M.Phil / MS

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Deep Learning Model for Identifying Organ at Risk Toxicity in Thorax Carcinomas Caused by Radiation Therapy
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Muhammad Mutayyab Ilyas; SP19-RPH-012; Dr. Naima Amin; LHR TP 7253
To assist doctors in making individualized treatment decisions, a machine learning algorithm that can accurately predict toxicity could be used. Patients undergoing thoracic radiation treatment often have organ at risk (OAR) toxicity, which may have a major effect on their health. The use of radiation therapy to treat thorax cancer has been shown to be beneficial. Toxins associated with treatment, on the other hand, are a different story. Treatment-related toxicity is a significant issue because cancer spreads to healthy cells and organs in close proximity. The use of radionics in therapy control strategies has been studied in a number of research features extracted from computed tomography (CT), magnetic resonance imaging (MRI), or dosimetric features collected by 3D dose distributions (DD). Convolutional Neural Networks (CNN) are being used to investigate the toxicity of organs in danger. Image processing, treatment planning, treatment delivery, and post-therapy follow-up are all feasible with a deep learning model. As well as by optimising Using convolution filters during the training stage, a deep learning-based prediction model may discover and fine-tune variables for particular categorization issues. The potential of a deep learning-based estimate technique to reduce the radiation treatment hazard rate is a significant benefit. Furthermore, using convolution methods, the attributes may be obtained automatically (CNN). The objective of this study is to create and test a 3D CNN-based toxicity prediction model. CT images, radiation treatment DD, and contours will be used to extract low and large temporal spatial features utilising (3D) filters, especially in low and large temporal spatial characteristics. The comparative test will demonstrate that the planned model is capable of forecasting OAR toxicity properly. Research into more specific criteria for places that are closely connected to OAR Toxicity might improve the model even further.
Utilization of Ultraviolet radiations for the closure of wounds
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Anzal Akbar; SP19-RPH-021; Dr. Naima Amin; LHR TP 6371
Acerbic Fluorescent Spectroscopy of Carbon dots for Biomedical Applications
(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) FAZAL UL NISA; CIIT/SP19-RPH-007/LHR; Dr. Naima Amin; LHR TP 6370
Carbon nanodots are well known in biomedical aspects due to their novel fluorescent properties. Nitrogen based carbon nanodots has showed the great photoluminescence and increased fluorescence quantum yield. The pH in living cells is very important as a lot of physiological systems are working in our body to keep them within a specific range. A slight change in pH can cause imbalance in cell function and growth. There are many diseases, which are associated with pH change (acidic pH) such as cancer, tuberculosis, and infections. Hence, a sensitive and selective system is needed to monitor the pH in living cells. The composition of carbon nanodots with Polypropylene glycol and 1,4- benzenediamine showed better chemical stability, high biocompatibility, intense photoluminescence. Therefore, they can be used to evaluate pH detection in living cells. The purpose of this study is to produce nitrogen-based carbon nanodots that are susceptible to show photoluminescence properties and high quantum yield. NCDs were synthesized via Microwave assisted method which is a very facile and cost-effective technique. The synthesized carbon nanodots were then characterized by using FT-IR, AFM, UV-Vis and Fluorescence spectroscopy. FT-IR spectra confirmed the presence of carboxyl functional groups on the surface of carbon nanodots. AFM results indicates that the produced carbon nanodots are spherical and have diameter in the range of 1-5 nm. All these experiments confirm that synthesized carbon nanodots possess high photoluminescence, better quantum yield henceforward they can be used for pH detection and other biomedical applications

M.Phil / MS

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