Study based on electronic descriptors of the diastereoselective aza-diels-alder cycloaddition of [(1r)-10-(n,n-diethylsulfamoyl)isobornyl] 2h-azirine-3-carboxylate to e,e-1,4-diacetoxy-1,3-butadiene

Cycloaddition of chiral [(1R)-10-(N,N-diethylsulfamoyl)isobornyl] 2H-azirine-3-carboxylate to E,E-1,4-diacetoxy-1,3-butadiene shows complete diatereoselectivity giving a single cycloadduct (-)-(2S,5R,6R)-6-[(1R)-10-(N,N-diethylsulfamoyl)isobornyloxycarbonyl]-1-azabicyclo[4.1.0]hept-3-ene-2,5-diyldiacetate. Our main objective is to identify electronic/steric parameters capable of describing the observed tendencies of this reaction. The results of the calculations conclude that: even though the steric factors can play an important role at the initial steps of the reaction, at the transition states the behavior of several electronic parameters; like hardness, polarizability, aromaticity, charge transfer, etc is decisive enough to justify the obtained product. Finally, this work summarizes an exhaustive analysis of electronic descriptors and empirical reactivity principles, reaching a definitive and comprehensive explanation to the observed experimental result.Thanks to Fundação para a Ciência e Tecnologia (FCT) for partial financial support (project PTDC/QUI/67407/2006

Surveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifiers

Abstract Background: The development of new ortholog detection algorithms and the improvement of existing ones are of major importance in functional genomics. We have previously introduced a successful supervised pairwise ortholog classification approach implemented in a big data platform that considered several pairwise protein features and the low ortholog pair ratios found between two annotated proteomes (Galpert, D et al., BioMed Research International, 2015). The supervised models were built and tested using a Saccharomycete yeast benchmark dataset proposed by Salichos and Rokas (2011). Despite several pairwise protein features being combined in a supervised big data approach; they all, to some extent were alignment-based features and the proposed algorithms were evaluated on a unique test set. Here, we aim to evaluate the impact of alignment-free features on the performance of supervised models implemented in the Spark big data platform for pairwise ortholog detection in several related yeast proteomes. Results: The Spark Random Forest and Decision Trees with oversampling and undersampling techniques, and built with only alignment-based similarity measures or combined with several alignment-free pairwise protein features showed the highest classification performance for ortholog detection in three yeast proteome pairs. Although such supervised approaches outperformed traditional methods, there were no significant differences between the exclusive use of alignment-based similarity measures and their combination with alignment-free features, even within the twilight zone of the studied proteomes. Just when alignment-based and alignment-free features were combined in Spark Decision Trees with imbalance management, a higher success rate (98.71%) within the twilight zone could be achieved for a yeast proteome pair that underwent a whole genome duplication. The feature selection study showed that alignment-based features were top-ranked for the best classifiers while the runners-up were alignment-free features related to amino acid composition. Conclusions: The incorporation of alignment-free features in supervised big data models did not significantly improve ortholog detection in yeast proteomes regarding the classification qualities achieved with just alignment-based similarity measures. However, the similarity of their classification performance to that of traditional ortholog detection methods encourages the evaluation of other alignment-free protein pair descriptors in future research.This work was supported by the following financial sources: Postdoc fellowship (SFRH/BPD/92978/2013) granted to GACh by the Portuguese Fundação para a Ciência e a Tecnologia (FCT). AA was supported by the MarInfo – Integrated Platform for Marine Data Acquisition and Analysis (reference NORTE-01-0145-FEDER-000031), a project supported by the North Portugal Regional Operational Program (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF)

Unify Markov model for Rational Design and Synthesis of More Safe Drugs. Predicting Multiple Drugs Side Effects

The 9th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryMost of present mathematical models for rational design and synthesis of new drugs consider just the molecular structure. In the present article we pretend extending the use of Markov Chain models to define novel molecular descriptors, which consider in addition other parameters like target site or biological effect. Specifically, this model takes into consideration not only the molecular structure but the specific biological system the drug affects too. Herein, it is developed a general Markov model that describes 19 different drugs side effects grouped in 8 affected biological systems for 178 drugs, being 270 cases finally. The data was processed by Linear Discriminant Analysis (LDA) classifying drugs according to their specific side effects, forward stepwise was fixed as strategy for variables selection. The average percentage of good classification and number of compounds used in the training/predicting sets were 100/95.8% for endocrine manifestations(18 out of 18)/(13 out of 14); 90.5/92.3% for gastrointestinal manifestations (38 out of 42)/(30 out of 32); 88.5/86.5% for systemic phenomena (23 out of 26)/(17 out of 20); 81.8/77.3% for neurological manifestations (27 out of 33)/(19 out of 25); 81.6/86.2% for dermal manifestations (31 out of 38)/(25 out of 29); 78.4/85.1% for cardiovascular manifestation (29 out of 37)/(24 out of 28); 77.1/75.7% for breathing manifestations (27 out of 35)/(20 out of 26) and 75.6/75% for psychiatric manifestations (31 out of 41)/(23 out of 31). Additionally a Back-Projection Analysis (BPA) was carried out for two ulcerogenic drugs to prove in structural terms the physic interpretation of the models obtained. This article develops a model that encompasses a large number of drugs side effects grouped in specifics biological systems using stochastic absolute probabilities of interaction (Apk (j)) by the first time

TI2BioP — Topological Indices to BioPolymers. A Graphical– Numerical Approach for Bioinformatics

We developed a new graphical–numerical method called TI2BioP (Topological Indices to BioPolymers) to estimate topological indices (TIs) from two-dimensional (2D) graphical approaches for the natural biopolymers DNA, RNA and proteins The methodology mainly turns long biopolymeric sequences into 2D artificial graphs such as Cartesian and four-color maps but also reads other 2D graphs from the thermodynamic folding of DNA/RNA strings inferred from other programs. The topology of such 2D graphs is either encoded by node or adjacency matrixes for the calculation of the spectral moments as TIs. These numerical indices were used to build up alignment-free models to the functional classification of biosequences and to calculate alignment-free distances for phylogenetic purposes. The performance of the method was evaluated in highly diverse gene/protein classes, which represents a challenge for current bioinformatics algorithms. TI2BioP generally outperformed classical bioinformatics algorithms in the functional classification of Bacteriocins, ribonucleases III (RNases III), genomic internal transcribed spacer II (ITS2) and adenylation domains (A-domains) of nonribosomal peptide synthetases (NRPS) allowing the detection of new members in these target gene/protein classes. TI2BioP classification performance was contrasted and supported by predictions with sensitive alignment-based algorithms and experimental outcomes, respectively. The new ITS2 sequence isolated from Petrakia sp. was used in our graphical–numerical approach to estimate alignment-free distances for phylogenetic inferences. Despite TI2BioP having been developed for application in bioinformatics, it can be extended to predict interesting features of other biopolymers than DNA and protein sequences. TI2BioP version 2.0 is freely available from http://ti2biop.sourceforge.net/

Markovian Chemicals “in silico” Design (MARCH-INSIDE), a Promising Approach for Computer-Aided Molecular Design III: 2.5D Indices for the Discovery of Antibacterials

The 9th International Electronic Conference on Synthetic Organic Chemistry session Computational ChemistryThe present work continues our series on the use of MARCH-INSIDE molecular descriptors [parts I and II: J. Mol. Mod. (2002) 8: 237-245 and (2003) 9: 395-407]. These descriptors encode information regarding to the distribution of electrons in the molecule based on a simple stochastic approach to the idea of electronegativity equalization (Sanderson’s principle). Here, 3D-MARCH-INSIDE molecular descriptors for 667 organic compounds are used as input for a Linear Discriminant Analysis. This 2.5D-QSAR model discriminates between antibacterial compounds and non-antibacterial ones with a 92.9 % of accuracy in training sets. On the other hand, the model classifies correctly 94.0 % of the compounds in test set. Additionally, the present QSAR performs similar-to-better than other methods reported elsewhere. Finally, the discovery of a novel compound illustrates the use of the method. This compound, 2-bromo-3-(furan-2-yl)-3-oxo-propionamide have MIC50 of 6.25 and 12.50 µg/mL against Ps. Aeruginosa ATCC 27853 and E. Coli ATCC 27853 respectively while ampicillim, amoxicillim, clindamycin, and metronidazole have, for instance, MIC50 values higher 250 µg/mL against E. Coli. Consequently, the present method may becomes a useful tool for the in silico discovery of antibacterialsWe thank the Spanish Ministry of Science and Technology (SAF2003-02222), for partial financial support. Molina RR, Castañedo C, and Almeida SM, acknowledges support from the Universität Rostock, German

An Alignment-Free Approach for Eukaryotic ITS2 Annotation and Phylogenetic Inference

The ITS2 gene class shows a high sequence divergence among its members that have complicated its annotation and its use for reconstructing phylogenies at a higher taxonomical level (beyond species and genus). Several alignment strategies have been implemented to improve the ITS2 annotation quality and its use for phylogenetic inferences. Although, alignment based methods have been exploited to the top of its complexity to tackle both issues, no alignment-free approaches have been able to successfully address both topics. By contrast, the use of simple alignment-free classifiers, like the topological indices (TIs) containing information about the sequence and structure of ITS2, may reveal to be a useful approach for the gene prediction and for assessing the phylogenetic relationships of the ITS2 class in eukaryotes. Thus, we used the TI2BioP (Topological Indices to BioPolymers) methodology [1], [2], freely available at http://ti2biop.sourceforge.net/ to calculate two different TIs. One class was derived from the ITS2 artificial 2D structures generated from DNA strings and the other from the secondary structure inferred from RNA folding algorithms. Two alignment-free models based on Artificial Neural Networks were developed for the ITS2 class prediction using the two classes of TIs referred above. Both models showed similar performances on the training and the test sets reaching values above 95% in the overall classification. Due to the importance of the ITS2 region for fungi identification, a novel ITS2 genomic sequence was isolated from Petrakia sp. This sequence and the test set were used to comparatively evaluate the conventional classification models based on multiple sequence alignments like Hidden Markov based approaches, revealing the success of our models to identify novel ITS2 members. The isolated sequence was assessed using traditional and alignment-free based techniques applied to phylogenetic inference to complement the taxonomy of the Petrakia sp. fungal isolate

Predicción de la afinidad de ligandos antagonistas por receptores de adenosina A2A usando árboles de decisión

Neurodegenerative diseases are being treated by modulating adenosine receptors with more effective, safe and selective antagonists. The objective of the study was to develop a methodology to obtain classification models based on decision tree algorithms and descriptors from 0D to 2D of non-congenital families of organic compounds to qualitatively predict ligand-RAA2A affinity. For this purpose, a non-congeneric database of 315 antagonists was constructed and cured with its inhibition constant in nano molar, labeled as potent and weak. The Dragon and ISIDA / QSPR programs were used to calculate molecular descriptors and five groups of descriptors were obtained. In each group 50 descriptors were selected using the mRMR criterion. The database was divided into Training, Test and External series through a random selection and a generalized k-means cluster analysis. Classifiers were developed and validated using the WEKA program. The results were analyzed using the statistical tests of Friedman and Wilcoxon. The significant influence of parameter m of algorithm J48 on the predictivity was verified for the models that used the descriptors of the aug.a-b and hyb.aug.a groups of ISIDA / QSPR. The best performance model was obtained from the selected descriptors of the ISIDA-all group with a value of m = 6 and reached 90.6% prediction on the External series. The methodology developed to obtain classification models based on decision tree algorithms and descriptors from 0D to 2D of non-congenital families of organic compounds is effective in qualitatively predicting ligand-RAA2A affinity with accuracy, specificity and selectivity greater than 90 %. Keywords: classification, machine learning, modeling, QSARLas enfermedades neurodegenerativas están siendo tratadas mediante la modulación de los receptores de adenosina con antagonistas más eficaces, seguros y selectivos. El objetivo del estudio consistió en desarrollar una metodología para obtener modelos de clasificación sobre la base de algoritmos de árboles de decisión y descriptores de 0D a 2D de familias no congenéricas de compuestos orgánicos para predecir cualitativamente la afinidad ligando-RAA2A. Para ello se construyó y curó una base de datos no congenérica de 315 antagonistas con su constante de inhibición en nano molar, etiquetados como potentes y débiles. Se utilizaron los programas Dragon e ISIDA/QSPR para calcular descriptores moleculares y se obtuvieron cinco grupos de descriptores. En cada grupo se seleccionaron 50 descriptores usando el criterio mRMR. La base de datos se dividió en series de Entrenamiento, Prueba y Externa mediante una selección aleatoria y un análisis de clúster k-means generalizado. Se desarrollaron y validaron clasificadores utilizando el programa WEKA. Los resultados fueron analizados mediante las pruebas estadísticas de Friedman y Wilcoxon. Se comprobó la influencia significativa del parámetro m del algoritmo J48 en la predictividad, para los modelos que usaron los descriptores de los grupos aug.a-b e hyb.aug.a del ISIDA/QSPR. El modelo de mejor desempeño se obtuvo de los descriptores seleccionados del grupo ISIDA-todos con un valor de m=6 y alcanzó 90.6% de predicción sobre la serie Externa. La metodología desarrollada para obtener modelos de clasificación sobre la base de algoritmos de árboles de decisión y descriptores de 0D a 2D de familias no congenéricas de compuestos orgánicos es efectiva para predecir cualitativamente la afinidad ligando-RAA2A con una exactitud, especificidad y selectividad superiores al 90%. Palabras clave: aprendizaje automatizado; clasificación; modelación; QSA

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

LA U INVESTIGA: Revista Científica. Facultad Ciencias de la Salud. Volumen 3. Número 1

Los ámbitos de investigación expuestos en este número corresponden a salud, educación, comunicación; temática relacionados íntimamente con la problemática social, que trata de visibilizarla a través de la difusión científica. Desde una visión de caso clínico, como son aquellos artículos investigados en los centros asistenciales del país; o de análisis colectivo evidente en los artículos elaborados por académicos de la Universidad Técnica del Norte.• Adenoma pleomorfo metastásico a mama y pulmón. • Evaluación clínica y atención temprana de la potencialidad cerebromotriz innata en los recién nacidos vivos con factores de riesgo neonatal del hospital general San Vicente de Paúl. • Embarazo ectopico cervical a proposito de un caso • Ganglio centinela en cáncer de mama uso de azul patente en unidades de segundo nivel. • Morbimortalidad en recién nacidos pretérminos menores de 36 semanas, hospital IESS Ibarra año 2014. • Ruptura esplénica asociada con preeclampsia severa presentacion de un caso. • Enfermedad de Addison de etiología tuberculosa: presentación de caso clínico. • Estado de la independencia funcional en personas con discapacidad del cantón Otavalo. • Síntesis analítica sobre las bondades medicinales de la jícama (smallanthus sonchifolius) 2015. • Prevención de infecciones puerperales con Churiyuyo (kalanchoe pinnata), una experiencia de las parteras tradicionales en Napo Ecuador. • Valoración de las habilidades comunicativas en la relación fisioterapeuta paciente. • Objeto de aprendizaje móvil en el aula, para estudiantes de la carrera de Nutrición y salud comunitaria, Universidad Técnica del Norte. • Infecciones de vías urinarias asociadas a catéter vesical en mujeres embarazadas. Hospital San Vicente de Paúl, 2015. • Proceso enfermero en la satisfacción de las necesidades básicas en usuario colecistectomizado. • Satisfacción laboral de los profesionales de enfermerí