Novel tacrine-based acetylcholinesterase inhibitors as potential agents for the treatment of Alzheimer’s disease: Quinolotacrine hybrids

A new series of quinolotacrine hybrids including cyclopenta- and cyclohexa-quinolotacrine derivatives were designed, synthesized, and assessed as anti-cholinesterase (ChE) agents. The designed derivatives indicated higher inhibitory effect on the acetylcholinesterase (AChE) with IC₅₀ values of 0.285–100 µM compared to butyrylcholinesterase (BChE) with IC₅₀ values of?>?100 µM. Of these compounds, cyclohexa-quinolotacrine hybrids displayed a little better anti-AChE activity than cyclopenta-quinolotacrine hybrids. Compound 8-amino-7-(3-hydroxyphenyl)-5,7,9,10,11,12-hexahydro-6H-pyrano[2,3-b:5,6-c'] diquinolin-6-one (6m) including 3-hydroxyphenyl and cyclohexane ring moieties exhibited the best AChE inhibitory activity with IC₅₀ value of 0.285 µM. The kinetic and molecular docking studies indicated that compound 6m occupied both the catalytic anionic site (CAS) and peripheral anionic site (PAS) of AChE as a mixed inhibitor. Using neuroprotective assay against H₂O₂-induced cell death in PC12 cells, the compound 6h illustrated significant protection among the assessed compounds. In silico ADME studies estimated good drug-likeness for the designed compounds. As a result, these quinolotacrine hybrids can be very encouraging AChE inhibitors to treat Alzheimer’s disease.

Graphic abstract

A novel series of quinolotacrine hybrids were designed, synthesized, and evaluated against AChE and BChE enzymes as potential agents for the treatment of AD. The hybrids showed good to significant inhibitory activity against AChE (0.285–100 μM) compared to butyrylcholinesterase (BChE) with IC₅₀ values of?>?100 μM. Among them, compound 8-amino-7-(3-hydroxyphenyl)-5,7,9,10,11,12-hexahydro-6H-pyrano[2,3-b:5,6-c′] diquinolin-6-one (6 m) bearing 3-hydroxyphenyl moiety and cyclohexane ring exhibited the highest anti-AChE activity with IC₅₀ value of 0.285 μM. The kinetic and molecular docking studies illustrated that compound 6 m is a mixed inhibitor and binds to both the catalytic anionic site (CAS) and peripheral anionic site (PAS) of AChE.

     

Thermal conductivity modeling of MgO/EG nanofluids using experimental data and artificial neural network

The application of nanofluids in energy systems is developing day by day. Before using a nanofluid in an energy system, it is necessary to measure the properties of nanofluids. In this paper, first the results of experiments on the thermal conductivity of MgO/ethylene glycol (EG) nanofluids in a temperature range of 25–55 °C and volume concentrations up to 5 % are presented. Different sizes of MgO nanoparticles are selected to disperse in EG, including 20, 40, 50, and 60 nm. Based on the results, an empirical correlation is presented as a function of temperature, volume fraction, and nanoparticle size. Next, the model of thermal conductivity enhancement in terms of volume fraction, particle size, and temperature was developed via neural network based on the measured data. It is observed that neural network can be used as a powerful tool to predict the thermal conductivity of nanofluids.     

Numerical evaluation on thermal–hydraulic characteristics of dilute heat-dissipating nanofluids flow in microchannels

The present work deals with numerical investigations on heat transfer characteristics and friction factor of aqueous CuO nanofluids flow in a set of four microchannels connected in parallel under laminar regime. For each single phase, volume of fluid, mixture and Eulerian models, a particular computer code is developed to carefully simulate this problem. The three-dimensional steady-state governing equations are solved through finite volume method. The primary aim of this study is to comparatively distinguish the most appropriate and accurate model for numerical studies of nanofluids in microchannels. The results are compared with one another and the data obtained from an experimental work. Regarding the results, an acceptable consistency is observed for all models with the experimental data. The current study truly demonstrates that applying single-phase model to simulate and evaluate the laminar flow of CuO–water nanofluid inside microchannels with uniform wall temperature is more modest, precise and reliable compared with two-phase models.

     

A Comprehensive Review on Different Numerical Approaches for Simulation in Nanofluids: Traditional and Novel Techniques

Computational fluid dynamics (CFD) has already proven to be an important tool to study fluids flow characteristics. Due to the rapid growth of powerful computers, CFD has become more applicable nowadays. In the field of nanofluids, this tool has also been applied by different approaches to understand and explain the effective phenomena. This article reviews and summarizes the numerical investigations implemented on nanofluids including conventional and novel methods. The studies conducted using methods such as Lattice Boltzmann, Eulerian–Lagrangian, thermal dispersion, Eulerian–Eulerian, and so forth are assessed.     

Quantification of Sub‐Nanomolar Levels of Aluminum by Adsorptive Stripping Voltammetry Using Rubeanic Acid as a Selective Chelating Agent

A novel, sensitive and selective adsorptive stripping procedure for determination of aluminum is presented. The method is based on the adsorptive accumulation of dithiooxamide (Rubeanic acid) complex of aluminum onto a hanging mercury drop electrode, followed by reduction of adsorbed species by voltammetric scan using differential pulse modulation. The influences of control variables on the sensitivity of the proposed method for the determination of aluminum were studied. The optimum analytical conditions were found to be Rubeanic acid (RA) concentration of 8.0×10^?5 M, ammonia buffer (NH₃? NH₄Cl) pH of 6.5, and accumulation potential at ?50 mV vs. Ag/AgCl with an accumulation time of 60 s. The peak currents are proportional to the concentration of aluminum over the 0.3–70 ng mL^?1 ranges with detection limit of 0.012 ng mL^?1. The procedure was applied to the determination of aluminum in the Lab. Water, HCl of Merck and potato samples with satisfactory results.     

Prediction of hydrothermal behavior of a non-Newtonian nanofluid in a square channel by modeling of thermophysical properties using neural network

Journal of Thermal Analysis and Calorimetry - This paper assesses the contribution of TiO2 nanoparticles on thermal performance of a 0.5 mass% aqueous solution of carboxymethyl cellulose...     

Experimental Study of Nanofluid Convective Heat Transfer for Implementation of Dispersion Model Considering Non-Uniform Particle Distribution

Thermal dispersion model has been used here to simulate heat transfer of water–Al₂O₃ nanofluid. A new form for dispersion thermal conductivity has been introduced in which non-uniform concentration distribution is applied on the model. It was observed that the non-uniformity of concentration increases at greater Reynolds numbers and average concentrations. An experimental set-up was made, and an experimental study was conducted to find the empirical coefficient in the dispersion thermal conductivity. The obtained results show that the developed dispersion model is able to properly simulate heat transfer of the nanofluid and provides more accurate results in comparison with a homogenous model.     

Thermal Dispersion Model Compared with Euler-Lagrange Approach in Simulation of Convective Heat Transfer for Nanoparticle Suspensions

Convective heat transfer characteristics of water/Al₂O₃ nanofluid flow inside a tube were evaluated in this study. A non-uniform concentration distribution was used in thermal dispersion model. Meanwhile, an experimental study was done to find the dispersion coefficient in addition to assess the accuracy of simulation results. The accuracy of the results of thermal dispersion model was compared with the numerical solution using discrete phase modeling and homogenous method, while the effective parameters on particle migration were considered to find the particle distribution for being used in the dispersion model. Non-uniformity of the particle distribution is increased by raising volume fraction and Reynolds number. Concentration distribution was obtained using discrete phase method and was compared with the distribution employed for the dispersion model. When a uniform concentration is used in the dispersion model, error of prediction is expected to be increased. The thermal dispersion model, in which the particles have followed a non-uniform distribution, provides acceptable results in spite of its lower calculational time as compared to the two-phase approach.