The present work constitutes an approach to nanotechnology from the legal point of view. We present and analyze different definitions of nanomaterial - especially from the European Union and the International Organization for Standardization-. The aim is to assess the relevance of the definition given by the National Advisory Council of the Colombian Network of Nanoscience and Nanotechnology.

The discovery of new molecules with antimicrobial activity, and the understanding of the mechanisms of action, are important strategies against multiresistant pathogens. The positive enantiomer of α-pinene appears as an alternative to combat them, since it inhibits the growth of microorganisms, including strains of S. aureus, which gives the possibility of its use as an antimicrobial agent, isolated or in combination with other drugs. Therefore, the main goal of this study is to evaluate the modulating effect of (+)–α–pinene on the activity of synthetic antimicrobials that act on protein synthase and interfere with bacterial genetic material. The modulating effect of (+)–α–pinene on the activity of antibiotics for clinical use on some S. aureus strains was studied using the modified disc-diffusion method. The disks contain the following antimicrobials: Ciprofloxacin 5 μg, Tetracycline 30 μg, Nitrofurantoin 300 μg and Rifampicin 5 μg. 50 microliters of (+) - alpha-pinene (160 µL/mL) were added to the disks containing the antimicrobials to verify the modulating effect of monotrepene. Indifferent activity was observed in the association between the phytoconstituent and the four tested antimicrobials. Further studies using new methods in order to evaluate the antimicrobial activity of the association between the (+)–α–pinene and commercial antimicrobials are still needed.

In the present work, a software module has been developed that allows the selection of continuous variables from an Excel file, which are initially subjected to a process of verification and cleaning of the information, allowing the elimination of cases, or otherwise replacing outliers. With the average value, it is then possible to perform transformation operations such as Identity, exponential, absolute value, numerical power, logarithm, maximum and minimum probability, z-score, harmonic mean sum, Euclidean distance, in batch processing of continuous variables. In the end, the respective results can be obtained within a dataset that can be stored in CSV format, or in turn continue processing with PTML

Build a Dataset of structural and external variables of the Plasmodium Falciparum organism using the information collected from different databases such as ChEMBL, NCBI-GDV and Uniprot to predict the activity of the drug to the gene on the chromosome of this organism and determine if the orientation of the gene it influences this process. In this research work it is important to calculate the Shannon of Entropy in Canonical Smile, Protein Sequences and genes sequences.

USA-Europe Data Analysis Training School (USEDAT) is a Multi-center Trans-Atlantic initiative offering hands-on training focused in both Introduction to Experimental Data Recording (NMR, MS, IR, 2DGE, EEG, etc.) and/or posterior Computational Data Analysis (Machine Learning, Complex Networks, etc.). We made emphasis on applications in for Cheminformatics, Bioinformatics, Medicinal Chemistry, Nanotechnology, Systems Biology, Biomedical Engineering, etc. The school also promotes training and knowledge of ethical and legal regulatory issues (GDPR, REACH, OECD, FDA, etc.) about data use and data protection in chemistry and biomedical research. The school is directed to researchers and students worldwide.

Malaria causes one of the highest mortality rates worldwide. Malaria cases and malaria deaths are still increasing due to, among other factors, the resistance that the parasite has developed to treatments. New molecules have been studied to be used as treatment for this disease. The present study analyzed the antiplasmodial activity of the Aminobicyclo[2.2.2]octan-2-yl 4-aminobutanoates and their ethanoates and propanoates analogs using molecular topology to develop a quantitative structure-activity relation (QSAR) model. Linear discriminant analysis was used to find a mathematical statement able to classify 32 of 35 compounds accurately by their antiplasmodial activity. The model classified 82.35% of molecules considered active with experimental methods, and differentiated 100% of the inactive molecules as such. Multilinear regression analysis was applied to find an equation with the ability to predict the antiplasmodial activity of each compound in terms of pIC₅₀. Crossvalidation technique and randomness test were used to validate this model. After the analysis, new potential antiplasmodial molecules were suggested.

Data from preclinical assays of ChEMBL are obtained. This data is treated mathematically and a PTML-LDA model is obtained in which the conditions are analyzed individually. With this equation and the values of the descriptors of new molecules, the activity of the compound to be tested could be predicted.

Alzheimer's disease is the most common cause of dementia in elderly people. Currently there are near to 50 million cases of Dementia worldwide, among them 60-70 % correspond to Alzheimer’s disease. Unfortunately, the only diagnosis available is postmortem and there is no treatment or cure. However, studies by several authors have proposed Selenoamides as neuroprotective compounds, since they promote survival pathways on stress conditions. We cultured Dental Pulp Stem Cells (DPSCs) in DMEM/F12 with 10% fetal bovine serum, medium was changed every third day until they reached a confluence of approximately 70-80%, then we treated these cells with the Selenoamides compounds. Cells were fixed with paraformaldehyde (PFH) 4% for immunofluorescence; and protein was extracted for Western blot to detect mesenchymal, stem cell and neuronal markers, such as: CD73, CD13, CD105; and SOX2, OCT4, and Nanog, and b-III-Tubulin, respectively. Neuroprotection by Selenoamides compounds was measured with MTT viability assay. We found that one among seven Selenoamide compounds, showed significant effects on DPSCs survival, at relatively low concentration. Our results support the potential use of selenoamides as new therapeutics for Alzheimer´s disease.

Background

Cancer forms second-most cause of death in the USA next to cardio vascular diseases, contributing to significant economic and social burden. WHO reports suggest that, almost 18 millions cancer cases and 60 lakhs deaths from cancer is expected in the US in 2019 alone. The most common types such as lung, breast, prostate, and colorectal cancer are mostly prevalent in the United States and worldwide. Although knowledge of molecular diagnosis has improved in the recent years, advanced therapeutic approaches still needs to be addressed for the diseases like cancer. In the recent years, CRISPR Cas9 system has emerged as a powerful tool for genome editing in mammalian cells including humans which offers a great promise in cancer therapeutics. For instance, the immune cell can be modified to express certain antigenic receptors like CARs (Chimeric Antigen Receptors), which efficiently recognizes and kill cancer cell. CARs have shown to be faster and effective in killing tumour cells than any immune cells studied so far. The immune cell(T-cell) can be isolated from the venous blood and be genetically modified to effectively target cancer cells. CRISPR technology can be deployed to edit and insert T-Cell CAR at the target location of the genome where the variants occur or tumour gene identified. The study hence proposes the creation of highly potent T endowed with CAR expression, which can be further inserted in the patient's genome with retroviral delivery methods.

Next Generation Sequencing technology with targeted amplicon methods can be used to detect on-and off-target mutations and further establish phenotype-genotype relations. Several trained libraries and machine learning algorithms, such as Delphi, are available to predict a CRISPR cleavage and editing patterns. During the CRISPR genome editing, the endonucleases such as cas9 assisted by guide RNA, cleaves the target DNA. In the process, genetic material is stitched back together, later it may be inaccurate and in the absence of the template this process may loose its precise targeting. Artificial intelligence can therefore be deployed for faster and accurate CRISPR enabled genome editing, overcoming the pitfalls of non-specific genome targeting.

The main goal of precision cancer medicine is the accurate prediction of finest drug therapies depends on the personalized genetic profiles of patient tumors. Ideally, such predictions are depends on well-established genetic cause-and-reflex association that are disrupted in cancer cells. According to a statistical survey done at Harward medical school, In USA almost 17 various research studies were done using CRISPR to treat cancer. But most of those studies were used by genome editing technology to engineer immune cells to attack tumor cells. But recently researchers in china were rumored that they were directly use CRISPR to knock out viruses that cause cervical cancer which is not been done in yet.

The present research proposes a machine learning approach to exploit the large scale genomic data by NGS. The machine learning and deep learning methods can be effectively used to identify and determine the tumor immunogenicity which may pave for the development of a scoring system (immunophenoscore IPS algorithm) for immunogenicity quantification, hence, facilitatating for precision immuno-oncology therapy. The machine learning method can be also be used to classify the immune cell subtype based on gene set enrichment analysis. With random forest classification in machine learning and deep learning approaches, the study can further determine the immune checkpoint blockade response phenotypes of melanoma patients. The present study, hence, is pursued to apply machine learning algorithms for predicting CRISPR-Cas9 cleavage patterns concerted with CARs mechanism, which can be anticipated to provide better long-term prognosis of cancer patients in comparison to tumor grade.

Biofilm formation is considered a defense mechanism against environmental, chemical or biological factors. Some of the main bacteria with greater capacity for the formation of these structures are gram positive, in which Staphylococcus aureus stands out. This bacterium is implicated in a large number of diseases of a livestock nature, such as bovine mastitis, in which the ability to form a biofilm gives the bacteria a high resistance to chemical and antibiotic treatments, causing economic losses to increase. Therefore, the work seeks to identify in a molecular way the genes that are involved in the formation of biofilms, and to know if the bacteria possess one or more of said genes. The bacteria were isolated in the municipalities of Venustiano Carranza (AVC) and Marcos Castellanos (AMC), 39 and 15 respectively, for a total of 55 samples. Total DNA was extracted from all samples, its quality was verified by electrophoresis, to finally perform the genetic detection using endpoint PCR. A total of 10 genes were tested, of which their presence in bacteria with the ability to form biofilms was already proven. The ATCC isolate was used as a positive control. Only the icaD and icaA genes amplified, for a percentage of 27% and 24% respectively for the AMC isolates. In the case of AVC, the data was 41% for icaD and 37% for icaA. Comparing the data obtained with a work carried out by Toro Castillo in 2018, in which he sought the best biofilm formers with the same group of bacteria, it was found that bacteria with such capacity also had said ica genes, directly relating them to the formation of biofilms.