Ficus exasperata Vahl leaves extract attenuates motor deficit in vanadium-induced parkinsonism mice

Adeshina O Adekeye,Gold J Irawo,Adedamola Adediran Fafure 대한해부학회 2020 Anatomy & Cell Biology Vol.53 No.2

Medicinal herbs have played significant roles in the treatment of various diseases in humans and animals. Sodium metavanadate is a potentially toxic environmental pollutant that induces oxidative damage, neurological disorder, Parkinsonism and Parkinson-like disease upon excessive exposure. This study is designed to investigate the impact of saponin fraction of Ficus exasperata Vahl leaf extract (at 50 and 100 mg/kg body weight for 14 days at different animal groupings) on vanadium treated mice. Animals were randomly grouped into five groups. Control (normal saline), NaVO3 (10 mg/kg for 7 days), withdrawal group, NaVO3+Vahl (low dose) and NaVO3+Vahl (high dose). The animals were screened for motor coordination using rotarod and PBTs and a post mortem study was conducted by quantitatively assessing the markers of oxidative stress such as lipid peroxidation, catalase, glutathione activities, and also through immunohistochemistry via glia fibrillary acidic protein, tyrosine hydroxylase and dopamine transporter to study the integrity of astrocytes and dopaminergic neurons of the substantia nigra (SNc). Vanadium-exposed group showed a decreased motor activity on the neurobehavioural tests as well as an increase in markers of oxidative stress. Saponin fraction of F. exasperata Vahl leaves extract produced a statistically significant motor improvement which may be due to high antioxidant activities of saponin, thereby providing an ameliorative effect on the histoarchitecture of the SNc. It can be inferred that the saponin fraction of F. exasperata Vahl leaves extract to possesses ameliorative, motor-enhancing and neurorestorative benefit on motor deficit in vanadium-induced parkinsonism mice.

Influence of temperature, time, and moisture content on rheology of tomatoes and pepper purees

Adeshina Fadeyibi,Zainab Ololamide Ayinla,Rasaq A. Ajiboye 한국식품저장유통학회 2024 한국식품저장유통학회지 Vol.31 No.2

This study explored how temperature, time, and moisture content affect the rheological properties (apparent viscosity, flow behavior index, and consistency coefficient) of stored tomato and pepper purees. These purees were prepared with moisture contents of 85%, 90%, and 95% (w/v) using the hot-break method and tested over 6 days at 2-day intervals and temperatures of 5℃, 10℃, and 15℃. Results displayed distinct ranges for apparent viscosity, consistency coefficient, and flow behavior indices: tomato puree (2,519.9-4,324.6 mPa ․ s, 258.0-550.6 mPa ․ Sn, 1.80-0.48) and pepper puree (2,105.6-4,562.0 mPa ․ s, 268.4-580.4 mPa ․ Sn, 0.22-0.48). The temperature and storage time had significant (p≤0.05) effects, but moisture content did not affect these properties. Flow behavior and consistency coefficients demonstrated relative variation with apparent viscosity, indicating pseudoplastic behavior. Optimal processing and storage conditions were identified within specific ranges: 13.21-14.42℃ for 2 days with 92.22-94.23% (w/v) moisture content for pepper, and 8.42-11.77℃ for 2-6 days with 85% (w/v) moisture for tomato.

Impact of Electric Field on Propagation Velocity of Phase Boundary Between Nematic and Isotropic Phases of 5CB Liquid Crystal

Mohammad Awwal Adeshina,Jonghoo Park,마레디바라쓰쿠마르,강대경,Bongjun Choi 한국센서학회 2019 센서학회지 Vol.28 No.6

Liquid crystal (LC) mesophase materials manifest a variety of phase transitions. The optical properties of LCs are highly dependent upon the phase and orientation of the optical axis with respect to the polarization of incoming light. Studying the LC phase transitions is significantly important for a wide range of scientific and industrial applications. In this study, we demonstrate the propagation velocity of the phase boundary between the nematic and isotropic phase of 4-Cyano-4-pentylbiphenyl (5CB) liquid crystal for different electric fields using a polarized optical microscope. The results demonstrate that the propagation velocity of the phase boundary exhibits a peak value for a specific voltage, attributed to the supercooling of the isotropic phase of the LC. The analysis of the propagation velocity for different electric fields also provides a simple optical platform to measure the thermal anisotropy and voltage dependent thermal properties of the homogeneously aligned LC

Hepatitis C Stage Classification with hybridization of GA and Chi2 Feature Selection

Umar, Rukayya,Adeshina, Steve,Boukar, Moussa Mahamat International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.1

In metaheuristic algorithms such as Genetic Algorithm (GA), initial population has a significant impact as it affects the time such algorithm takes to obtain an optimal solution to the given problem. In addition, it may influence the quality of the solution obtained. In the machine learning field, feature selection is an important process to attaining a good performance model; Genetic algorithm has been utilized for this purpose by scientists. However, the characteristics of Genetic algorithm, namely random initial population generation from a vector of feature elements, may influence solution and execution time. In this paper, the use of a statistical algorithm has been introduced (Chi2) for feature relevant checks where p-values of conditional independence were considered. Features with low p-values were discarded and subject relevant subset of features to Genetic Algorithm. This is to gain a level of certainty of the fitness of features randomly selected. An ensembled-based learning model for Hepatitis has been developed for Hepatitis C stage classification. 1385 samples were used using Egyptian-dataset obtained from UCI repository. The comparative evaluation confirms decreased in execution time and an increase in model performance accuracy from 56% to 63%.

Feature Selection with Ensemble Learning for Prostate Cancer Prediction from Gene Expression

Abass, Yusuf Aleshinloye,Adeshina, Steve A. International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.12

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

Feature Selection with Ensemble Learning for Prostate Cancer Prediction from Gene Expression

Abass, Yusuf Aleshinloye,Adeshina, Steve A. International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.spc12

Machine and deep learning-based models are emerging techniques that are being used to address prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that has attracted a great deal of attention in the biomedical domain. Machine and deep learning-based models have been shown to provide more accurate results when compared to conventional regression-based models. The prediction of the gene sequence that leads to cancerous diseases, such as prostate cancer, is crucial. Identifying the most important features in a gene sequence is a challenging task. Extracting the components of the gene sequence that can provide an insight into the types of mutation in the gene is of great importance as it will lead to effective drug design and the promotion of the new concept of personalised medicine. In this work, we extracted the exons in the prostate gene sequences that were used in the experiment. We built a Deep Neural Network (DNN) and Bi-directional Long-Short Term Memory (Bi-LSTM) model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The models were evaluated using different classification metrics. Our experimental results show that DNN model prediction offers a training accuracy of 99 percent and validation accuracy of 96 percent. The bi-LSTM model also has a training accuracy of 95 percent and validation accuracy of 91 percent.

Detection and Localization of Image Tampering using Deep Residual UNET with Stacked Dilated Convolution

Aminu, Ali Ahmad,Agwu, Nwojo Nnanna,Steve, Adeshina International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.9

Image tampering detection and localization have become an active area of research in the field of digital image forensics in recent times. This is due to the widespread of malicious image tampering. This study presents a new method for image tampering detection and localization that combines the advantages of dilated convolution, residual network, and UNET Architecture. Using the UNET architecture as a backbone, we built the proposed network from two kinds of residual units, one for the encoder path and the other for the decoder path. The residual units help to speed up the training process and facilitate information propagation between the lower layers and the higher layers which are often difficult to train. To capture global image tampering artifacts and reduce the computational burden of the proposed method, we enlarge the receptive field size of the convolutional kernels by adopting dilated convolutions in the residual units used in building the proposed network. In contrast to existing deep learning methods, having a large number of layers, many network parameters, and often difficult to train, the proposed method can achieve excellent performance with a fewer number of parameters and less computational cost. To test the performance of the proposed method, we evaluate its performance in the context of four benchmark image forensics datasets. Experimental results show that the proposed method outperforms existing methods and could be potentially used to enhance image tampering detection and localization.