Polycystic Ovary Syndrome (PCOS) is extremely prevalent and diverse. It is the most commonly encountered heterogeneous endocrine disorder in premenopausal (reproductive age) women worldwide.^[1] Studies shows that it affects 5-10% of this population.^[2] It is a characteristic syndrome of ovarian dysfunction associated with hyperandrogenism, chronic anovulation, endometrial hyperplasia and significant morbidity. Many other crucial body systems are also affected causing hirsutism, infertility, alopecia, menstrual irregularities and obesity.^[3] The aetiology of this syndrome is still debatable. Although it is found that PCOS is common among middle and high-income urban population rather than in the rural population.^[5]The women with PCOS have a higher risk of developing type 2 diabetes mellitus and impaired glucose tolerance at an early stage.^[4]Insulin resistance is the key feature of PCOS and it is characterised by hyperinsulinemia. Obese women are at a higher risk of developing insulin resistance than normal-weight women and have higher hyperandrogenism.^[6]

The growth factor receptor-bound protein-2 (GRB2) is an adapter protein and is essential for cellular functions. It can either promote or block the cellular transformation and proliferation depending upon its activation and inhibition respectively. It plays a critical role in linking cell surface growth receptors (EGFR) and the Ras signalling pathway.^[7]

It is very crucial to curb the overexpression of the protein by using a potent ligand to activate essential cellular functions. It is used to comprehend the strength of association and the binding affinity between the appropriate ligands and the target binding site. This helps to develop more efficient drug candidates which would essentially help in the curing of the syndrome. The aim of the present investigation is to identify a potential GRB2 inhibitor towards the clinical treatment of Polycystic ovary syndrome (PCOS) using various molecular docking^[8-15] and virtual screening approaches^[16-28].

Among the greatest difficulties of Biology teachers who teach in high school is the adequacy of alternative teaching resources to the teaching process, in order to facilitate mediation and understanding of the content covered. The absence of didactic resources and difficulties in developing low-cost strategies, which integrate the contents covered and contextualize them within problems in which the student must seek to solve, leaves the teaching and learning process deficient. Within this context, this study aimed to apply and evaluate two practical classes in a public school in the municipality of Jaciara-MT. The evaluation was carried out using questionnaires about the contents involved in the classes, applied before and after the practical classes. It was observed that most of the students involved in the study showed greater mastery over the contents, after the practical classes.

Silver nanoparticles (AgNP) have found prominence in different fields such as medicine, catalysis, nanoelectronics, textile field, pollution and water treatment due to their unique attributes. Applications of AgNP are increasing rapidly in the medical purpose including drug delivery, treatment, diagnosis, medical device coating. Various chemical and physical methods are used to synthesize the AgNP conventionally. However, these processes are expensive and also have side effects. To solve these problems by modification in synthesis process for safer and more efficiency, synthesis of biogenic AgNP from plant extract, known as Green Nanotechnology, have come to play a very crucial role, In this study, we have reported the green synthesis of AgNP using Ocimum sanctum (Tulsi) leaf extract, which act as reducing agent as well as capping agent. Synthesized AgNPs were characterized and their antibacterial and anticancer activities were observed. The development of brown color by the addition of Tulsi signifies the formation of silver nanoparticles. UV-Vis absorption spectroscopy, XRD and zeta potential were applied to estimate the quantitative formation of silver nanoparticles. FTIR analysis was used to reveal that the AgNPs were stabilized by eugenols, terpenes, and other aromatic compounds present in the leaf extract. The antimicrobial and anticancer properties of AgNPs were assessed by various in vitro cellular assays. Our present study confirms that AgNP can be used as a dual therapeutic option for combating pathogenic microbial strains as well as hepatocellular cancer.

Welcome to the MODEC2020 workshop. This is Amazon State University's (UEA) FIFTH workshop (30 Jan 2020–30 Jan 2021), devoted to the promotion and application of the Multidisciplinary Sciences to the development of natural products and agro-industrial processes in Ecuadorian Amazon regions. This includes the application of Information and Communications Technologies (ICTs) for data analysis and computational model including the fields of Agro-industrial Engineering, Chemistry, Chemical Engineering, Biotechnology, Veterinary Medicine, and/or Environmental Sciences, etc. We invite you to visit the official web of the workshop: https://mol2net-06.sciforum.net/

This study describes a new chemometric tool for the analysis of chromatographic data: the Superposing Significant Interaction Rules (SSIR) is a variable selector coming from QSAR field that directly analyses the raw internal data coming from the chromatographic software. This allowed the identification of relevant volatile compounds in cork (treated and not treated samples in the industry) extracted by untargeted HS-SPME in a particular case for which traditional treatments (PCA, Discriminant Analysis) did not produced relevant results. The procedure has revealed the presence of compounds which are increased in the case of treated samples. The obtained classificatory model is robust, as it passed satisfactorily cross-validation tests (96% or more in performance for leave-one-out processes). This is the first time SSIR procedure is applied for the analysis of chromatographic information.

Alginates are outstanding biomaterials due to their excellent biocompatibility, renewability, biodegradability and cost-effectiveness in comparison with other biopolymers. Nevertheless, in general, these hydrogels have poor mechanical performance that limit their potential applications in biomedical areas such as skin tissue engineering and wound healing. In this regard, the study follows an enhanced engineering route to synthesized alginate-based films reinforced with different amounts (0, 0.1, 0.5, 1 and 2% w/w) of carbon nanofibers (CNFs) and characterize their physical and biological behavior. The results of this study showed that these composite materials possess similar biological properties to neat alginate hydrogels. Thus, none of the synthesized composite materials showed any cytotoxic effect and no cell adhesion was observed on the films. Water sorption at the human temperature (about 37°C) did not suffer substantial changes with the addition of CNFs into the polymer matrix. The dynamic mechanical and tensile/compressive performance of calcium alginate were significantly enhanced with the incorporation of even a very low amount of CNFs. Thus, the tensile and compression modulus of the calcium alginate films in the dry and hydrated state increases up to three and six times, respectively, with the load of 2% w/w CNFs. Furthermore, the composite biomaterials reinforced with the lowest CNFs amount have the advantage of possessing more transparency and lower production costs.

Carbon nanoparticles are known because of their highly fluorescent property. Thus, among several different types of nanoparticles, carbon nanoparticles have great potential of bio imaging applications. Highly fluorescent crystalline carbon nanoparticles (CNPs) have been synthesized in a facile, rapid method which involves microwave irradiation of sucrose with phosphoric acid at 100 W for 4 mins. Hence this method is fleet and cost effective for large scale applications. Physical characterization of synthesized CNPs was done by DLS and Zeta potential, Hydrodynamic size of CNPs as measured by DLS was 281.2 d.nm. The surface charge of carbon nanoparticles was found to be -39.7 mV. These CNPs have green fluorescence under UV exposure. CNPs enter into cell without any further modification and show their efficiency as fluorescence based cell imaging application. Further, we have explored the antibacterial property of carbon nanoparticles by Minimum Inhibitory Concentration (MIC).

The currently surging SARS-COV-2 (or CoVID-19) is challenging the public health authorities worldwide. As of now there is no approved vaccine or drug available for the control of the viral disease. Therefore, non-pharmaceutical interventions (NPIs) are being used around the world to manage the spread of CoVID-19. In this article we used a computer-assisted vaccine design (CAVD) approach to develop a set of most probable peptide vaccine candidates which can be tested for their efficacy by wet lab experiments.

Corona virus disease 2019 (COVID-19) is a respiratory illness that can spread from person to person. The virus that causes COVID-19 is a novel corona virus that was first identified during an investigation into an outbreak in Wuhan, China.

COVID-19 is spreading from person to person in almost all the countries worldwide. Risk of infection with COVID-19 is higher for people who are close contacts of someone known to have COVID-19, for example healthcare workers, or household members. Other people at higher risk for infection are those who live in or have recently been in an area with ongoing spread of COVID-19. Infection from corona virus can be characterised by common symptoms such as fever, cough and fatigue, while other symptoms may include sputum production, headache and pneumonia.

The virus that causes COVID-19 probably emerged from an animal source, but is now spreading in human beings. The virus is thought to spread mainly between people who are in close contact with one another (within about 6 feet) through respiratory droplets produced mainly when an infected person coughs or sneezes. It may also possible that a person can get COVID-19 by touching a surface or object that is infected from virus and after that touching their own mouth, nose, or possibly their eyes, but this is not thought to be the main way the virus spreads.

The particles within the nanoregime are quite smaller (100- 10,000 times) than human cells but are comparable to that of biomolecules like enzymes and receptors. The nanoparticles smaller than 50 nm can easily pervade into most cells, and those particles smaller than 20 nm can easily escape into the circulation through the blood vessels. Nanoparticles are quite conducive to be fabricated appropriately to serve as a device/vehicles of important therapeutic genes or drugs specifically to the cancer cells avoiding any hazardous effects on the normal cells. This review encompasses the recent investigations employing nanocarriers like liposomes, micelles, carbon nanotubes, dendrimers, nanoshells that has been developed with positive results.The cancer therapeutic agents like Doxorubicin, Paclitaxel, Cystatin ,Small interfering RNA(SiRNAs) are encapsulated within these nanocarriers through the processes of entrapping, covalent binding, encapsulation or adsorption. Furthermore these nanoparticles were conjugated with cancer specific targetic ligands like Folic acid, Monoclonal antibody,Luteinising hormone releasing hormone(LHRH)peptide,etc which enable them to successfully deliver the therapeutic agents to the cancerous cells. Henceforth the development of these smart nanodevices will undoubtedly pave the way for coming up with future novel therapeutic strategies for combating the malignant cells circumventing any adverse side effects on the normal cells.

We consider a novel approach to mathematically define a graphing method to represent amino acid sequences of proteins in two-dimensional plane and characterize them numerically. The amino acids are represented by their relative magnitude of their hydrophobicity. Each amino acid is compared with a vector and moves in relative direction which generates a graph. Applications are shown in Zaire Ebola Virus to conclude how this plotting can be more useful than base sequence plotting. Also, superimposition graph of SARS and SARS-CoV-2 shows that these sequences are strongly related and Various other applications are shown too to explain it's fruitfulness.

Okra is a cultigen (a plant that has been altered by humans through aprocess of selective breeding). The exact origin of okra is unknown, but it is thought to have come from Africa, where it has been grown as a crop for centuries. Evidence suggests it was grown in Egypt as long ago as 2,000 BC. Today it is widely cultivated for its edible green fruits, which are harvested when immature (after 3 - 5 days of development), and are infamous for their slimy mucilage. It plays a vital role to preserve our health. In recent times, the use of herbal products has increased tremendously in the western world as well as developed countries. India is one of the most medico-culturally diverse countries in the world where the medicinal plant sector is part of a time-honoured tradition that is respected even today. Medicinal plants are believed to be safer and proved elixir in the treatment of various ailments. Abelmoschus esculentus (Okra) is an important medicinal plant of tropical and subtropical India. Its medicinal usage has been reported in the traditional systems of medicine such as Ayurveda, Siddha and Unani.

The emergence of natural polymers like gums and mucilages in drug delivery systems has curbed the rampant use of the synthetic materials for therapeutic purposes. Natural excipients offered advantages such as non-toxicity, less cost and abundantly availablity. Aqueous solubility of natural excipients plays an important role in their selection for designing immediate, controlled or sustained release formulations. This review article provide an overview of natural gum, polymers and mucilages as excipients in dosage forms as well as novel drug delivery systems.These recent investigations have provided ample evidences that these natural gums and mucilages like Gellan Gum,Gum Acacia,Locust bean gum could efficaciously deliver therapeutics to the diseased site without exerting any significant adverse effects on the normal cells. Hencefoth these natural polymers are endowed with the ability to function as like green emissaries for the transport of essential therapeutic agents which in turn will help in restraining the belligerence of different grave diseases like cancer.

In India emissions of gaseous pollutants increasing day by day due to rapidly growth in industrialization, population density and urbanization. In this study, we present the annual and seasonal variations of carbon monoxide (CO) concentration over India region from 2006-2015 using satellite remote sensing dataset from the sources Atmospheric Infrared Sounder (AIRS). In this study we analyzed the spatio-temporal variations of gases and their seasonal behaviors i.e., monthly, seasonal, annual mean variations of trace gases and also trend analysis of CO gases and comparison of the seasonal behavior of the CO gases by trend analysis were assessed. The highest column amount of CO emission was observed in east-to-western part of India region due to fossil and bio-fuel combustions, biomass burning, smoke and industrial process and oxidation of methane. In this study we also examine the seasonal yearly variations, increment and decrement of CO concentrations over the selected eleven different cities of India region by considering 2006 as a base year and propose the behaviors of gases during (2007-2015). In India maximum CO emission noticed in the immensely populated states of Uttar Pradesh, Bihar and West Bengal, where usage of fuel wood burning and bio-fuel is predicted to be acute for domestic purpose as in rural areas, 80% of population lives.

A new method, 4D-Dynamic Representation of DNA/RNA Sequences, aiming at similarity/dissimilarity analysis of biological sequences, has been formulated. It belongs to a group of non-standard bioinformatics approaches called alignment-free methods. In the new method, sequences are represented by sets of material points in a 4D space - 4D-dynamic graphs. The method is a generalization of our previous 2D and 3D approaches [1,2]. In particular, 2D and 3D methods have been applied for the characterization of the complete genome sequences of viruses [3,4,5]. We call the methods dynamic, because the graphs are characterized by some values analogous to the ones used in the classical dynamics. In this work 4D moments of inertia are proposed as numerical characteristics (descriptors) of the 4D-dynamic graphs representing the sequences. 2D and 3D projections of the 4D-dynamic graphs are proposed as graphical representations of the sequences. 4D-Dynamic Representation of DNA/RNA Sequences has been applied to an analysis of the complete genome sequences of the 2019 novel coronavirus, available in March 2020 in GenBank. The proposed descriptors of the 4D-dynamic graphs proved to be very good. 4D moments of inertia correctly classify the sequences. The descriptors representing complete genome sequences of Deltacorovirus and of Betacoronavirus are located in different parts of the classification maps. The detailed classification of Betacoronaviruses to Embevovirus and the 2019 novel coronavirus is also recognized by the method. The descriptors representing Embevovirus and the 2019 novel coronavirus are also located in different parts of the maps.

References

[1]. Bielińska-Wąż, D.; Clark, T.; Wąż, P.; Nowak, W.; Nandy, A. 2D-dynamic representation of DNA sequences. Chem. Phys. Lett. 2007, 442, 140–144.

[2]. Wąż, P.; Bielińska-Wąż, D. 3D-dynamic representation of DNA sequences. J. Mol. Model. 2014, 20, 2141.

[3]. Panas, D.; Wąż, P.; Bielińska-Wąż, D.; Nandy, A.; Basak, S.C. 2D–Dynamic Representation of DNA/RNA Sequences as a Characterization Tool of the Zika Virus Genome. MATCH Commun. Math. Comput. Chem. 2017, 77 321–332.

[4]. Panas, D.; Wąż, P.; Bielińska-Wąż, D.; Nandy, A.; Basak, S.C. An Application of the 2D-Dynamic Representation of DNA/RNA Sequences to the Prediction of Influenza A Virus Subtypes, MATCH Commun. Math. Comput. Chem. 80 (2018) 295-310.

[5]. Bielińska-Wąż, D.; Panas, D.; Wąż, P. Dynamic Representations of Biological Sequences, MATCH Commun. Math. Comput. Chem. 82 (2019) 205-218.

You are welcome to follow the lesson on Data Commercialization by Ann-Kristin Lieberknecht, MSc. in the course Data Ethical and Legal Issues Regarding ICT Data Protection. The lesson focuses on (1) what is data commercialization, (2) why is it relevant, and (3) the regulation of data commercialization and other aspects. This course is offered within the framework of the PANELFIT Project by the University of the Basque Country UPV/EHU, with the help of the Partners and especially of the European Citizen Science Association (ECSA). The specific objective of this course is to train citizens on basic ethical and legal issues related to the protection of personal data, in accordance with the new European Data Protection Regulation and complementary regulations. https://www.panelfit.eu/ethical-and-legal-issues-regarding-ict-data-protection/

You are welcome to follow the lesson on The Importance of Data in Today´s World by Prof. Dr. Carlos María Romeo Casabona in the course Data Ethical and Legal Issues Regarding ICT Data Protection. This course is offered within the framework of the PANELFIT Project by the University of the Basque Country UPV/EHU, with the help of the Partners and especially of the European Citizen Science Association (ECSA). The specific objective of this course is to train citizens on basic ethical and legal issues related to the protection of personal data, in accordance with the new European Data Protection Regulation and complementary regulations. https://www.panelfit.eu/ethical-and-legal-issues-regarding-ict-data-protection/

Among the different types of biomaterials, natural excipients Okra mucilage (MNP) is economic and has the potential for controlled drug delivery. We have synthesized MNPs by co-precipitation method and characterized them by XRD, FESEM, FTIR, UV-Vis spectra and DLS. Despite their potential anti-cancer activity, solubility of curcumin, piperine, thymoquinone is very low rendering its limit in application. We have used MNPs where the natural compounds like curcumin, piperine, thymoquinone can be loaded comfortably and thereby increases its bioavailability. The antibacterial activity of these nanoparticles were evaluated against pathogenic bacterial strains. The cytotoxicity of curcumin ,piperine, thymoquinone encapsulated MNPs was evaluated on triple negative breast cancer cell lines. They were found to induce apoptosis by perturbing the mitochondrial membrane potential. Folic acid was conjugated to curcumin, piperine, thymoquinone encapsulated MNPs, for delivering it specifically to the breast cancer cells. The antimicrobial and anticancer potential of conjugated and non conjugated MNP were assessed by various in vitro cellular assays. Our present study confirms that these functionacan be used as a dual therapeutic option for combating pathogenic microbial strains and triple negative breast cancer cell.

The application of natural polysaccharides in novel drug delivery systems to deliver the bioactive agents has been hampered by the synthetic polymers. The main benefits of the natural polysaccharides are their biodegradable, biocompatible, non-toxic, abundant and economic. Because of the advances in drug delivery technology, natural polysaccharides are included in novel drug delivery to fulfill multitask functions and in some cases directly or indirectly control the extent and/or rate of drug release. Substantial research efforts have been directed towards developing safe and efficient natural based polysaccharide particulate drug delivery systems. The present work outlines the natural based okra mucilage and their isolation, purification, standardization and characterization along with their biological applications are covered.

Interactions of plants with nanoparticles ( NPs) include uptake, translocation and accumulation of NPs, depending on the nature of plant species as well as the shape, size, type, chemical composition, functionalization and stability of engineered nanoparticles (ENPs). Most of the studies deal with negative effect on growth and development of seedlings. So far, germination, biomass assay, antimicrobial, anatomical as well as few histological studies has been done by using varieties of ENPs. This interaction is very important as the NPs which are stored within the plants; get transferred from plants (producers) to animals (consumers).

Research shows that not only the negative aspect, but NPs can have great impact on positive aspects on different fields, of which we highlight mainly on the agricultural sector.

The use of plastics for packaging across many industries has become a standard, however, in recent years there has been an increase in demand for more eco-friendly packaging alternatives. In this vein, the use of plant proteins in film formation has been studied extensively.^1-2 Due to the strong mechanical properties obtained through the use of plant-based proteins, the biodegradability of such materials, and their economic efficiency, these proteins are of substantial interest in the effort to replace current synthetic packaging films.^{3 -5}

In our work, we focus on combining both experimental and computational techniques in order to develop protein-based films for food packaging applications. By combing the two techniques, we aim to better understand the interactions of plant-based proteins with selected plasticizing modifiers in order to develop films with optimal mechanical properties. In our work we compare the interactions of selected modifiers with soy protein and zein protein from corn. In this regard, we apply various computational techniques, including protein-ligand docking^6-7 and molecular modeling methods^8-9 to assess the interactions and then compare our finding with experimental data^1,3,10-11. By modeling how choice plasticizing modifiers interact differently with each protein, we aim to better formulate our films in order to achieve mechanical properties that compete with those of current synthetic packaging systems.

References

1. Calva-Estrada, S. J.; Jiménez-Fernández, M.; Lugo-Cervantes, E. Protein-Based Films: Advances in the Development of Biomaterials Applicable to Food Packaging. Food Engineering Reviews2019, 11(2), 78–92.
2. Park, H. J.; Bunn, J. M.; Weller, C. L.; Vergano, P. J.; Testin, R. F. Water Vapor Permeability and Mechanical Properties of Grain Protein-Based Films as Affected by Mixtures of Polyethylene Glycol and Glycerin Plasticizers. Transactions of the ASAE1994, 37(4), 1281–1285.
3. Ghanbarzadeh, B.; Oromiehi, A. Biodegradable Biocomposite Films Based on Whey Protein and Zein: Barrier, Mechanical Properties and AFM Analysis. International Journal of Biological Macromolecules2008, 43(2), 209–215.
4. Reddy, N.; Yang, Y. Thermoplastic Films from Plant Proteins. Journal of Applied Polymer Science2013, 130(2), 729–738.
5. Shukla, P.; Bhise, S.; Thind, S. S. Roles of Biodegradable Edible Films and Coatings in Food Industry. ACTA Scientific Nutritional Health2019, 3(5), 138–147.
6. Yilmaz, H., Ahmed, L., Rasulev, B., Leszczynski, J. Application of ligand- and receptor-based approaches for prediction of the HIV-RT inhibitory activity of fullerene derivatives, Journal of Nanoparticle Research, 2016, 18(5):123
7. Ahmed L., Rasulev B., Turabekova M., Leszczynska D., Leszczynski J. Receptor- and ligand-based study of fullerene analogues: comprehensive computational approach including quantum-chemical, QSAR and molecular docking simulations, Organic & Biomolecular Chemistry, 2013, 11, 5798–5808
8. Turabekova M.A., Rasulev B.F., Levkovich M.G., Abdullaev N.D. and Leszczynski J. Aconitum and Delphinium sp. Alkaloids as Antagonist Modulators of Voltage-Gated Na+ Channels. AM1/DFT Electronic Structure Investigations and QSAR Studies. Computational Biology and Chemistry, 2008, 32, 88-101
9. Simsek, T., Simsek, S., Mayer, C., Rasulev, B. Combined Computational and Experimental Study on the Inclusion Complexes of β-Cyclodextrin with Selected Food Phenolic Compounds, Structural Chemistry, 2019, 30(4), 1395–1406
10. Demchuk, Z.; Kohut, A.; Voronov, S.; Voronov, A. Versatile Platform for Controlling Properties of Plant Oil-Based Latex Polymer Networks. ACS Sustainable Chemistry & Engineering2018, 6(2), 2780–2786.
11. Masamba, K.; Li, Y.; Zhong, F. Effect of Homogenization Stirring Speed on Mechanical and Water Barrier Properties of Gallic Acid Treated Zein-Oleic Acid Composite Films. Food Packaging and Shelf Life2016, 10, 97–105.
12. Kashiri, M.; Cerisuelo, J. P.; Domínguez, I.; López-Carballo, G.; Hernández-Muñoz, P.; Gavara, R. Novel Antimicrobial Zein Film for Controlled Release of Lauroyl Arginate (LAE). Food Hydrocolloids2016, 61, 547–554.

Breast cancer is a heterogeneous disease that can be classified into different clinical, histopathological and molecular subtypes.‘Triple-negative’ breast cancer (TNBC) comprises about 15% of all breast cancer cases and are devoid of the estrogen receptor (ER), progesterone receptor (PR) and the human epidermal growth factor receptor 2 (HER2) is overexpressed. Triple Negative Breast cancer is differentiated by Brenton et al 2005 on the basis of prognosis and response to the therapy. The treatment that was possible till date for TNBC was chemotherapy as these can breast cancer cells typically lack ER, PR and express ex HER2 Hence the patients are unable to receive any conventional endocrine therapies .It is quite intriguing for for scientific and translational researcher, as TN phenotype initially appears as a potential surrogate for basal-like breast cancers. ER and HER2 mRNA expression are found in basal/ myoepithelial cells of the normal breast, but one of the ‘intrinsic gene’ subtypes of the disease TNBC is characterized by the lack of these ER and HER2 mRNA expression. According, to Perou et al 2000 TNBC are lacking ER and PR expression and HER2 over-expression /HER2 gene amplification.

TNBCs usually exhibit quite high belligerent behaviour and is predominantly found in young women of Hispanic and African descent and a considerable link with BRCA1 germline mutations has also been detected. These TNBCs exhibit quite high metastasis which culminates in the death of the patient within 5 years after diagnosis. However, TNBC is vastly heterogeneous and best considered as an umbrella term which comprises of different entities with characteristic genetic, histological,transcriptional, and clinical attributes. Till date, no effective treatment for metastatic TNBC is available following surgery, radiation and chemotherapy

Among the different types of nanoparticles, gum acacia nanoparticles have the potential for controlled drug delivery to cancer cells. We have synthesized gum acacia nanoparticles by nanoprecipitation method. Physical characterization of the synthesized nanoparticles was done by DLS(Dynamic Light Scattering) and Zeta Potential. Hydrodynamic size of Gum Acacia NPs as measured by DLS was 413.3nm and surface charge of the same was found to be -0.348mV.For the possible biomedical application,we have used curcumin, a natural chemotherapeutic agent.Despite its potential anti-cancer activity, solubility of curcumin is very low rendering its limit in application. Several efforts have been made to solubilise curcumin to enhance its bioavailability. We have used gum acacia nanoparticles where curcumin can be loaded comfortably and thereby increases its bioavailabilty. The cytotoxicity of curcumin-gum acacia NP complex was evaluated on breast cancer cell lines with respect to pre-determined LD50 doses. To investigate the mechanisms, various biochemical assays were also performed. Further, we have explored the fact that the uptake of curcumin by the cells is increased when the curcumin is loaded with gum acacia NPs by utilising the fluorescence property of curcumin. The curcumin-Gum Acacia NP complex was found to induce apoptosis by perturbing the mitochondrial membrane potential of the cancer cells.

The ongoing rapid spread of COVID-19 disease from its first detection in Wuhan, China in late 2019 was declared a pandemic by World Health Organization on 11th March, 2020. It is believed that to combat this deadly virus, now designated as SARS-CoV-2, designing and developing a proper vaccine is the best solution. For developing a sustainable vaccine against this virus, one should have a proper understanding of the mutational changes occurring constantly in its genome and also about the variations that may arise in different communities. Here, we report an algorithm to identify and characterize the mutational changes in the COVID-19 sequences isolated from different countries. The patterns in mutation along with the demographic analysis shown here can be very effective for community specific vaccine designing in the future.

Glass transition temperature T_g is one of the most important thermophysical properties of amorphous polymers. The substantial change in polymer dynamics at glass-transition temperature causes a major change in physical properties, including mechanical modulus, density, specific heat, damping characteristics, dielectric properties of the polymer[1-5]. The cheminformatics approach based on machine learning algorithms is often applied to predict the quantitative relationships between key molecular descriptors and the glass transition temperature T_g of investigated polymers. In this work, we discussing an innovative modeling framework by integrating cheminformatics and coarse-grained molecular dynamics simulations to predict T_g of diverse set of more than hundred polymers[6-10]. This synergistic approach provides valuable insights into the roles of key molecular features (i.e., cohesive interactions, chain stiffness, and topology) influencing the of polymers, paving the way to establish a materials-by-design framework for polymeric materials [11-12]. By harnessing the power of this unprecedented computational efficiency provided by this novel framework, we can successfully predict properties of not only the polymeric materials, but also other classes of organic and inorganic materials

MOL2NET Conference highlights fragments of abstracts published in special issues of journals associated to the conference. This is a fragment of the abstract of the original article that belongs to: Big Data Analysis in Biomolecular Research, Bioinformatics, and Systems Biology with Complex Networks and Multi-Label Machine Learning Models), Biomolecules 2020, 10(4), 503; https://doi.org/10.3390/biom10040503.

Fragment: Exploring Alzheimer’s Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures. Despite huge investments and major efforts to develop remedies for Alzheimer’s disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients. Here we explore the molecular heterogeneity among a large cohort of AD and non-demented brain samples, aiming to address the question whether AD-specific molecular biomarkers can progress our understanding of the disease and advance the development of anti-AD therapeutics. We studied 951 brain samples, obtained from up to 17 brain regions of 85 AD patients and 22 non-demented subjects. Utilizing an information-theoretic approach, we deciphered the brain sample-specific structures of altered transcriptional networks. Our in-depth analysis revealed that 7 subnetworks were repetitive in the 737 diseased and 214 non-demented brain samples. Our results emphasize the need to expand the biomarker-based stratification to patient-specific transcriptional signature identification for improved AD diagnosis and for the development of subclass-specific future treatment.

This is a fragment of the original article that belongs to: Special Issues (Associated to Mol2Net): Big Data Analysis in Biomolecular Research, Bioinformatics, and Systems Biology with Complex Networks and Multi-Label Machine Learning Models, https://www.mdpi.com/journal/biomolecules/special_issues/big_data_analysis_biomolecular

Reference (Read Full Paper Free): Exploring Alzheimer’s Disease Molecular Variability via Calculation of Personalized Transcriptional Signaturesby Hila Dagan ,Efrat Flashner-Abramson ,Swetha Vasudevan ,Maria R. Jubran ,Ehud Cohen andNataly Kravchenko-BalashaBiomolecules 2020, 10(4), 503; https://doi.org/10.3390/biom10040503 - 26 Mar 2020,

MOL2NET Conference highlights fragments of abstracts published in special issues if journals associated to the conference. This is a fragment of the abstract of the original article that belongs to:

Big Data Analysis in Biomolecular Research, Bioinformatics, and Systems Biology with Complex Networks and Multi-Label Machine Learning Models), Biomolecules 2020, 10(1), 26; https://doi.org/10.3390/biom10010026 - 23 Dec 2019

Fragment: Alignment-free (AF) methodologies have increased in popularity in the last decades as alternative tools to alignment-based (AB) algorithms for performing comparative sequence analyses. They have been especially useful to detect remote homologs within the twilight zone of highly diverse gene/protein families and superfamilies...

(This article belongs to the Special Issue Big Data Analysis in Biomolecular Research, Bioinformatics, and Systems Biology with Complex Networks and Multi-Label Machine Learning Models)

References

Reference (Read Full Paper Free): Biomolecules 2020, 10(1), 26; https://doi.org/10.3390/biom10010026

MOL2NET Conference highlights fragments of abstracts published in special issues if journals associated to the conference. This is a fragment of the abstract of the original article that belongs to: A Computational Toxicology Approach to Screen the Hepatotoxic Ingredients in Traditional Chinese Medicines: Polygonum multiflorum Thunb as a Case Studyby Shuaibing He ,Xuelian Zhang ,Shan Lu ,Ting Zhu ,Guibo Sun andXiaobo SunBiomolecules 2019, 9(10), 577; https://doi.org/10.3390/biom9100577 - 07 Oct 2019.

In recent years, liver injury induced by Traditional Chinese Medicines (TCMs) has gained increasing attention worldwide. Assessing the hepatotoxicity of compounds in TCMs is essential and inevitable for both doctors and regulatory agencies. However, there has been no effective method to screen the [...] Read more. (This article belongs to the Special Issue Big Data Analysis in Biomolecular Research, Bioinformatics, and Systems Biology with Complex Networks and Multi-Label Machine Learning Models)

Geoid heights are important for converting terrain elevation from one reference system to another. Different space agencies have developed digital elevation model (DEM) products, which are commercially as well as accessible openly for the earth or its regions in different vertical datums. These DEMs are commonly available in either EGM96 or WGS84 datum. The shape of the geoid(s) developed over time have been derived using approximation of spherical harmonics. Geoid height plays an important role during comparison, validation and utilization of these DEMs. In this study, geoid heights (N) were calculated for EGM84, EGM96 and EGM 2008 using GeographicLib online service at locations of ground control points (GCPs) and analyzed. The mean geoid undulation for the three sites at Kendrapara, Orissa; Jaipur, Rajasthan and Dehradun, Uttarakhand are -62.92m, -50.24m and -44.02m respectively. Whereas the standard deviation for the three sites at Kendrapara, Orissa; Jaipur, Rajasthan and Dehradun, Uttarakhand are 0.27m, 0.46m and 1.22m respectively. The negative values of geoid heights in all the three experimental sites depicts negative gravity anomaly i.e. mass deficit, at these sites and thus indicating that in these regions the surface of the geoid is lower than the reference ellipsoid (WGS84). The resulting standard deviations also depict the increasing roughness of the experimental sites in the order: Kendrapara site, Jaipur site to maximum at Dehradun site.

Maritime surveillance has been an essential requirement since ancient times. However, in the absence of technology, it could not be done so effectively at that time as is being done in today's era of science and technology. The advancement in remote sensing technology has made maritime surveillance quicker and more precise. Ship detection and identification are playing a crucial role in the field of maritime surveillance in order to dealing with sea border activity, illegal fishery, maritime traffic, illegal migration of humans, navy movements or oil spill detection and monitoring. The information provided by imaging radar is fundamentally different from sensors that operate in infrared and visible portions of electromagnetic spectrum. SAR images are found to be very much suitable for the identification of sea objects because of very bright appearance of sea objects in SAR images against dark sea surface in background. In this work RISAT-1 SAR image of Mumbai offshore region (acquired on September 15, 2016) and Radarsat-2 SGF W2 mode of Vancouver, Canada (acquired on August 14, 2008) has been used for the rapid detection of ship objects using CFAR (Constant False Alarm Rate) algorithm technique provided by SNAP (Sentinel Application Platform) software. Presence of detected ship objects has also been demonstrated using kurtosis graph generated from azimuth and range FFT (Fast Fourier Transform) images of final ship detection resulted images of each study area.

Determining the rate of student attendance is an important task in determining the completion of the courses. Despite the success of the technology, it is unfortunate that in many academic institutions, the current systems used to detect student absences. Furthermore, one of the crucial problems in the attendance system does not count student background for continuing in the courses. In this paper, we propose an intelligent approach for calculating student attendance based on their Grade Point Average (GPA) and their activities, this approach uses Artificial Neural Network (ANN) for calculating the attendance rating accurately, meaning the system provide a new rating for each student based on their background. The aim of this research is developing an attendance system for motivation students taking attendance or taking high grade in the class. The result of this approach helps the instructor to allow students who have more activities with more absents to continue in the courses if not the students have low activity should taking high attendance. This system will more efficient for monitoring students for replacing absent to activity.

Background: since early times, the people of Morocco use medicinal plants as traditional medicine to treat diabetes. However, little studies have been made in the past to properly document and promote the traditional knowledge. This study was carried out in the Rif (North of Morocco), it aimed to identify medicinal plant used by the local people to treat diabetic problems, together with the associated ethnomedicinal knowledge.

Materials and Methods: The ethnomedical information collected was from 582 traditional healers using semi-structured interviews, free listing and focus group. Family use value (FUV), use value (UV), plant part value (PPV) and informant agreement ratio (IAR) were employed in data analysis. medicinal plant were collected, identified and kept at the natural resources and biodiversity laboratory, Ibn Tofail University, Kenitra.

Results: During the present study 30 medicinal plant species belonging to 14 families has been documented. The most frequent ailments reported were type 1 diabetes. The majority of the remedies were prepared from infusion. Leaves were the most frequently used plant part and Rosmarinus officinalis L. was the specie most commonly prescribed by local herbalists.

Conclusions: The results of this study showed that people Arabs and Imazighen living in the Rif of Morocco are still dependent on medicinal plants. The documented medicinal plants can serve as a basis for further studies on the regions medicinal plants knowledge and for future phytochemical and pharmacological studies.

Currently, antioxidant compounds, such as vitamins C and E, are widely used to deactivate free oxygen radical species (ROS), playing an essential role in preventing chronic neurodegenerative diseases where ROS levels are significantly increased ^1,2. Herein, single-walled carbon nanocones (SWCNC), a nanocarbon allotrope with unique physico-chemical properties, was combined with antioxidant vitamins C and E toward exploring mitochondrial nanomedicine applications in molecular neurosciences ^3,4.To this end, we carried out the study of SWCNCs interaction with vitamin C and vitamin E using ab initio calculations based on Density Functional Theory⁵.Besides, molecular docking methodology was applied by selecting the human ABC-mitochondrial carrier ABCB10, PDB ID: 4ayt to address the study of molecular interactions with the antioxidant vitamins C and E ⁶. The results obtained from ab initio study, showed that the most stable configuration was observed for the SWCNC interacting with vitamin C >> vitamin E, with DFT-binding energy of 0.98 and 0.56 eV, respectively. The results on molecular docking study provided a free binding energy (FEB) and rmsd for the neurotarget (4ayt) following the order of as: ABC-carrier/SWCNCs (-17.6 Kcal / mol and 0.931 Å)>>ABC-carrier/vitamin E (-5.4 Kcal / mol and 0.911 Å) and ABC-carrier/vitamin C (-4.5 Kcal / mol and 1.567 Å), and SWCNCs simultaneously interacting with vitamin E on ABC-carrier was -18 Kcal /mol and r.m.s.d = 0.079 Å). Lastly, the results suggest that the potential therapeutic combination of SWCNTs with vitamins E > C, could be a new and promising alternative for neurotherapy-based nanomedicine.

References

1. 1. Chambial S, Dwivedi S, Shukla KK, John PJ, Sharma P. Vitamin C in disease prevention and cure: An overview. Indian J Clin Biochem. Published online 2013. doi:10.1007/s12291-013-0375-3
2. Eerman K, Brodaty H. Tocopherol (vitamin E) in Alzheimer’s disease and other neurodegenerative disorders. CNS Drugs. Published online 2004. doi:10.2165/00023210-200418120-00005
3. Karousis N, Suarez-Martinez I, Ewels CP, Tagmatarchis N. Structure, Properties, Functionalization, and Applications of Carbon Nanohorns. Chem Rev. Published online 2016. doi:10.1021/acs.chemrev.5b00611
4. Shenderova OA, Lawson BL, Areshkin D, Brenner DW. Predicted structure and electronic properties of individual carbon nanocones and nanostructures assembled from nanocones. Nanotechnology. 2001;12(3):191-197. doi:10.1088/0957-4484/12/3/302
5. Soler JM, Artacho E, Gale JD, et al. The SIESTA method for ab initio order-N materials simulation. J Phys Condens Matter. Published online 2002. doi:10.1088/0953-8984/14/11/302
6. Oleg T, Arthur J. O. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. J Comput Chem. Published online 2010. doi:10.1002/jcc

Social media analytics plays an important role in e-commerce for retrieving the useful information of a product or service. Sentiment Analysis has become the key function of social media analytics. Opinion Mining is to used to analyze the polarity of sentiment expressed in data .Taking the data flood from online social media, in its many forms, and transforming it into useful knowledge for strategic decision making is the backbone of this paper. The paper proposes a model for doing customer review analytics on social media using big data for improving target advertising and improved business decision making.

Now a day’s most of the activities and financial transactions uses internet, since internet is accessible from anywhere, perpetrator takes advantage of this and commit a crime. Cybercrime is a term used to broadly describe criminal activity in which computers or computer networks are a tool, a target, or a place of criminal activity and include everything from electronic cracking to denial of service attacks. It is also used to include traditional crimes in which computers or networks are used to enable the illicit activity. Cyber criminals take full advantage of the anonymity, secrecy, and interconnections provided by the Internet. In this paper we have tried to provide information about Cyber crime, its nature, Perpetrators, Classification of cyber crime, Reasons for its emergence, In next section of this paper we have given an information about cyber law, IT legislation in India. Further in next section we have discuses about Cyber crime scenario in India. Finally Last two sections of this paper discuss about some cyber crime cases in India and some cyber crimes and punishments related with those crime.

Proceedings of the conference MOL2NET International Conference on Multidisciplinary Sciences (5th edition), 2019 is part of a year-round worldwide conference series hosted by MDPI Sciforum, Basel, Switzerland. This conference series has had organized more than 20 associated workshop series in universities worldwide: USA, France, Portugal, Spain, China, Chile, Brazil, India, etc. These workshop series run in person and/or online. Some of these workshops are the USINEWS-02 University of Minnesota, USA; MICROBIOTA, UDC, Coruña, Spain, LAWSCI-02, UPV/EHU, Bilbao, Spain, etc. Workshops allow both in person and/or online only publication of papers, research highlights of previous papers, letters, short reviews, etc. The topics are multidisciplinary covering, but not limited to, Chemistry (All areas), Physics, Biology, Ecology, Statistics, Bioinformatics, Education, Nanotechnology, Materials, Computational, Complex Networks, Legal, and Social sciences, etc. The present book of proceedings have been released in a short version without communications including links to online versions of all communications (only 31 pages). Thank you very much to all colleagues for your kind support.

Currently, many of the practical engineering and environmental applications require a digital elevation model (DEM) as an important scientific input causing variations in the quality of the results in an application depending on the accuracy of the DEM. The availability of fused or assimilated DEMs at global scale is a recent development strengthening the topographic studies and modeling of related phenomenon. Openly accessible EarthEnv-DEM90 is generated by fusion of ASTER GDEM2 and CGIAR-CSI v4.1 (SRTM 90m) products using rigorous techniques for enhanced quality under a collaborative project of NASA known as EarthEnv project. Whereas the other publicly available Multi-Error-Removed Improved-Terrain (MERIT) DEM is the product generated by Dai Yamazaki (University of Tokyo) using SRTM3 v2.1 and AW3D-30m v1 along with supplementary datasets available in different regions of the globe and primarily focusing it for global hydrodynamic modeling. In this study, EarthEnv-DEM90 and MERIT DEM are evaluated using ground control points (GCPs) acquired through differential global positioning system (DGPS) at three experimental sites with varied landforms and consequent topography. The resulting DEM statistics can assist an application scientist in selection of a suitable fused DEM products with improved accuracy for specific applications in comparison to individual DEM products. Mean error (ME), Mean absolute error (MAE) and root mean square errors (RMSE) were computed and revealed that MERIT DEM performs better in plains of Kendrapara site with RMSE as 4 m. However the EarthEnv-DEM90 achieves better accuracy in moderate topography at Jaipur site and rugged topography at Dehradun site with RMSE of 3.05m and 6.55m respectively.

A new peptidase designated as SAPV produced from a moderately halophilic Virgibacillus natechei sp. nov., strain FarD^T was investigated by purification to homogeneity followed by biochemical and molecular characterization purposes. Through optimization, it was determined that the optimum peptidase activity to be 16,000 U/mL in the optimized liquid medium that contains only white shrimp shell by-product as sole energy and carbon sources. The SAPV enzyme is a monomer protein with a molecular mass of 31 kDa. The sequence of its NH₂-terminal amino-acid residues showed homology with those of Bacillus peptidases S8/S53 superfamily. The SAPV showed optimal activity at pH 9 and 60 °C. The sapV gene was cloned, sequenced, and heterologously over expressed in the extracellular fraction of E. coli BL21(DE3)pLysS. The biochemical properties of the recombinant peptidase (rSAPV) were similar to those of native one. The highest sequence identity value (97.66%) of SAPV was obtained with peptidase S8 from Virgibacillus massiliensis DSM 28587, with 9 amino-acid residues of difference. Interestingly, rSAPV exhibited an excellent detergent stability and compatibility than Alcalase 2.4 L FG and Bioprotease N100L

Mediterranean sea is threatened by a large number of factors such as habitat alteration, urbanization, climate change, pollution and more specifically by the introduction of autochton and non-native species. Shrimps are caught all along the Tunisian coasts. They are found in the Gulf of Gabes area, wherein the largest concentrations of these species are located, especially the king shrimp Penaeus kerathurus. In addition, fishing with both trawlers and inshore fishing units have been recorded as relatively large quantities of another shrimp species, commonly known as white or speckled shrimp, Metapenaeus monoceros. The efficiency of the proteolytic strain Anoxybacillus kamchatkensis M1V in the fermentation of speckled shrimp by-product was investigated for the recovery of a deproteinized bioactive hydrolysate. The biological activities of the resulting hydrolysate were also examined by applying several antioxidant and enzyme inhibitory assays. The strain M1V was found to produce high level of protease activity (2,000 U/mL) when grown in media containing only shrimp powder at 25 g/L. The obtained hydrolysate showed a significant enzymatic inhibitory potential against acetylcholinesterase, tyrosinase, amylase, and angiotensin I convertase, and a strong antioxidant activity.

The high world energy demand has caused sustained growth in the use of fossil fuels, depleting its reserves, raising the cost of energy in many places, and contributing significantly to climate change [1]. Biomass is considered a sustainable energy source by having a net neutral production of carbon dioxide [2], such that it can partially replace fossil fuels. One of the ways of using biomass is to obtain new energy vectors, such as hydrogen, whose production from residual biomass is technically and economically feasible [3]. On the other hand, the Aspen Plus simulation software has been used in various petrochemical processes, such as methanol synthesis, indirect liquefaction and hydrogasification of coal, combined cycles in power plants; however, its application in biomass transformation processes has been limited [4]. Therefore, in this research the gasification process of palm kernel shell (PKS) was modeled and simulated at steady-state using Aspen Plus, varying the temperature (750 to 950 °C) and the steam/biomass ratio (S/B) between 0 and 2.5 (w/w), to determine its effect on the production of H₂ present in the syngas. The kinetic parameters of the gasification were determined by means of a thermogravimetric analysis (TG/DTG) using two gasifying agents (CO₂ and steam) and applying three semi-empirical kinetic models to interpret the experimental results (linear model, grain model, and volumetric model). Linear model and grain model have the best fit with the experimental results of PKS gasification with steam and CO₂, with R² values of 0.966 and 0.965, respectively. The simulation allowed obtaining results with a good fit with the experimental data (RMSE 0.135) and with greater precision compared to another model simulated in Aspen Plus (RSME 0.282) [4]. The yield of H₂ production as a function of temperature and S/B ratio was estimated by a multiple linear regression model, obtaining that its production oscillates between 80 and 109 g H₂/kg PKS, reaching its maximum peak at 950 °C and an S/B ratio of 0, and the minimum production at a temperature of 700 °C and an S/B ratio of 2.5.

References
1. BP Global Organization, (2015) BP Statistical review of world energy, London.
2. Marrugo, G. et al. (2016) Characterization of colombian agroindustrial biomass residues as energy resources. Energy and Fuels 30, 8386–98.
3. Li, J. et al. (2009) H2 rich gas production by steam gasification of palm oil wastes over supported tri-metallic catalyst. Int J Hydrog Energy 34, 9108–15.
4. Nikoo, M. and Mahinpey N. (2008) Simulation of biomass gasification in fluidized bed reactor using Aspen Plus. Biomass and Bioenergy 32, 1245–54.

The vitamin D receptor is a common target for various drugs, and is of great interest due to the protective function that this vitamin exerts on the body. The presence of single nucleotide polymorphisms (SNPs) in this receptor can affect the binding of the drug, which makes its analysis important. Through chemoinformatic studies, Perturbation Theory Machine Learning Information Fusion (PTMLIF) models that analyze this interaction can be established, for which the drug data set was downloaded from the public databases ChEMBL and NCBI (National Center for Biotechnology Information) and was subsequently performed the fusion of information. The database included 26064 trials with 47 different properties and 376 SNPs. In the present study, the deviations of the reference drug with respect to the perturbation operators were measured. The best model obtained showed values ​​of Sp = 72.62%, Sn = 89.54% and Ac = 83.85% for training and Sp = 74.78% Sn = 90.88% and Ac = 85.31% for validation for a given application domain.

Abstract

The main objective of the present work was to study the aptitude to drying of Pulicaria odora L. leaves pre-coated in functional gels. This involves drying in the open air leaves of P.odora pre-coated in a carrageenan gel, a carrageenan-based gel added to Z. jujuba Mill peel powder. In addition, the evaluation of certain properties (physicochemical, rheological and biological) of the obtained powders was carried out.

The obtained results show that coating has a very positive effect on the rheological properties of P. odora leaves powder (small grain size, good flow, and little swelling). It also promotes the conservation and release of bioactive substances (polyphenols and flavonoids). Indeed, the best extraction rates were obtained for the powder from the leaves coated in carrageenan gel based Z. jujuba Mill peel powder with levels of 1099.996 ± 8.545 mg EAG /g d.b and 141.336 ± 0.89 mg quercetin / g d.b) respectively for total polyphenols and flavonoids. Finally, the test for the antimicrobial activity of the total polyphenol extracts of the obtained powders reveals that they are effective against Gram-positive bacteria (S.aureus ATCC 25923) and Gram-negative bacteria (E.coli ATCC 25322) and yeast (C.albicans). The ethanolic extract of the powder from the leaves coated in the carrageenan gel based Z.jujuba Mill peel powder has an inhibition diameter of 24.5 ± 0.15 mm with respect to C.albicans.

We provide a method for experimental data smoothing under a certain noise by using a statistical fitting considering gaussian weight functions. On the one hand, this method is quite useful when we have a large amount of experimental data, which are expected to approach an unknown theoretical curve. This allows us to find quite closely the derivative of the theoretical curve from the data and provides as well the error in the numerical integration of the data. On the other hand, the proposed method improves the typical smoothening of the time series of financial data and allows the calculation of the volatility as a function of time.

Abstract

The allosteric modulator performs the function of allosteric regulation, which indirectly increases or decreases the effect of an agonist or antagonist on a cellular receptor by activating a catalytic site on the protein[1]. Allostery can both cause diseases and this involves synthesizing drugs with higher selectivity and less toxicity, to fit into the primary active center (orthosteric) of the biological objectives, in order to induce a therapeutic effect. [2] In this study we have employed Perturbation Theory (Pt) ideas and Machine Learning techniques (ML) to seek a PTML model of the ChEMBL database for allosteric modulators. In this case, the Linear Discriminant Analysis (LDA) has been used to develop this model. This aims to predict the probability of allosteric activity for more than 20000 preclinical tests, leading to very good results of statistical parameters: Specificity Sp = 87.61 / 87.51% and sensitivity Sn = 75.18 / 75.35 % in training / validation series.

[1] Monod, J.; Wyman, J.P.: On the nature of allosteric transitions: A plausible model. Journal of Molecular Biology 1965, 12, 88-118.

[2] Nussinov, R.; Tsai, C. J.: Allostery in disease and in drug discovery. Cell 2013, 153, 293-305.

The number of possible designs of nano-systems is elevated. The design depends on the function we need to develop. Among these systems we highlight Nanoparticle Drug Delivery Systems (DDNS) of high interest not only for Nanotechnology but also for Biomaterials science.^1–3

In this work we fusion the following information: 1) Drug-vitamin release nano-systems (DVRNs). This data set was collected from literature. 2) Vitamin derivatives data set extracted from ChEMBL database. Both data sets contain different assay conditions and molecular descriptors. Once we fusion the information, we apply Perturbation Theory Machine Learning (PTML) method in order to build the model. Once built with Perturbation Theory Operators (PT Operators), it presents both Specificity and Sensibility higher than 80%.

Until the best of our knowledge, we developed the first multi-label PTML model useful to design DVRNs for optimal biological activity.

Due to the high rate of resistance and the frequent relapse after treatment, Mexican

Leishmania, the causative agent of cutaneous leishmaniasis in countries such as Mexico and

Central America, constitutes a health problem and the search for new therapies is necessary.

Hydroxyurea, a cancer drug, has been shown to be effective in stopping the main cell cycle

of Leishmania. Martínez-Rojano H and collaborators carried out a study where said drug was

tested in an in vitro model of infection in macrophages. Meglumine antimony, standard

pharmacological treatment for Leishmania mexicana, was used as a reference under the same

experimental conditions. The hydroxyurea completely eliminated the Leishmania parasites

when used at a dose of 10 or 100 microg / ml, with a difference in the duration of treatment

of 9 and 3 days respectively. More recent studies have shown that 2 and 3-hydroxypyridine

hydroxyalkyl and acyloxyalkyl derivatives show inhibitory activity against the growth of

Mexican Leishmania. García Liñares G and collaborators obtained thirty new compounds by

means of a chemoenzymatic methodology in two reaction stages. The influence of

parameters such as enzyme source, acylating agent / substrate ratio, enzyme / substrate ratio,

solvent and temperature on the enzymatic reaction was evaluated. Acetylated derivatives

showed greater efficacy in inhibiting the growth of Mexican Leishmania. On the other hand,

Mendoza-Martínez C synthesized a series of quinazoline-2,4,6-triamine and evaluated it in

vitro against Leishmania mexicana. N (6) - (Ferrocenmethyl) quinazolin-2,4,6-triamine (H2)

showed activity in intracellular promastigotes and amastigotes, in addition to low

cytotoxicity in mammalian cells. The study showed the importance of the ferrocene nucleus

and the heterocyclic nucleus for the observed activity, in addition to indicating that the

mechanism of action involves redox reactions due to the easy oxidation of H2.

Degenerative neurological diseases have become serious risks to human health. These diseases depend on age and are becoming more common today, as the number of older people in society increases. The discovery of new drugs for the treatment of neurodegenerative diseases such as Alzheimer's, Parkison’s, and Huntington's diseases, Friedreich ataxia and others is an important goal of medicinal chemistry. For this reason, it is very useful to use the existing public information on preclinical assays with a high number of combinations of experimental conditions to create models that allow predicting new compounds useful for the treatment of these diseases. ChEMBL is a chemical database of bioactive molecules with drug-like properties. This database manages Big Data feature with a complex data set, which is hard to organize. This makes information difficult to analyze due to a big number of characteristics described in order to predict new drug candidates for neurodegenerative diseases. In this context, we propose to combine perturbation theory (PT) ideas and machine learning (ML) modeling to solve this combinatorial-like problem. The PT operators used are founded on multi-condition moving averages, combining different features and simplifying the difficulty to manage all data. For the construction of this model, the structure of the drug, the sequence of the proteins with which these drugs interact, the protein interaction network and the brain region in which these proteins are expressed were considered. The bondaring conditions that were taken into account were: the activity of the drug, the cell line in which the drug was tested, the brain region and the test organism. The developed PTML model reached considerable values in sensibility (80.89% for training and 80.94% for validation), specificity (80.18% for training and 80.33% for validation), and accuracy (80.25% for training and 80.39% for validation). We can conclude that this PTML model is the first one that can predict the activity of drug candidate compounds against degenerative neurological diseases taking into account the structure of the drug, the sequence of the proteins with which these drugs interact, the protein interaction network and the brain region in which these proteins are expressed.

The Quantitative Structure-Activity Relationship (QSAR) models are a very useful tool in the design of new chemical compounds. The QSAR methods are based on the assumption that the activity of a certain chemical compound is related to its structure. Two types of QSAR analysis are summarized in this review: Linear Regression model (LR) and Linear Discriminant Analysis model (LDA).

The main objective of this project is the development of a useful computational tool for future preclinical trials. The implemented model will be a PTML-LFER model, for the prediction of the pharmacological activity of a certain molecule, or list of molecules, under multiple test conditions.

Using the solid-state method, we were synthesized ZnO/ZnNb₂O₆ composite. The elaborated composite was characterized by X-ray diffraction and impedance spectroscopy. The XRD patterns show the co-existence of a hexagonal ZnO and an orthorhombic ZnNb₂O₆ structures. Then, the dielectrically properties were investigated at room temperature in a wide range of frequencies (from 20 Hz to 1MHz). Moreover, the composite nyquist curve revealed the contribution of grain and grain-boundaries. Indeed, theoretical fit exhibits high resistance, capacitive behavior, high permittivity and low loss factor. So this composite is a good candidate for super capacitor for energy storage application.