This paper presents an investigation focused on the impact of muscle fatigue on a pattern recognition scheme for myoelectric control that uses three features sets and a Linear Discriminant Analysis classifier. Separability and repeatability between classes were used to evaluate the features changes while muscle fatigue was induced. Results show that while muscle fatigue is increasing over time, both separability and repeatability of the classes decrease. Finally two training schemes that use data acquired under fatigue, multiconditional training and selective classification, were evaluated using the Total Error Rate (TER). Results indicate that, when LDA classifier was trained whit no-fatigue, moderated fatigue and fatigue data, TER decreased to moderated and fatigue data, but increased to no-fatigue data. On the other hand, using three LDA classifiers to each of the condition, TER decreased to 9.26 % and 11 % in moderated fatigue and fatigue cases, while no-fatigue case was not affected.

The enzyme acetylcholinesterase (AChE) is currently a therapeutic target for the treatment of neurodegenerative diseases. These diseases have highly variable causes but irreversible evolutions. Although the treatments are palliative, they help relieve symptoms and allow a better quality of life, so the search for new therapeutic alternatives is the focus of many scientists worldwide. In this study, a QSAR-SVM classification model was developed by using the MATLAB numerical computation system and the molecular descriptors implemented in the Dragon software. The obtained parameters are adequate with accuracy of 88.63% for training set, 81.13% for cross-validation experiment and 81.15% for prediction set. In addition, its application domain was determined to guarantee the reliability of the predictions. Finally, the model was used to predict AChE inhibition by a group of quinazolinones and benzothiadiazine 1,1-dioxides obtained by chemical synthesis, resulting in 14 drug candidates with in silico activity comparable to acetylcholine.

In this study, a methodology is proposed, combining ligand- and structure-based virtual screening tools, for the identification of phosphorus-containing compounds as inhibitors of zinc metalloproteases. First, we use Dragon molecular descriptors to develop a Linear Discriminant Analysis classification model, which is widely validated according to the OECD principles. This model is simple, robust, stable and has good discriminating power. Furthermore, it has a defined applicability domain and it is used for virtual screening of the DrugBank database. Second, docking experiments are carried out on the identified compounds that showed good binding energies to the enzyme thermolysin. Considering the potential toxicity of phosphorus-containing compounds, their toxicological profile is evaluated according to Protox II. Of the five molecules evaluated, two show carcinogenic and mutagenic potential at small LD50, not recommended as drugs, while three of them are classified as non-toxic, and could constitute a starting point for the development of new vasoactive metalloprotease inhibitor drugs. According to molecular dynamics simulation, two of them show stable interactions with the active site maintaining coordination with the metal. A high agreement is evident between QSAR, docking and molecular dynamics results, demonstrating the potentialities of the combination of these tools.

Aqueous leaf extracts of Boldoa purpurascens are widely used because of their diuretic, natriuretic, antiurolithiatic, anti-inflammatory and antihypertensive properties. The major component of the extract is the flavonoid 4',5-dihydroxy-6,7-methylenedioxyflavonol-3-O-α-L-rhamnopyranosyl-(1→2)-β-D-xylopyranoside, but it is not known if this compound is responsible for the biological activity. The objective of this work is to develop effective in silico tools that allow predicting the possible activity of the flavonoid aglycone as an inhibitor of metalloproteases that regulate renal fluid excretion. First, a mathematical ligand-based classification model was developed, using an artificial intelligence and machine learning technique of support vector machines to find the relationship between chemical structure and biological activity. This showed good fit of the statistical parameters with an accuracy greater than 90%, offering a priori information of the flavonoid activity. Subsequently, the flavonoid aglycone was docked to the active site of the enzymes thermolysin (PDB: 6YMS), angiotensin-converting enzyme (PDB: 6TT4) and neprilysin (PDB: 6SUK) using the Extra Precision glide method (Glide-XP), showing conformations with binding energies lower than -5 Kcal/mol. In this study, possible interactions were determined at the catalytic site, where the coordination of negatively charged pharmacophoric groups with the zinc atom of these enzymes is observed. Finally, a preliminary in vivo evaluation was carried out using a diuresis-natriuresis model with sodium quantification in urine which revealed good activity profiles. These results are in correspondence with the ethnopharmacological use of the plant as a diuretic-natriuretic and for the treatment of hypertension.