Effects of cinnamic acid on memory deficits and brain oxidative stress in streptozotocin-induced diabetic mice

Hemmati, Ali Asghar,Alboghobeish, Soheila,Ahangarpour, Akram The Korean Society of Pharmacology 2018 The Korean Journal of Physiology & Pharmacology Vol.22 No.3

The present study aimed to evaluate the cinnamic acid effect on memory impairment, oxidative stress, and cholinergic dysfunction in streptozotocin (STZ)-induced diabetic model in mice. In this experimental study, 48 male Naval Medical Research Institute (NMRI) mice (30-35 g) were chosen and were randomly divided into six groups: control, cinnamic acid (20 mg/kg day, i.p.), diabetic, and cinnamic acid-treated diabetic (10, 20 and 40 mg/kg day, i.p.). Memory was impaired by administering an intraperitoneal STZ injection of 50 mg/kg. Cinnamic acid was injected for 40 days starting from the 21st day after confirming STZ-induced dementia to observe its therapeutic effect. Memory function was assessed using cross-arm maze, morris water maze and passive avoidance test. After the administration, biochemical parameters of oxidative stress and cholinergic function were estimated in the brain. Present data indicated that inducing STZ caused significant memory impairment, whereas administration of cinnamic acid caused significant and dose-dependent memory improvement. Assessment of brain homogenates indicated cholinergic dysfunction, increase in lipid peroxidation and reactive oxygen species (ROS) levels, and decrease in glutathione (GSH), superoxide dismutase (SOD), and catalase (CAT) activities in the diabetic group compared to the control animals, whereas cinnamic acid administration ameliorated these indices in the diabetic mice. The present study demonstrated that cinnamic acid improves memory by reducing the oxidative stress and cholinergic dysfunction in the brain of diabetic mice.

Increasing the flexural capacity of RC beams using partially HPFRCC layers

Ali Hemmati,Ali Kheyroddin,Mohammad K. Sharbatdar 사단법인 한국계산역학회 2015 Computers and Concrete, An International Journal Vol.16 No.4

High Performance Fiber Reinforced Cementitious Composites which are called HPFRCC, include cement matrices with strain hardening response under tension loading. In these composites, the cement mortar with fine aggregates, is reinforced by continuous or random distributed fibers and could be used for various applications including structural fuses and retrofitting of reinforced concrete members etc. In this paper, mechanical properties of HPFRCC materials are reviewed briefly. Moreover, a reinforced concrete beam (experimentally tested by Maalej et al.) is chosen and in different specimens, lower or upper or both parts of that beam are replaced with HPFRCC layers. After modeling of specimens in ABAQUS and calibration of those, mechanical properties of these specimens are investigated with different thicknesses, tensile strengths, tensile strains and compressive bars. Analytical results which are obtained by nonlinear finite analyses show that using HPFRCC layers with different parameters, increase loading capacity and ultimate displacement of these beams compare to RC specimens.

Effects of cinnamic acid on memory deficits and brain oxidative stress in streptozotocin-induced diabetic mice

Ali Asghar Hemmati,Soheila Alboghobeish,Akram Ahangarpour 대한약리학회 2018 The Korean Journal of Physiology & Pharmacology Vol.22 No.3

The present study aimed to evaluate the cinnamic acid effect on memory impairment, oxidative stress, and cholinergic dysfunction in streptozotocin (STZ)-induced diabetic model in mice. In this experimental study, 48 male Naval Medical Research Institute (NMRI) mice (30-35 g) were chosen and were randomly divided into six groups: control, cinnamic acid (20 mg/kg day, i.p.), diabetic, and cinnamic acidtreated diabetic (10, 20 and 40 mg/kg day, i.p.). Memory was impaired by administering an intraperitoneal STZ injection of 50 mg/kg. Cinnamic acid was injected for 40 days starting from the 21st day after confirming STZ-induced dementia to observe its therapeutic effect. Memory function was assessed using cross-arm maze, morris water maze and passive avoidance test. After the administration, biochemical parameters of oxidative stress and cholinergic function were estimated in the brain. Present data indicated that inducing STZ caused significant memory impairment, whereas administration of cinnamic acid caused significant and dose-dependent memory improvement. Assessment of brain homogenates indicated cholinergic dysfunction, increase in lipid peroxidation and reactive oxygen species (ROS) levels, and decrease in glutathione (GSH), superoxide dismutase (SOD), and catalase (CAT) activities in the diabetic group compared to the control animals, whereas cinnamic acid administration ameliorated these indices in the diabetic mice. The present study demonstrated that cinnamic acid improves memory by reducing the oxidative stress and cholinergic dysfunction in the brain of diabetic mice.

A new efficient empirical correlation for filtrate flux in slurry bubble column reactor of a gas-to-liquid process

Mohammad Reza Hemmati,Mohammad Ali Khodagholi 한국화학공학회 2015 Korean Journal of Chemical Engineering Vol.32 No.12

Gas to Liquid has recently become of great interest. In this technology slurry bubble column reactors are favored for many reasons. Separation of liquid wax from the slurry is still a major problem that may be done by internal or external filtration. A system of sintered metal candle filters are designed and operated to collect experimental data of internal filtration. Data for 4 and 8 micron filter elements with different pressure differences and kinematic viscosity were collected. Data analysis revealed that these data could be correlated as a simple function of time, pressure drop and kinematic viscosity. This new and efficient correlation shows excellent ability to reproduce original data at moderate filtration conditions, but it is less precious in severe conditions. It was understood that main reason for this behavior is different filtrate flux regimes through filter media pores, led to inability of a single correlation to fit both regimes properly.

Numerical and experimental behavior of moment concrete frame retrofitted with TADAS metal yielding damper under lateral loading

Reza Nazeran,Ali Hemmati,Hasan Haji Kazemi 국제구조공학회 2024 Structural Engineering and Mechanics, An Int'l Jou Vol.89 No.5

Since the cost of reconstruction is very high and the structure may have been damaged by an earthquake, we must retrofit the structure. Therefore, the importance of studying this issue is very high in order to achieve the desired resistance against the regulations. The present study involved the numerical and experimental analysis of nine concrete frames, consisting of three concrete frames, three concrete frames with bracing, and three concrete frames with a TADAS damper. The purpose of this study is to strengthen the damaged concrete frame using braces and TADAS dampers. Observations were made of the frames as they were subjected to controlled displacement. Also, ABAQUS software was used to compare numerical and experimental results. According to the results, the software was sufficiently capable of modeling the studied frames. Additionally, a parametric study was conducted on the thickness and number of bending plates. Thickness increases from 8 mm to 12 mm, 8 mm to 15 mm, and 8 mm to 20 mm, increasing the base shear by about 6.7%, 11.1%, and 25%, respectively. Furthermore, increasing the number of plates from 4 to 5, 4 to 6, and 4 to 7 increased base shears by about 4.5%, 8.4%, and 14%, respectively.

이산화티탄 나노입자 필러가 PET와 PLA 나노복합체의 특성에 미치는 영향

Seyed Mohammad Ali Mousavi,Mehdi Farhoodi,Saeed Dadashi,Rahmat Sotudeh Gharebagh,Zahra Emam Djomeh,Abdolrasul Oromiehie,Farkhondeh Hemmati 한국고분자학회 2012 폴리머 Vol.36 No.6

Two types of polymers were tested in this study; poly(ethylene terephthalate) (PET) as a synthetic example and poly(lactic acid) (PLA) as a natural polymer. DSC analyses showed that the use of nanofiller increased the degree of crystallinity (Xc) of both PET and PLA polymers, but the effect was more noticeable on PET nanocomposites. The crystallization of PLA and PET nanocomposites occurred at higher temperatures in comparison to neat polymers. According to dynamic mechanical-thermal analysis (DMTA), the damping factor of PET/TiO2 nanoparticles decreased compared to the neat matrix, but for PLA nanocomposites the opposite trend was observed. Results of the mechanical test showed that for both PET and PLA nanocomposites, the most successful toughening effect was observed at 3 wt% loading of TiO2 nanoparticles. SEM micrographs revealed uniform distribution of TiO2 nanoparticles at 1 and 3 wt% loading levels. The results of WAXD spectra explained that the polymorphs of PLA and PET was not affected by TiO2 nanoparticles. UV-visible spectra showed that TiO2 nanocomposite films had high ultraviolet shielding compared to neat polymer, but there was significant reduction in transparency.

Experimentally evaluating the seismic retrofitting of square engineered cementitious composite columns using CFRP

Alireza Akhtari,Alireza Mortezaei,Ali Hemmati 국제구조공학회 2021 Structural Engineering and Mechanics, An Int'l Jou Vol.78 No.5

The present experimental study evaluated the seismic performance of six engineered cementitious composite (ECC) columns strengthened with carbon fiber reinforced polymer (CFRP) laminates under cyclic lateral loading. The ECC columns damaged and crushed in the first stage of cyclic tests were repaired using the ECC with a certain polyvinyl alcohol (PVA) fiber and strengthened with flexural and sheer CFRP laminates and then re-assessed under the cyclic loading. The effects of some variables were examined on lateral displacement, energy absorption and dissipation, failure modes, crack patterns, load bearing capacity and plasticity, and the obtained results were compared with those of the first stage of cyclic tests. The results showed that retrofitting the ECC columns can improve their performance, plasticity and load-bearing threshold, delayed the concrete failure, changed the failure modes and increased the energy absorbed by the strengthened columns element by over 50%.

Modeling CO2 Loading Capacity of Diethanolamine (DEA) Aqueous Solutions Using Advanced Deep Learning and Machine Learning Algorithms: Application to Carbon Capture

Mahmoudzadeh Atena,Hadavimoghaddam Fahimeh,Atashrouz Saeid,Abedi Ali,Abuswer Meftah Ali,Mohaddespour Ahmad,Hemmati-Sarapardeh Abdolhossein 한국화학공학회 2024 Korean Journal of Chemical Engineering Vol.41 No.5

Several carbon capture techniques have been developed in response to the notable rise of atmospheric carbon dioxide ( CO2 ) levels. The utilization of diethanolamine (DEA) as an absorption method is prevalent in various industries due to its high reactivity and cost-effi ciency. Hence, comprehending the equilibrium solubility of CO2 in DEA solutions is an essential step in developing and optimizing absorption procedures. In order to predict the CO2 loading capacity in the DEA solutions, four advanced deep learning and machine learning models were developed: recurrent neural networks (RNN), deep neural networks (DNN), random forest (RF), and adaBoost-support vector regression (AdaBoost-SVR). The models predict the capacity of CO2 loading as a function of temperature, CO2 partial pressure, and the concentration of DEA in the solution. Intelligent models were developed employing an extensive database which includes new experimental data points published within recent years, which were not considered in the previous studies. The RNN model was found to outperform other models based on graphical and statistical assessments, as evidenced by its lower root mean square error ( RMSE = 0.285 ) and standard deviation ( SD = 0.032 ), and higher determination coeffi cient ( R2 = 0.992 ). While the RNN model resulted in the highest accuracy in predicting CO2 absorption, the DNN, RF, and AdaBoost-SVR models also demonstrated satisfactory accuracy in predicting CO2 solubility, placed in the following ranking. A sensitivity analysis was performed on the four developed models, revealing that the CO2 partial pressure has the strongest eff ect on the CO2 loading capacity. Furthermore, a trend analysis was performed on the RNN model, demonstrating that the developed model has a high degree of accuracy in following physical trends. The binary interaction analysis was conducted with two varying parameters and one constant parameter in the RNN model through 3-D image plots, which illustrated the simultaneous eff ect of two independent parameters on CO2 loading. Finally, outlier detection was conducted by employing the Leverage method to fi nd outlier data points in the data bank, demonstrating the applicability domain of intelligent models.

Influence of TiO₂ Nanoparticle Filler on the Properties of PET and PLA Nanocomposites

Farhoodi, Mehdi,Dadashi, Saeed,Mousavi, Seyed Mohammad Ali,Sotudeh-Gharebagh, Rahmat,Emam-Djomeh, Zahra,Oromiehie, Abdolrasul,Hemmati, Farkhondeh The Polymer Society of Korea 2012 폴리머 Vol.36 No.6

Two types of polymers were tested in this study; poly(ethylene terephthalate) (PET) as a synthetic example and poly(lactic acid) (PLA) as a natural polymer. DSC analyses showed that the use of nanofiller increased the degree of crystallinity ($X_c$) of both PET and PLA polymers, but the effect was more noticeable on PET nanocomposites. The crystallization of PLA and PET nanocomposites occurred at higher temperatures in comparison to neat polymers. According to dynamic mechanical-thermal analysis (DMTA), the damping factor of PET/$TiO_2$ nanoparticles decreased compared to the neat matrix, but for PLA nanocomposites the opposite trend was observed. Results of the mechanical test showed that for both PET and PLA nanocomposites, the most successful toughening effect was observed at 3 wt% loading of $TiO_2$ nanoparticles. SEM micrographs revealed uniform distribution of $TiO_2$ nanoparticles at 1 and 3 wt% loading levels. The results of WAXD spectra explained that the polymorphs of PLA and PET was not affected by $TiO_2$ nanoparticles. UV-visible spectra showed that $TiO_2$ nanocomposite films had high ultraviolet shielding compared to neat polymer, but there was significant reduction in transparency.

Modeling the permeability of heterogeneous oil reservoirs using a robust method

Arash Kamari,Farzaneh Moeini,Mohammad-Javad Shamsoddini-Moghadam,Seyed-Ali Hosseini,Amir H. Mohammadi,Abdolhossein Hemmati-Sarapardeh 한국지질과학협의회 2016 Geosciences Journal Vol.20 No.2

Permeability as a fundamental reservoir property plays a key role in reserve estimation, numerical reservoir simulation, reservoir engineering calculations, drilling planning, and mapping reservoir quality. In heterogeneous reservoir, due to complexity, natural heterogeneity, non-uniformity, and non-linearity in parameters, prediction of permeability is not straightforward. To ease this problem, a novel mathematical robust model has been proposed to predict the permeability in heterogeneous carbonate reservoirs. To this end, a fairly new soft computing method, namely least square support vector machine (LSSVM) modeling optimized with coupled simulated annealing (CSA) optimization technique was utilized. Statistical and graphical error analyses have been employed separately to evaluate the accuracy and reliability of the proposed model. Furthermore, this model performance has been compared with a newly developed multilayer perceptron artificial neural network (MLP-ANN) model. The obtained results have shown the more robustness, efficiency and reliability of the proposed CSA-LSSVM model in comparison with the developed MLP-ANN model for the prediction of permeability in heterogeneous carbonate reservoirs. Estimations were found to be within acceptable agreement with the actual field data of permeability, with a root mean square error of approximately 0.42 for CSA-LSSVM model in testing phase, and a R-squared value of 0.98. Additionally, these error parameters for MLP-ANN are 0.68 and 0.89 in testing stage, respectively.