On the improvement of signal repeatability in laser-induced air plasmas

The relatively low repeatability of laser-induced breakdown spectroscopy (LIBS) severely hinders its wide commercialization. In the present work, we investigate the optimization of LIBS system for repeatability improvement for both signal generation (plasma evolution) and signal collection. Timeintegrated spectra and images were obtained under different laser energies and focal lengths to investigate the optimum configuration for stable plasmas and repeatable signals. Using our experimental setup, the optimum conditions were found to be a laser energy of 250 mJ and a focus length of 100 mm. A stable and homogeneous plasma with the largest hot core area in the optimum condition yielded the most stable LIBS signal. Time-resolved images showed that the rebounding processes through the air plasma evolution caused the relative standard deviation (RSD) to increase with laser energies of > 250 mJ. In addition, the emission collection was improved by using a concave spherical mirror. The line intensities doubled as their RSDs decreased by approximately 25%. When the signal generation and collection were optimized simultaneously, the pulse-to-pulse RSDs were reduced to approximately 3% for O(I), N(I), and H(I) lines, which are better than the RSDs reported for solid samples and showed great potential for LIBS quantitative analysis by gasifying the solid or liquid samples.     

Spin transfer torque in the semiconductor/ferromagnetic structure in the presence of Rashba effect

Spin transfer torque in magnetic structure occurs when the transverse component of the spin current that flows from the nonmagnetic medium to ferromagnetic medium is absorbed by the interface. In this paper, considering the Rashba effect on the semiconductor region, we discuss the spin transfer torque in semiconductor/ferromagnetic structure and obtain the components of spin-current density for two models:(i) single electron and(ii) the distribution of electrons. We show that no matter whether the difference in Fermi surface between semiconductor and Fermi spheres for the up and down spins in ferromagnetic increases, the transmission probability decreases. The obtained results for the values used in this article illustrate that Rashba effect increases the difference in Fermi sphere between semiconductor and Fermi sphere for the up and down spins in ferromagnetic. The results also show that the Rashba effect, brings an additional contribution to the components of spin transfer torque, which does not exist in the absence of the Rashba interaction. Moreover, the Rashba term has also different effects on the transverse components of the spin torque transfer.     

Comparative Study of the Synthetic Approaches and Biological Activities of the Bioisosteres of 1,3,4-Oxadiazoles and 1,3,4-Thiadiazoles over the Past Decade

The bioisosteres of 1,3,4-oxadiazoles and 1,3,4-thiadiazoles are well-known pharmacophores for many medicinally important drugs. Throughout the past 10 years, 1,3,4-oxa-/thiadiazole nuclei have been very attractive to researchers for drug design, synthesis, and the study of their potential activity towards a variety of diseases, including microbial and viral infections, cancer, diabetes, pain, and inflammation. This work is an up-to-date comparative study that identifies the differences between 1,3,4-thiadiazoles and 1,3,4-oxadiazoles concerning their methods of synthesis from different classes of starting compounds under various reaction conditions, as well as their biological activities and structure–activity relationship.     

Predicting Bitcoin (BTC) Price in the Context of Economic Theories: A Machine Learning Approach

Bitcoin (BTC)—the first cryptocurrency—is a decentralized network used to make private, anonymous, peer-to-peer transactions worldwide, yet there are numerous issues in its pricing due to its arbitrary nature, thus limiting its use due to skepticism among businesses and households. However, there is a vast scope of machine learning approaches to predict future prices precisely. One of the major problems with previous research on BTC price predictions is that they are primarily empirical research lacking sufficient analytical support to back up the claims. Therefore, this study aims to solve the BTC price prediction problem in the context of both macroeconomic and microeconomic theories by applying new machine learning methods. Previous work, however, shows mixed evidence of the superiority of machine learning over statistical analysis and vice versa, so more research is needed. This paper applies comparative approaches, including ordinary least squares (OLS), Ensemble learning, support vector regression (SVR), and multilayer perceptron (MLP), to investigate whether the macroeconomic, microeconomic, technical, and blockchain indicators based on economic theories predict the BTC price or not. The findings point out that some technical indicators are significant short-run BTC price predictors, thus confirming the validity of technical analysis. Moreover, macroeconomic and blockchain indicators are found to be significant long-term predictors, implying that supply, demand, and cost-based pricing theories are the underlying theories of BTC price prediction. Likewise, SVR is found to be superior to other machine learning and traditional models. This research’s innovation is looking at BTC price prediction through theoretical aspects. The overall findings show that SVR is superior to other machine learning models and traditional models. This paper has several contributions. It can contribute to international finance to be used as a reference for setting asset pricing and improved investment decision-making. It also contributes to the economics of BTC price prediction by introducing its theoretical background. Moreover, as the authors still doubt whether machine learning can beat the traditional methods in BTC price prediction, this research contributes to machine learning configuration and helping developers use it as a benchmark.     

Macrophage-Targeted Punicalagin Nanoengineering to Alleviate Methotrexate-Induced Neutropenia: A Molecular Docking,DFT, and MD Simulation Analysis

Punicalagin is the most bioactive pomegranate polyphenol with high antioxidant and free-radical scavenging activity and can potentially cure different ailments related to the cardiovascular system. The current research work was envisioned to predict the targeting efficiency of punicalagin (PG) nanoparticles to the macrophages, more specifically to bone marrow macrophages. For this, we selected mannose-decorated PLGA-punicalagin nanoparticles (Mn-PLGA-PG), and before formulating this nanocarrier in laboratory settings, we predicted the targeting efficiency of this nanocarrier by in silico analysis. The analysis proceeded with macrophage mannose receptors to be acquainted with the binding affinity and punicalagin-based nanocarrier interactions with this receptor. In silico docking studies of macrophage mannose receptors and punicalagin showed binding interactions on its surface. PG interacted with hydrogen bonds to the charged residue ASP668 and GLY666 and polar residue GLN760 of the Mn receptor. Mannose with a docking score of −5.811 Kcal/mol interacted with four hydrogen bonds and the mannose receptor of macrophage, and in PLGA, it showed a −4.334 Kcal/mol docking score. Further, the analysis proceeded with density functional theory analysis (DFT) and HOMO–LUMO analysis, followed by an extensive 100 ns molecular dynamics simulation to analyse the trajectories showing the slightest deviation and fluctuation. While analysing the ligand and protein interaction, a wonderful interaction was found among the atoms of the ligand and protein residues. This computational study confirms that this nanocarrier could be a promising lead molecule to regulate the incidence of drug-induced neutropenia. Furthermore, experimental validation is required before this can be stated with complete confidence or before human use.     

The Design of 3D-Printed Polylactic Acid–Bioglass Composite Scaffold: A Potential Implant Material for Bone Tissue Engineering

Bio-based and patient-specific three-dimensional (3D) scaffolds can present next generation strategies for bone tissue engineering (BTE) to treat critical bone size defects. In the present study, a composite filament of poly lactic acid (PLA) and 45S5 bioglass (BG) were used to 3D print scaffolds intended for bone tissue regeneration. The thermally induced phase separation (TIPS) technique was used to produce composite spheres that were extruded into a continuous filament to 3D print a variety of composite scaffolds. These scaffolds were analyzed for their macro- and microstructures, mechanical properties, in vitro cytotoxicity and in vivo biocompatibility. The results show that the BG particles were homogeneously distributed within the PLA matrix and contributed to an 80% increase in the mechanical strength of the scaffolds. The in vitro cytotoxicity analysis of PLA-BG scaffolds using L929 mouse fibroblast cells confirmed their biocompatibility. During the in vivo studies, the population of the cells showed an elevated level of macrophages and active fibroblasts that are involved in collagen extracellular matrix synthesis. This study demonstrates successful processing of PLA-BG 3D-printed composite scaffolds and their potential as an implant material with a tunable pore structure and mechanical properties for regenerative bone tissue engineering.     

Limoniastrum monopetalum–Mediated Nanoparticles and Biomedicines: In Silico Study and Molecular Prediction of Biomolecules

An in silico approach applying computer-simulated models helps enhance biomedicines by sightseeing the pharmacology of potential therapeutics. Currently, an in silico study combined with in vitro assays investigated the antimicrobial ability of Limoniastrum monopetalum and silver nanoparticles (AgNPs) fabricated by its aid. AgNPs mediated by L. monopetalum were characterized using FTIR, TEM, SEM, and DLS. L. monopetalum metabolites were detected by QTOF–LCMS and assessed using an in silico study for pharmacological properties. The antibacterial ability of an L. monopetalum extract and AgNPs was investigated. PASS Online predictions and the swissADME web server were used for antibacterial activity and potential molecular target metabolites, respectively. Spherical AgNPs with a 68.79 nm average size diameter were obtained. Twelve biomolecules (ferulic acid, trihydroxy-octadecenoic acid, catechin, pinoresinol, gallic acid, myricetin, 6-hydroxyluteolin, 6,7-dihydroxy-5-methoxy 7-O-β-d-glucopyranoside, methyl gallate, isorhamnetin, chlorogenic acid, 2-(3,4-dihydroxyphenyl)-5,7-dihydroxy-4-oxo-4H-chromen-3-yl 6-O-(6-deoxy-β-l-mannopyranosyl)-β-d-glucopyranoside) were identified. The L. monopetalum extract and AgNPs displayed antibacterial effects. The computational study suggested that L. Monopetalum metabolites could hold promising antibacterial activity with minimal toxicity and an acceptable pharmaceutical profile. The in silico approach indicated that metabolites 8 and 12 have the highest antibacterial activity, and swissADME web server results suggested the CA II enzyme as a potential molecular target for both metabolites. Novel therapeutic agents could be discovered using in silico molecular target prediction combined with in vitro studies. Among L. Monopetalum metabolites, metabolite 12 could serve as a starting point for potential antibacterial treatment for several human bacterial infections.     

The Physical Properties of P2O5–Sm2O3–MnO2 Glass System

Phosphate glasses doped with samarium oxide have been made using the melt quenching technique. The density of the glass was determined using the Archimedes method while the Vickers hardness was measured using a micro-vicker tester. Both density and hardness have shown an increasing trend with the addition of a small amount of Sm₂O₃. The refractive index was characterized using UV-visible spectroscopy and the Sellmeier fitting equation was used to verify the result. The refractive index was found to vary from 1.972 to 1.807 depending on the wavelength and well agreed with the fitting curve     

On site visual detection of <Emphasis Type="Italic">Porphyromonas gingivalis</Emphasis> related periodontitis by using a magnetic-nanobead based assay for gingipains protease biomarkers

Porphyromonas gingivalis (P. gingivalis) is a pathogen causing periodontitis. A rapid assay is described for the diagnosis of periodontal infections related to P. gingivalis. The method is making use of gingipains, a group of P. gingivalis specific proteases as a detection biomarker. Magnetic-nanobeads were labeled with gingipain-specific peptide substrates and immobilized on a gold biosensing platform via gold-thiol linkage. As a result of this, the color of the gold layer turns black. Upon cleavage of the immobilized substrates by gingipains, the magnetic-nanobeads-peptide fragments were attracted by a magnet so that the golden surface color becomes visible again. This assay is highly sensitive and specific. It is capable of detecting as little as 49 CFU·mL^?1 of P. gingivalis within 30 s. Examination of periodontitis patients and healthy control saliva samples showed the potential of the assay. The simplicity and rapidity of the assay makes it an effective point-of-care device.

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Graphical abstract Schematic of the assay for the detection of P. gingivalis proteases as one of the promising biomarkers associated with periodontal diseases.

     

Regiocontrolled synthesis of the trimeric quinone framework of conocurvone

[reaction: see text] The trimeric quinone framework of conocurvone is crucial for its potent anti-HIV activity. A new synthesis of trimeric quinones based on stepwise substitution of the halogens in 2,3-dihaloquinones by hydroxyquinone anions is described. Chlorinated biquinones are key intermediates that undergo regiospecific substitution reactions to yield trimeric quinone monomethyl ethers.