Browsing by Author "Barua, Niloy"

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Chemical Profiling, Pharmacological Insights and In Silico Studies of Methanol Seed Extract of Sterculia foetida
(https://www.mdpi.com/journal/plants, 2021-06-03) Alam, Najmul; Banu, Naureen; Aziz, Arfin Ibn; Barua, Niloy; Ruman, Umme; Jahan, Israt; Jahan, Farhana; Denath, Susmita; Paul, Arkajyoti; Uddin, Nazim; Sayeed, Mohammed Aktar; Emran, Talha Bin; Gandara, Jesus Simal
Sterculia foetida, also known as jangli badam in Bangladesh, is a traditionally used plant that has pharmacological activities. A qualitative phytochemical analysis was performed to assess the metabolites in a methanolic extract of S. foetida seeds (MESF), and the cytotoxic, thrombolytic, anti-arthritics, analgesic, and antipyretic activities were examined using in vitro, in vivo, and in silico experiments. Quantitative studies were performed through gas chromatography-mass spectroscopy (GC-MS) analysis. The brine shrimp lethality bioassays and clot lysis were performed to investigate the cytotoxic and thrombolytic activities, respectively. The anti-arthritics activity was assessed using the albumin denaturation assay. Analgesic activity was determined using the acetic acidinduced writhing test and the formalin-induced paw-licking test. A molecular docking study was performed, and an online tool was used to perform ADME/T (absorption, distribution, metabolism, and excretion/toxicity) and PASS (Prediction of Activity Spectra for Substances). GC-MS analysis identified 29 compounds in MESF, consisting primarily of phenols, terpenoids, esters, and other organic compounds. MESF showed moderate cytotoxic activity against brine shrimp and significant thrombolytic and anti-arthritics activities compared with the relative standards. The extract also showed a dose-dependent and significant analgesic and antipyretic activities. Docking studies showed that 1-azuleneethanol, acetate returned the best scores for the tested enzymes. These findings suggested that MESF represents a potent source of thrombolytic, anti-arthritic, analgesic, antipyretic agents with moderate cytotoxic effects.
Design of an arduino based Maximum Power Point Tracking (MPPT) solar charge controller
(BRAC University, 8/16/2016) Dutta, Ananya; Barua, Niloy; Saha, Aninda; Das, Avijit; Chakma, Shoilie
Renewable sources such as the Photovoltaic Systems (PV) have been used over decades in order to focus on greener sources of power generation. Today it has become a matter of concern on how to reduce COST and improve EFFICIENCY in order to harness and use these natural resources in a much better way possible. Hence the idea of Maximum Power Point Tracking System (MPPT) has emerged, which is basically a system used by charge controllers for wind turbines and Photovoltaic Systems to employ and also provide a maximized power output. This Thesis is mainly concerned with the utilization of such a system in order to achieve a controlled photovoltaic power using MPPT mechanism. The main aim of this project was to track the maximum power point of the photovoltaic module so that the maximum possible power can be extracted from the photovoltaic systems by varying certain conditions in algorithm and set up mechanism. Finally the output data from this project was compared with the other MPPT algorithms in order to attain an improved performance hence a better MPPT system. Furthermore, the system was interfaced with GSM to get a better access of data from anywhere for analysis thus reducing the physical work of data collection.
Investigation of the Pharmacological Properties of Lepidagathis hyalina Nees through Experimental Approaches
(https://www.mdpi.com/journal/life, 2021-02-25) Fahad, Fowzul Islam; Barua, Niloy; Islam, Md. Shafiqul; Sayem *, Al Jawad; Barua, Koushik; Uddin, Mohammad Jamir; Uddin, Md. Nazim; Adnan, Md.; Islam, Mohammad Nazmul; Sayeed, Mohammed Aktar; Emran, Talha Bin; Gandara, Jesus Simal; Ester Pagano 5; Rafandfaele Capasso 6
Lepidagathis hyalina Nees is used locally in Ayurvedic medicine to treat coughs and cardiovascular diseases. This study explored its pharmacological potential through in vivo and in vitro approaches for the metabolites extracted (methanolic) from the stems of L. hyalina. A qualitative phytochemical analysis revealed the presence of numerous secondary metabolites. The methanol extract of L. hyalina stems (MELHS) showed a strong antioxidative activity in the 1,1-diphenyl-2- picrylhydrazyl (DPPH) and reducing power assays, and in the quantitative (phenolic and flavonoid) assay. Clot lysis and brine shrimp lethality bioassays were applied to investigate the thrombolytic and cytotoxic activities, respectively. MELHS exhibited an expressive percentage of clot lysis (33.98%) with a moderately toxic (115.11 g/mL) effect. The in vivo anxiolytic activity was studied by an elevated plus maze test, whereas the antidepressant activity was examined by a tail suspension test and forced swimming test. During the anxiolytic evaluation, MELHS exhibited a significant dosedependent reduction of anxiety, in which the 400 mg/kg dose of the extract showed 78.77 4.42% time spent in the open arm in the elevated plus maze test. In addition, MELHS demonstrated dosedependent and significant activities in the tail suspension test and forced swimming test, whereas the 400 mg/kg dose of the extract showed 87.67 6.40% and 83.33 6.39% inhibition of immobile time, respectively. Therefore, the current study suggests that L. hyalina could be a potential source of anti-oxidative, cytotoxic, thrombolytic, anxiolytic, and antidepressant agents. Further study is needed to determine the mechanism behind the bioactivities.
Masked face identification using face recognition
(BRAC University, 2022-05) Hossen, Tareq; Uddin, Abbas; Barua, Niloy; Faik, Chowdhury Azmain; Rhaman, Md. Khalilur; Roy, Shaily
This work intends to express one of the several well-known biometric authentications entitled Masked Face Identification models by applying current Face Recognition algorithms and public masked face raw data that predict beneficial use. At the end of 2019, the COVID-19 pandemic has been exotically expanding worldwide, which severely negatively harms people’s economies and well-being. Since using facial masks in social environments is now an efficient system to stop the spread of viruses, Nevertheless, appearance identification using facial masks is now a profoundly demanding duty because of the shortage of appropriate facial statistics. Here in our approach, the Deep Learning method will be executed by us to recognize the masked appearance by employing different face portions, some extra-superintendent and some owned-superintendent multi-task training facial appearance spotters, which can compact with different scales of appearance quickly and effectively. Additionally, the features are extracted by us from the masked face’s eyes, forehead, and eyebrow areas and merged with characteristics acquired from those methodologies into a combined structure for identifying masked faces. In order to process, we will perform various image processing techniques on our dataset to clean our data for better accuracy. We will train our model from scratch to perform face-mask recognition. The most important part of this project remains the data collection and data cleaning process. Using a data-centric approach, we will systematically enhance our data-set to improve accuracy and prevent overfitting by performing data augmentation and stratified sampling and keeping our model architecture constant. Finally, our proposed systems will be compared by us with multiple unions of genius appearance identification techniques among those advertised by CASIA, LFW, and owned gathered raw data, which are managed from different sources. When wearing a mask, a person’s face is hidden by 60–75%. Using only 30–40% of a person’s face, we designed a face mask recognition model with an accuracy of 99.84%. Trained on a modified CASIA dataset containing images with and without masks, the model could successfully get the embeddings of 85743 people within a few minutes and perform perfect face recognition with and without masks.