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.     

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.     

Doxorubicin Encapsulation in Carbon Nanotubes Having Haeckelite or Stone–Wales Defects as Drug Carriers: A Molecular Dynamics Approach

Doxorubicin (DOX), a recognized anticancer drug, forms stable associations with carbon nanotubes (CNTs). CNTs when properly functionalized have the ability to anchor directly in cancerous tumors where the release of the drug occurs thanks to the tumor slightly acidic pH. Herein, we study the armchair and zigzag CNTs with Stone–Wales (SW) defects to rank their ability to encapsulate DOX by determining the DOX-CNT binding free energies using the MM/PBSA and MM/GBSA methods implemented in AMBER16. We investigate also the chiral CNTs with haeckelite defects. Each haeckelite defect consists of a pair of square and octagonal rings. The armchair and zigzag CNT with SW defects and chiral nanotubes with haeckelite defects predict DOX-CNT interactions that depend on the length of the nanotube, the number of present defects and nitrogen doping. Chiral nanotubes having two haeckelite defects reveal a clear dependence on the nitrogen content with DOX-CNT interaction forces decreasing in the order 0N > 4N > 8N. These results contribute to a further understanding of drug-nanotube interactions and to the design of new drug delivery systems based on CNTs.     

In Vivo Biological Behavior of Polymer Scaffolds of Natural Origin in the Bone Repair Process

Autologous bone grafts, used mainly in extensive bone loss, are considered the gold standard treatment in regenerative medicine, but still have limitations mainly in relation to the amount of bone available, donor area, morbidity and creation of additional surgical area. This fact encourages tissue engineering in relation to the need to develop new biomaterials, from sources other than the individual himself. Therefore, the present study aimed to investigate the effects of an elastin and collagen matrix on the bone repair process in critical size defects in rat calvaria. The animals (Wistar rats, n = 30) were submitted to a surgical procedure to create the bone defect and were divided into three groups: Control Group (CG, n = 10), defects filled with blood clot; E24/37 Group (E24/37, n = 10), defects filled with bovine elastin matrix hydrolyzed for 24 h at 37 °C and C24/25 Group (C24/25, n = 10), defects filled with porcine collagen matrix hydrolyzed for 24 h at 25 °C. Macroscopic and radiographic analyses demonstrated the absence of inflammatory signs and infection. Microtomographical 2D and 3D images showed centripetal bone growth and restricted margins of the bone defect. Histologically, the images confirmed the pattern of bone deposition at the margins of the remaining bone and without complete closure by bone tissue. In the morphometric analysis, the groups E24/37 and C24/25 (13.68 ± 1.44; 53.20 ± 4.47, respectively) showed statistically significant differences in relation to the CG (5.86 ± 2.87). It was concluded that the matrices used as scaffolds are biocompatible and increase the formation of new bone in a critical size defect, with greater formation in the polymer derived from the intestinal serous layer of porcine origin (C24/25).     

GDME-based methodology for the determination of free formaldehyde in cosmetics and hygiene products containing formaldehyde releasers

Analytical and Bioanalytical Chemistry - Formaldehyde is often applied in the industrial production of different products, such as textiles, insulation materials, or cosmetics, due to its...     

The Problem of the Pointwise Fourier Inversion for Piecewise Smooth Functions of Several Variables

We study the pointwise convergence problem for the inverse Fourier transform of piecewise smooth functions, i.e., whether S_rD f (\bx) ? f (\bx)S_{\rho D} f (\bx) \to f (\bx) as r? ￥\rho \to \infty . r? ￥\rho \to \infty . Here for \bx,\bxi ? \Rn\bx,\bxi \in \Rn S_rDf(\bmx)=\dsf1(2p)^n/2\intli_rD [^(f)](\bxi) e^{\dst iá\bmx,\bxi?} d\bxi . S_{\rho D}f(\bm{x})=\dsf1{(2\pi)^{n/2}}\intli_{\rho D} \widehat{f}(\bxi) e^{\dst i\langle\bm{x},\bxi\rangle} d\bxi~. is the partial sum operator using a convex and open set DD containing the origin, and rD={ r\bxi:\bxi ? D }\rho D=\left\{ \rho \bxi:\bxi\in D \right\}.