International Islamic University, Chittagong

About: International Islamic University, Chittagong is a education organization based out in Chittagong, Bangladesh. It is known for research contribution in the topics: Debye model & Density functional theory. The organization has 1111 authors who have published 1089 publications receiving 7918 citations. The organization is also known as: IIUC.

Evaluation of anti-nociceptive and anti-inflammatory activities of Piper sylvaticum (Roxb.) stem by experimental and computational approaches

Abstract: Piper sylvaticum Roxb., (Family: Piperaceae), commonly known as pahaari peepal, is used in traditional medicine for the treatment of rheumatic pain, headache, asthma, chronic cough, diarrhea, and wounds. To provide scientific proof for its traditional use, the present study was designed to investigate the antinociceptive and anti-inflammatory properties of methanol extract of P. sylvaticum stem (MEPSS) in pain models. Additionally, computational studies viz. molecular docking, ADME and toxicological property predictions were performed to identify the potent phytochemicals of this plant for antinociceptive and anti-inflammatory activities with good oral bioavailability and safety features. Quantitative phytochemical analysis of MEPSS was performed using established protocols. The antinociceptive activity was determined using acetic acid and formalin test in mice at the doses of 200 and 400 mg/kg while paw edema induced by carrageenan used for anti-inflammatory activity. Molecular docking study was performed by Schrodinger Maestro 10.1 whereas the SwissADME and admetSAR were used for ADME and toxicity prediction respectively. The total phenolic and flavonoid contents of MEPSS were 93.39 and 53.74 mg gallic acid and quercetin equivalent/g of extract respectively. The methanol extract exhibited significant and dose-dependent antinociceptive and anti-inflammatory effects in experimental pain models. Also, our docking study showed that piperine, piperlonguminine, and sylvamide have the best binding affinities to cyclooxygenase enzymes with good ADME/T properties. This study confirmed that MEPSS possess significant antinociceptive and anti-inflammatory activities which could be due to the presence of phytochemicals and three bioactive compounds (piperine, piperlonguminine, and sylvamide) were found to be most effective in computational studies.

Antibacterial and cytotoxic activity of ethanol extract of mikania cordata (burm.f.) B.L. Robinson leaves.

Abstract: The purpose of the present study to examine the antibacterial and cytotoxic properties of ethanol extract of leaves of Mikania cordata (Burm.f.) B.L. Robinson. To determine antibacterial activities, the extract was tested against four Gram positive and six Gram negative bacteria at three concentrations (500, 800, 1000 μg disc(-1)) through disc diffusion method. The extract showed moderate antibacterial actions and that was increased by increasing the concentration of the sample. The maximum antimicrobial potential was obtained against Shigella flexneri and no sen sitivity was found for Klebsiella sp. Comparatively gram-positive bacteria demonstrated more susceptibility to the extract than gram-negative bacteria. Cytotoxic property of the sample was done using Brine shrimp lethality bioassay where it did not show noticeable toxicity. So, our present study reveals that the leaves extract of M. cordata possess considerable antibacterial properties along with lesser amount of cytotoxicity.

Birth Mode Prediction Using Bagging Ensemble Classifier: A Case Study of Bangladesh

Abstract: Maternal mortality and childbirth complications are major delivery issues in most developing countries, especially in rural areas. The proper identification of risks associated with the delivery of an expecting woman at an earlier stage can substantially reduce the mortality rate. A few studies have been conducted on using Machine Learning (ML) techniques for predicting birth mode i.e. caesarean section or normal delivery. The most commonly used methods are Decision Tree (DT), K-Nearest Neighbour (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). In this study we have implemented Bagging Ensemble Classifiers based on these traditional machine learning algorithms, which is a novel approach in the area of birth mode prediction. This paper examines the performance of four ML algorithms, with bagging ensemble classifiers (DT-Bagging, KNN-Bagging, NB-Bagging, SVM-Bagging). The result shows that bagging ensemble models outperformed the traditional models in this domain. Besides, we have identified the association between important factors and caesarean section. This study may later be used to create a decision support system by extracting knowledge from the hidden patterns in data to reduce the rate of caesarean delivery in Bangladesh.

A Comparative Study on Heart Disease Prediction Using Data Mining Techniques and Feature Selection

Abstract: The world health organization shows us that cardiovascular disease is one of the noteworthy reasons for death in the world. In this paper, data mining classification techniques i.e. Naive Bayes (NB), Support Vector Machine (SVM), k-nearest neighbors' (k-NN), Decision Tree (DT), Neural Network (NN), Logistic Regression (LR), Random Forest (RF), Gradient Boosting are proposed to predict the probability of the coronary heart disease. In the present world, researchers are trying heart and soul to make advancements in the smart health care system. An automated system predicting the risk of heart disease may be added as a great achievement. This work of predicting heart disease is evaluated using the dataset from the UCI machine learning repository. The feature selection method enhances the performance of traditional machine learning algorithms. Among the classification algorithms, Random Forest (RF) algorithm with PCA has given the best accuracy of 92.85% for heart disease classification.