Development of Gelatin/Alginate Based Sponge by Freeze-Gelation Technique for Potential Biomedical Applications

Student: Nahida Sultana

Supervisor: Dr. Muhammad Tarik Arafat, Professor

Abstract:
This project explores the fabrication of a biocompatible gelatin/alginate sponge using the freeze-gelation method. The study focuses on optimizing the material’s porosity and mechanical properties for potential use in wound dressing and tissue engineering. Initial characterization suggests that the resulting sponge may support cell adhesion and fluid absorption.


Development of CuO and Naproxen Loaded PVA/Chitosan Electrospun Fiber Matrix

Student: Sk. Faijus Sadekin

Supervisor: Dr. Muhammad Tarik Arafat, Professor

Abstract:
This work investigates an electrospun fiber matrix composed of PVA and chitosan, co-loaded with copper oxide nanoparticles and naproxen. The aim is to develop a multifunctional scaffold with anti-inflammatory and antimicrobial potential. Physicochemical characterization was performed to assess fiber morphology and drug incorporation efficiency.


Tuning the Functional Properties of Chitosan Nanoparticles by Controlling Ionic Gelation for Biomedical Application

Student: Md. Mehedi Anas

Supervisor: Dr. Muhammad Tarik Arafat, Professor

Abstract:
This study examines the impact of ionic gelation parameters on the physicochemical properties of chitosan nanoparticles. By modulating factors such as pH, crosslinker concentration, and mixing rate, the work aims to optimize particle stability and size for applications such as drug delivery or surface coating.


Development of Doxorubicin Loaded Different Carbonic Acid Modified and Thermally Sintered Hydroxyapatite as a Nanocarrier

Student: Noumi Farnaj

Supervisor: Dr. Muhammad Tarik Arafat, Professor

Abstract:
This research focuses on modifying hydroxyapatite with various carbonic acid treatments followed by sintering, for use as a nanocarrier for doxorubicin. The drug loading efficiency, release behavior, and structural properties of the synthesized carriers were evaluated to explore their suitability for targeted cancer therapy.


Formulation of Mucin Nanoparticles by Ionic Gelation Technique

Student: Md. Ashakul Islam Sowad

Supervisor: Dr. Muhammad Tarik Arafat, Professor

Abstract:
Mucin nanoparticles were formulated using ionic gelation with the goal of developing a mucoadhesive platform for localized drug delivery. The study characterizes particle size, zeta potential, and encapsulation efficiency, aiming to assess stability and potential bioadhesive behavior.


Gelatin-CMC Based Electrospun Cross-Linked by Tannic Acid with Antibacterial Properties

Student: Rafia Hasnat Jinia

Supervisor: Dr. Muhammad Tarik Arafat, Professor

Abstract:
This work presents the development of a gelatin-CMC nanofiber scaffold cross-linked with tannic acid. The scaffold’s mechanical and antibacterial properties were evaluated, indicating potential for use as a wound dressing material with enhanced structural integrity and antimicrobial response.


Development of Tannic Acid Cross-Linked Chitosan-Starch Based Antibacterial Composite for Rapid Hemostasis

Student: Lamiya Hassan Tithy

Supervisor: Dr. Muhammad Tarik Arafat, Professor

Abstract:
This thesis explores a composite material based on chitosan and starch cross-linked with tannic acid, designed for rapid hemostasis. The study includes assessments of swelling behavior, antibacterial activity, and preliminary coagulation tests, suggesting the material may be a promising candidate for emergency wound care.


Disease Detection from Chest X-ray Images Using Deep Learning

Students: Md. Iqbal Hossain, S. M. Jawwad Hossain

Supervisor: Dr. Taufiq Hasan, Professor

Abstract:
A deep learning-based framework was developed to classify abnormalities in chest X-ray images. The model was trained and validated using publicly available datasets to identify common thoracic conditions. The results indicate that the method holds promise for supporting radiological diagnosis.


COVID-19 Infection Detection from Lung CT Scan Images

Students: Tanzila Binti Alam, Alfaj Uddin Ahmed, Prithwi Raj Das

Supervisor: Dr. Taufiq Hasan, Professor

Abstract:
This study applies convolutional neural networks to detect COVID-19 from lung CT images. Data augmentation and preprocessing techniques were used to enhance model robustness. Preliminary results show encouraging sensitivity in distinguishing infected lungs from non-infected cases.


Respiratory Distress Detection from Speech Signals

Students: Manzil-E-Akbar Khan, Fardeen Ahmed

Supervisor: Dr. Taufiq Hasan, Professor

Abstract:
The project explores the use of machine learning to detect respiratory distress through analysis of speech features. Various acoustic parameters such as pitch, formants, and energy distribution were analyzed to train classification models. The approach aims to support non-invasive respiratory monitoring.


Measurement of Cell-Phone Tower Radiation Inside Dhaka City and Effect on Public Health

Students: Md. Atiquzzaman Atiq, Nusaifa Nafsin Neha

Supervisor: Dr. Jahid Ferdous, Associate Professor

Abstract:
This study measures electromagnetic radiation levels from mobile towers in different areas of Dhaka and compares them to international safety standards. A literature-based review was conducted on possible health implications. The results provide a preliminary overview for public health policy discussions.


Preparation of Tunable Tannic Acid-Gelatin Bioadhesive with Antimicrobial Properties

Student: Soham Irtiza Swapnil

Supervisor: Dr. Muhammad Tarik Arafat, Professor

Abstract: 
This work focuses on developing a gelatin-based bioadhesive cross-linked with tannic acid. By varying the crosslinking ratio, the mechanical strength and antimicrobial properties were tuned. The material is intended for topical applications where both adhesion and infection control are critical.


Dual Drug Delivery from Sodium Alginate Nanoparticles

Student: Nondita Datta

Supervisor: Dr. Muhammad Tarik Arafat, Professor

Abstract:
Sodium alginate nanoparticles were engineered for the co-delivery of two model drugs. The project evaluates encapsulation efficiency, particle size, and in vitro release kinetics. The system shows potential for controlled delivery in multi-drug treatment regimens.


Heart Sound Enhancement and Classification

Students: Samiul Based Shuvo, Shams Nafisa Ali

Supervisor: Dr. Taufiq Hasan, Professor

Abstract:
This research aims to enhance and classify heart sounds using signal processing and deep learning. Denoising techniques were applied to improve audio quality, followed by classification of pathological and normal heart sounds. The system may serve as a screening aid in low-resource settings.


A Computational Approach to Identify Bile Salt Export Pump Inhibitors for Predicting Drug-Induced Liver Injury

Students: Ragib Abid, Ridoy Chandra Shil

Supervisor: Dr. Jahid Ferdous, Associate Professor

Abstract:
This thesis utilizes molecular docking and pharmacophore modeling to identify potential BSEP inhibitors, which are associated with drug-induced liver injury. Several candidate molecules were analyzed for binding affinity and interaction patterns, contributing to safer drug design pipelines.


Amino Functional Group-Based Prophylactic Antibiotic Loaded Hydrogel for Surgical Site Infection in Spine Surgery

Students: Nazifa Tasnim Ahmad, Fiaz Ahmed

Supervisor: Dr. Jahid Ferdous, Associate Professor

Abstract:
A hydrogel system incorporating antibiotics via amino-functionalized carriers was developed to prevent postoperative infections in spinal surgery. Characterization included swelling behavior, drug release profile, and preliminary antimicrobial testing. The hydrogel shows potential for localized prophylaxis.


Computational Study of Lung Airflow Pattern for Multiple Disease Conditions

Students: Tashfiq Ahmed, Hadi Ul Bashar

Supervisor: Dr. Jahid Ferdous, Associate Professor

Abstract:
This project simulates lung airflow under healthy and diseased conditions using computational fluid dynamics (CFD). 3D airway models were used to examine flow resistance and shear stress patterns, providing insights into airflow obstruction and helping visualize disease progression.