Zentrum München - German Research Center for Environmental Health (GmbH), Germany, The European Bioinformatics Institute (EMBL-EBI), Cambridge, United Kingdom, Center for Neuroscience and Cell Biology (CNC), University of Coimbra (UC), Coimbra, Portugal, the University of The Basque Country (UPV/EHU) and IKERBASQUE, Basque Foundation for Science, Bilbao, Basque Country, Spain. This congress is associated to the MOL2NET International Conference Series on Multidisciplinary Sciences. MOL2NET (From Molecules to Networks), International Conference on Multidisciplinary Sciences, ISSN: 2624-5078, MDPI SciForum, Basel, Switzerland.Topics of Interest. CHEMBIOINFO series aims to bring together leading academics, researchers and scholars to share their experiences on all aspects of computational programing, modeling, simulations, or scientific computing dedicated to Computational Chemistry, Chemoinformatics, Bioinformatics, Systems Biology, Biocomputing, etc. We focus on applications to Medicinal Chemistry, Drug Design & Discovery, Pharmaceutical Industry, Natural Products Research, Toxicology, Vaccine Design, Biotechnology, Personalized Medicine, Biomedical Engineering, etc. We welcome contributions involving the following topic:Chemoinformatics & Computational Chemistry. Chemoinformatics, QSAR, QSPR, QSTR Modelling, Quantum Chemistry, Functional Density Theory (DFT), Ab Initio Methods, Semi-Empirical Methods, Machine Learning (ML) Quantum Chemistry Potentials, Molecular Mechanics/Molecular Dynamics (MM/MD), Molecular Docking, etc.Chemometrics and Data Analysis. Chemometrics, Matlab, R and Python. Data types, PCA, Data Pre-processing, Multivariate Regression, Multivariate Classification, Variable selection methods, Multivariate Curve Resolution (MCR), Multi-way analysis, Non-linear modelling, Multiblock analysis and data fusion, Multivariate Error estimation, Design of Experiments, ANOVA-Simultaneous Component Analysis, Hyperspectral Image Analysis, Metabolomics Data Analysis.Bioinformatics and Systems Biology. Proteomics, Genomics, BLAST and sequence alignment, Meta-Genomics, Protein folding and Protein Structure prediction, RNA secondary structure prediction, Protein Interaction Networks (PINS), Gene Regulatory Networks (GRN), Metabolomics and Metabolic Networks, Synthetic Biology, Data Analysis, and related techniques.Computer Coding, Data Analysis, Artificial Intelligence, and Soft Computing. The congress also deals with the development or application of computer languages, computational programs, and/or algorithms for the study of chemical and biological process and/or inspired on biological processes. This include programing in Python/Biopython, Perl/Bioperl, etc. Development or application of Machine Learning (ML), Artificial Neural Networks (ANN), Deep Learning Networks (DLN), Bayesian Networks, Random Forest, Classification Trees, Genetic Algorithms (GA), Swarm Intelligence (SI), Ant Colony Algorithms, Artificial Imune Systems, etc.Bioethics and Biolaw: Last, not the least, the workshop deals with all the Legal, Regulatory, and Bioethics issues emerging from use of new experimental and ICTs in the previous areas such as Drug patenting, Genome patenting, Drug re-purposing patents, Copyrights and Intellectual Property Protection of Cheminformatics, Chemometrics, Bioinformatics, and Computational Chemistry Software and Models, etc. GDPR and Personal data protection in Human Bioinformatics, etc.
https://mol2net-08.sciforum.net/chembioinfo-08

*Note: Mol2Net conference is associated to different MDPI journals special issues guest edited by Mol2Net Conference Committee members. This is an strategy to increase the online post-publication visibility of papers and conference, promote post-publication brainstorming discussion, and increase authors feedback. This association implies that our conference perform post-publication indexing of selected papers already published in MDPI journals with the consent of the issue editors. We publish free-of-cost these post-publication summaries. They include a shortened title, corresponding author info, and paper cover pdf file. The cover pdf file contains paper first page with all authors, abstract, full reference , and link to original papers.

Reference: This is a Mol2Net conference post-publication cover for a paper published in the special issue Complex Networks, Bio-Molecular Systems, and Machine Learning, Edited by: Dr. H González-Díaz. Visit the link to see original paper. Reference: Int. J. Mol. Sci. 2021, 22(11), 5823; https://doi.org/10.3390/ijms22115823

Abstract. The variability of methicillin-resistant Staphylococcus aureus (MRSA), its rapid adaptive response against environmental changes, and its continued acquisition of antibiotic resistance determinants have made it commonplace in hospitals, where it causes the problem of multidrug resistance. In this study, we used molecular topology to develop several discriminant equations capable of classifying compounds according to their anti-MRSA activity. Topological indices were used as structural descriptors and their relationship with anti-MRSA activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones and quinolone-like compounds. Four extra equations were constructed, named DF_MRSA1, DF_MRSA2, DF_MRSA3 and DF_MRSA4 (DF_MRSA was built in a previous study), all with good statistical parameters, such as Fisher–Snedecor F (>68 in all cases), Wilk’s lambda (<0.13 in all cases), and percentage of correct classification (>94% in all cases), which allows a reliable extrapolation prediction of antibacterial activity in any organic compound. The results obtained clearly reveal the high efficiency of combining molecular topology with LDA for the prediction of anti-MRSA activity.

QSAR (Quantitative Structure-Activity Relationship) methods have been the basis for the design of new molecules with a certain activity. The great advantage of QSAR methods is that they are able to predict the pharmacological activity of compounds without the need to obtain or synthesize them previously.

Initially, drug design was a long and expensive process in which a vast number of compounds were synthesized and tested with a very low success rate. Virtual screening appears as a low cost solution to this problem by allowing researchers to identify molecules that are likely to be active from a virtual library of thousands of compounds, allowing also the application of drug-like filters.

Currently, the development of antibiotic resistance by microorganisms is one of the most important problems that have appeared in recent years in the treatment of infectious diseases. This increased resistance is associated with increased morbidity and mortality from infections, as well as an increase in healthcare costs. The development of new molecules with antibacterial activity is therefore urgently needed.

By means of molecular topology, we developed discriminant functions capable of predicting antibacterial activity (FD1 & FD2). When applied to a database with over 10000 compounds, they selected 266 compounds as candidates from which 40.6 % have this activity according to bibliography. Regression equations determining pharmacokinetic properties such as mean residence time (MRT), volume of distribution (VD) and Clearance (CL) were applied as filters to the selected molecules, reaching a bibliographic success rate of 45.5, 50.0 and 55.9 %, respectively, which proves the usefulness of these mathematical-topological filters.

Beta-glucan is a type of polysaccharide composed of a sequence of glucose (sugar) molecules linked together. It has been identified as providing great benefits for animals and humans. There are different sources of beta-glucan such as cereals (oats and barley), but those from yeast have been confirmed to be of greater benefit to both human and animal health because they allow a perfect binding with the membranes of immune cells. As a result of this high affinity, the percentage of beta-glucans that manage to bind to the immune cell receptor is notably higher than beta-glucans from plant sources. It can thus be stated that the immune response elicited by yeast beta-glucans is the highest known so far. At present, the microorganism most widely used industrially in the development of food additives is Saccharomyces cerevisiae because it was one of the first organisms to be genetically modified to produce such additives. However, the yeast Yarrowia lipolytica has also been shown to have different properties for food use.

In this review, the production of beta-glucan from Yarrowia lipolytica for different applications will be analyzed as a great potential for business creation.