Synthesis and characterization of two new fluorescent macrocycles: A novel fluorescent chemosensor for zinc ion

Two new macrocyclic systems (L¹, L²) containing two emissive naphthalene were synthesized and characterized. The macrocycles were studied by ¹H NMR, ¹³C NMR, COSY, HMQC, DEPT, microanalysis and mass spectroscopy. The influence of metal cations Na⁺, Cr³⁺, Mn²⁺, Fe³⁺, Co²⁺, Ni²⁺, Cu²⁺, Zn²⁺, Cd²⁺, and Hg²⁺ on the spectroscopic properties of the macrocyclic systems in acetonitrile/DMF 9:1 (v/v) mixtures were investigated by means of absorption and emission spectrophotometry. The macrocycle L¹ was found to be as an effective fluorescence sensor for Zn²⁺ ions. Zn²⁺ and Cd²⁺ ions show the most effects on the fluorescence intensity of L².     

Synthesis of Monodisperse Lanthanum Hydroxide Nanoparticles and Nanorods by Sonochemical Method

In this work, monodisperse nanoparticles and nanorods of lanthanum hydroxide was synthesized from the reaction of lanthanum(III) nitrate and sodium hydroxide by sonochemical method. The effect of some of the parameters such as feeding rate of precursors, different solvents of reaction, time of sonication, and various surfactants on the particle size and morphology of products was studied. The as-prepared products were characterized by X-ray diffraction, field emission scanning electron microscopy, transmission electron microscopy.     

Assessment of flavor volatiles of modified Iranian rice cultivars during the gelatinization process

A combined GC-MS with the headspace solid-phase microextraction (SPME) method has been employed for the analysis of the flavor volatiles of two modified Iranian rice cultivars during gelatinization. In order to optimize the different experimental parameters, the effect of fiber composition, water content of the rice samples, and equilibrium time were investigated. As a result, while gelatinization progresses, the amount of volatile compounds would increase as well. Therefore, a broad range of the flavor volatiles of rice could be extracted, concentrated, and identified. Altogether, 54 and 66 components were identified for HD5 and HD6 rice samples, respectively, of which 33 unique compounds were not detected previously. The identified volatile components in the modified cultivars belong to the chemical classes of aldehydes, ketones, alcohols, and heterocyclic compounds, phenolic compounds, and hydrocarbons.     

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.     

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 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.     

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 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     

Impacts of Chemical Variables on the Encapsulated Corticoids in Poly-ε-caprolactone Nanoparticles and Statistical Biological Analysis

The goal of this research is to evaluate the impact of different parameters on corticosteroids entrapment in biocompatible poly-ε-caprolactone nanoparticles. These findings provide better insight on the designing carriers for drugs. Nanocapsules were synthesized by interfacial deposition and their morphology was determined by SEM. Drug entrapment efficiency and particle size distribution were assayed by HPLC and DLS, respectively. The samples were assessed for cytotoxicity using MTT reduction assay. The anti-inflammatory effect of the formulated drug was determined by induction of inflammation in treated as well as native laboratory animals. Statistical analysis of the data was performed using SPSS 18.0.