http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/278 2026-05-04T20:29:11Z http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/1057 DEVELOPMENT AND ASSESSMENT OF CARBOXYMETHYL CELLULOSE LOADED ZINC OXIDE NANOPARTICLES FOR ANTIBACTERIAL PROPERTIES MAHMUD, NIAZ Zinc Oxide (ZnO) Nanoparticles (NPs) have some indigenous properties, which make them a good candidate for versatile biomedical and clinical applications. Although there are numerous potentials, clinical applications of ZnO NPs are still under many obstacles. Due to its stable nature, bigger-sized ZnO has already been used in various clinical applications (i.e., sunscreen, toothpaste, dermatological ointment, anti-etching ointment, etc.). The main problem of using nanosized ZnO in clinical applications is its lack of cell specificity and the tendency to produce reactive oxygen species by external influence (i.e., light, sound, etc.), stability in a biological system. Surface modification of the ZnO NPs can make them more stable, delay or control the release of reactive oxygen species generations, and be cell-specific in biological systems. To make the ZnO NPs enriched with cellulosic properties for antibacterial studies, the surface modification of ZnO NPs has been carried out by Carboxymethyl-Cellulose (CMC), and relevant physical properties (Fourier Transform Infrared Analysis, X-ray Diffraction, Scanning Electron Microscopy, Energy Dispersive X-Ray and Zeta Potentials) have been assessed which confirms the formation of a conjugated matrix of ZnO NPs-CMC. The antibacterial efficacy of the CMC-enriched ZnO NPs was further experimented with over Lactobacilli (Acidophilus and Bulgaricus) bacterial species to examine the antibacterial activity against the naïve molecules and found that with a slight modification of ZnO treated by CMC causes an overall increase in antibacterial efficacy at a concentration (mass/liquid-volume) of 0.5% (w/v) (viability reduction: 51% vs 66 %) & 1.5% w/v (viability reduction: 63 % vs 77 %) and insignificant at deficient concentrations (0.1% w/v) for both. DEVELOPMENT AND ASSESSMENT OF CARBOXYMETHYL CELLULOSE LOADED ZINC OXIDE NANOPARTICLES FOR ANTIBACTERIAL PROPERTIES 2024-10-01T00:00:00Z http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/824 DEVELOPMENT OF CHICKEN EGG WHITE AND MUPIROCIN LOADED HYDROGEL DRESSING MATERIAL FOR INHIBITION OF BACTERIAL GROWTH NAIM, ZANNATUL The bacterial infection of wound is one of the major challenges in wound care treatment. Topical antibiotic mupirocin is an antibacterial agent widely used to inhibit bacterial growth after wound formation in the human body. Antibacterial activity of egg white with wound healing capability also has been reported in research articles. Therefore, the hydrogel was prepared by incorporating chicken egg white and mupirocin into poly vinyl alcohol (PVA) and gelatin polymer matrix via solution casting method for the purpose of bacterial growth inhibition. The developed hydrogels were characterized by Fourier Transform Infrared Spectroscopy (FTIR) that confirmed the presence of a functional group of the different components in the hydrogel and crosslinking occurred by esterification process between PVA and gelatin by glutaraldehyde. The developed hydrogels were characterized morphologically by Scanning Electron Microscope (SEM), it showed a smooth break surface for neat hydrogel (NH) and chicken egg white loaded hydrogel (EH), smooth and homogenous surface for mupirocin loaded hydrogel (MH) and rough surface for mupirocin with chicken egg white loaded hydrogel (MEH). Moreover, swelling behavior in water, moisture retention capability, folding endurance, water vapor transmission rate (WVTR), pH determination, gel fraction, spreadability, and porosity tests were conducted to reveal the chemical and physical properties of the hydrogel. All the values obtained from these tests were compatible with the published data and to some extent with the commercially available products. Eventually, developed hydrogels were characterized biologically by the disc diffusion method to evaluate bacterial inhibition activity. MH and MEH showed significant inhibition against Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa bacteria and EH showed moderate inhibition against experimented bacteria. This newly prepared chicken egg white and mupirocin-loaded hydrogel might play an important role in inhibiting the growth of bacteria during the wound healing process. Hence, current outcomes with experimental findings can be used for further investigation in future in vivo tests on mice models. First of all, thanks to ALLAH to whom I relate my success in this thesis work. I have no words to thank my supervisor, Lieutenant Colonel Md. Maruf Hasan, PhD for guiding me throughout this project and for his great help, advice, and encouragement. I would like to thank Colonel Syed Mahfuzur Rahman, Head of the Department of Biomedical Engineering (BME) at the Military Institute of Science and Technology (MIST) for his outstanding support and encouragement during my thesis defense. I am also thankful to S. M. Masud Rana previous M.Sc. Engg. student of the BME Department, MIST and present Assistant Manager (Production Pharmacist) of The ACME Laboratories Ltd. Dhamrai, Dhaka, Bangladesh, and Shammi Quraishi previous B.Sc. Engg. student of the same department and present student in the Master’s program of Biomedical and Science and Engineering at the University of Tampere, Finland for outstanding support throughout the thesis project. I would like to thank the Department of BME, MIST, Dhaka, Bangladesh for overall support and the Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh for providing Scanning Electron Microscope (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) support. I am truly obliged to the rest of the Board of Examination members for reviewing this thesis and giving support, suggestions, and valuable time. I would especially like to thank the external member of the Board of Examination Prof. Dr. Muhammad Tarik Arafat, Head of the BME Department of BUET, Dhaka, Bangladesh for his intellectual inputs and valuable suggestions. Additionally, I am also grateful to Dr. Md. Asadur Rahman, Assistant Professor and Postgraduate Course Coordinator of the BME Department at MIST for his outstanding support. At last, I would like to thank my family that consistently supported and motivated me towards achieving my goal, particularly to my little new family member, little angel and princess my daughter ‘RUHAMA’ who was born just nineteen days after my thesis defense. Since then, she has been an encouragement and inspiration in a great deal. 2023-03-01T00:00:00Z http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/819 DEVELOPMENT OF A CLINICAL DIAGNOSIS AND DECISION SUPPORT SYSTEM FOR CHEST RADIOGRAPHY USING CNN MAGAR, BIPIN THAPA Chest X-ray (CXR) image is a widely used diagnostic tool for various chest diseases. Human interpretation of CXR images, however, has never been always effective. The diagnostic level of the radiologists along with other factors like cognitive ability, experience, fatigue, and other human-dependent factors may impair the diagnostic procedure with missed information, misinterpretation, and requiring more time and cost. Computer-aided analysis of CXR images has already demonstrated its potential over manual or human screening to facilitate rapid, correct, and low-cost diagnosis of chest diseases. Existing computer-aided systems are still not suitable for real-time applications due to limited findings, limited generalizability across wide datasets, and not being computationally and economically affordable as well. Therefore, an efficient Convolution Neural Network (CNN) based computer-aided decision support system, the CXRNet, was developed for the automatic detection of abnormalities from CXR images in a real-time clinical scenario. The proposed CXRNet model is a 16-layered CNN architecture with 5 output classes: Cardiomegaly, COVID, Normal, Pneumonia, and Tuberculosis. This architecture is trained with frontal CXR images obtained from various sources to improve the generalization of the model across multiple datasets. Upon testing the model on three different data distribution conditions (70% training and 30% testing, 80% training and 20% testing, and 90% training and 10% testing), it achieved a state-of-the-art performance with an average accuracy of 95.7%, a precision of 95.3%, a recall of 95.3%, and an f1-score of 95.3% for the multiclass classification task. The proposed CXRNet also demonstrates excellent performance on binary classification tasks with an average accuracy of over 98% for each disease condition. The results obtained from this work outperform several other custom-designed CNN architectures as well as pre-trained models-based architectures like ResNet, VGG, DenseNet, Xception, Inception, etc. Furthermore, with proper testing, validation, and debugging of the model in clinical practice, it can be successfully deployed as a decision support system for radiologists. I would like to express my sincere gratitude to my supervisor, Md. Asadur Rahman, Ph.D., Assistant Professor, Department of Biomedical Engineering, MIST, for his effective guidance and support throughout this research work whenever necessary. My sincere gratitude goes to Colonel Syed Mahfuzur Rahman, the respected Head of the Department, for his persistent guidance and financial allocation that made this research work possible. I also extend my profound appreciation to the Department of Biomedical Engineering, MIST, for their co-operations and for facilitating me with the materials and laboratory resources to carry out my research work. I am also indebted to all the individuals who directly or indirectly helped and supported me with their technical and editorial feedback in carrying out my research work. I want to acknowledge Capt Kumar Shrestha, my fellow companion, for his continuous support and cooperation in the journey of completing my M.Sc. Engineering together. Finally, I would like to acknowledge the continuous love and support from my loving family and friends throughout the journey and am grateful to everyone who has made this M.Sc. thesis a reality. 2023-03-01T00:00:00Z http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/818 PREDICTING THE DEPTH OF ANESTHESIA FOR OPERATING PATIENT USING MUSIC-BASED SPECTRAL FEATURES OF EEG SIGNALS RAHMAN, M. N. NASHID In modern practice of major surgery using anesthesia is entirely mandatory. But due to the failure of optimal dose of anesthetic dose delivery it is also common to the patients to face intraoperative and postoperative complications. The main cause of the imbalance dose of anesthesia is not being sure to assess the depth of sleep of the patient or the depth of anesthesia or. Therefore, precise prediction of the depth of anesthesia or the proper assessment of transitional sleep state (from deep sleep to awake) could be a way out to set the optimal anesthetic dose by the anesthesiologist. In this work, a different approach of feature extraction and classification method is proposed to predict three different sleep states during surgery from the EEG signal. This work used an open-source database containing the EEG data of anesthetic patients during surgery. The data were separated into three states: into the deep-sleep state (IntoDeep), the deep-sleep state (InDeep), and the awake state (InAwake). The raw EEG signals were filtered and their power spectral (PSD) densities were calculated using MUSIC (multiple signal classification) model, a parametric method. These MUSIC based PSD values are taken as the features of the EEG signal. An artificial neural network model was trained to develop a machine learning based predictive model with the MUSIC based PSD features. Finally, the predictive model was verified by the data separated for testing and evaluated the prediction accuracy in subject-dependent and subject-independent approach. Eventually, it is found that the results are better than the existing works those worked on the same dataset. 2023-01-01T00:00:00Z