On-line Method for Montelukast Determination in Bile Salt Medium with Multivariate Optimization and Fluorescent Detection

Journal of Analytical Chemistry - The present development involved a flow injection strategy using a mini-column of multiwalled carbon nanotubes and fluorescent detection (λex of 283 nm,...     

A type III porous-thermo-elastic problem with quasi-static microvoids

Meccanica - In this work we study, from the numerical point of view, a one-dimensional thermoelastic problem where the thermal law is of type III. Quasi-static microvoids are also assumed within...     

A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

In this review article, the authors discuss the use of machine learning algorithms as tools for the prediction of physical and chemical properties of ionic liquids.     

Synthesis and applications of graphene oxide aerogels in bone tissue regeneration: a review

Development of biocompatible porous supports is a promising strategy in the field of tissue engineering for the repair and regeneration of bone tissues with severe damage. Graphene oxide aerogels (GOAs) are excellent candidates for the manufacture of these systems due to their porosity, ability to imitate bone structure, and mechanical resistance, and according to their surface chemical reactivity, they can facilitate osseointegration, osteogenesis, osteoinduction and osteoconduction. In this review, synthesis of GOAs from the most primary source is described, and recent studies on the use of these functionalized carbonaceous foams as scaffolding for bone tissue regeneration are presented.     

Improved magnetosensor for the detection of hydrogen peroxide and glucose

Journal of Solid State Electrochemistry - In this work, the use of neodymium electrodes as a basis for the immobilization of magnetite nanoparticles has been carried out. The sensitivity and...     

The Cytotoxic Effect of α-Synuclein Aggregates

Parkinson's disease is a neurodegenerative disorder involving a functional protein, α-synuclein, whose primary function is related to vesicle trafficking. However, α-synuclein is prone to form aggregates, and these inclusions, known as Lewy bodies, are the hallmark of Parkinson's disease. α-synuclein can alter its conformation and acquire aggregating capacity, forming aggregates containing β-sheets. This protein's pathogenic importance is based on its ability to form oligomers that impair synaptic transmission and neuronal function by increasing membrane permeability and altering homeostasis, generating a deleterious effect over cells. First, we establish that oligomers interfere with the mechanical properties of 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) membrane, as demonstrated by nanoindentation curves. In contrast, nanoindentation revealed that the α-synuclein monomer's presence leads to a much more resistant lipid bilayer. Moreover, the oligomers’ interaction with cell membranes can promote lactate dehydrogenase (LDH) release, suggesting the activation of cytotoxic events.     

Identification of metabolic markers in patients with type 2 Diabetes by Ultrafast gas chromatography coupled to electronic nose. A pilot study

Metabolomics is a potential tool for the discovery of new biomarkers in the early diagnosis of diseases. An ultra-fast gas chromatography system equipped to an electronic nose detector (FGC eNose) was used to identify the metabolomic profile of Volatile Organic Compounds (VOCs) in type 2 diabetes (T2D) urine from Mexican population. A cross-sectional, comparative, and clinical study with translational approach was performed. We recruited twenty T2D patients and twenty-one healthy subjects. Urine samples were taken and analyzed by FGC eNose. Eighty-eight compounds were identified through Kovats's indexes. A natural variation of 30% between the metabolites, expressed by study groups, was observed in Principal Component 1 and 2 with a significant difference (p < 0.001). The model, performed through a Canonical Analysis of Principal coordinated (CAP), allowed a correct classification of 84.6% between healthy and T2D patients, with a 15.4% error. The metabolites 2-propenal, 2-propanol, butane- 2,3-dione and 2-methylpropanal, were increased in patients with T2D, and they were strongly correlated with discrimination between clinically healthy people and T2D patients. This study identified metabolites in urine through FGC eNose that can be used as biomarkers in the identification of T2D patients. However, more studies are needed for its implementation in clinical practice.