NUST Institutions Library Catalogue Search for 'an:"120667"'

Polycaprolactone Nanoparticle Encapsulating Antifouling Ceftaroline for Enhanced In-vitro Controlled Drug Release /

By Hanif, Naeema . . 79p. , Globally, infectious diseases are among the top ten significant contributors to large number of fatalities affecting both developed and developing countries evenly. It has been challenging to treat infections like skin infections due to the global trend of developing antibiotic resistance. In context of this, antibiotics that are effective against both gram-positive and gram-negative organisms and have a long half-life as well as high tissue permeability are needed. Cefepime, a member of the fourth generation intravenous cephalosporins, is one such antibiotic. However, the intravenous administration of drug has restricted the supply of drug to the intended area i.e., skin, instead reaches directly to the blood. This draws attention to the significance of developing a drug delivery system that stays at the skin surface while regulating drug absorption, boosts the drug's bioavailability, and lessens the requirement for frequent drug usage. The current research focuses on developing a chitosan nano-carrier using the ionic gelation method, subsequently transformed into carbopol gel for more effective, longer-lasting skin drug release. SEM analysis shows the spherical morphology of chitosan/alginate nanoparticles with sizes of 156±12.75nm and 222±56nm for both blank and drug-loaded nanoparticles, respectively. Positive zetapotential i.e., 18.2mV indicates polycationic chitosan matrix of the nano-capsules. Finally, the in-vitro drug release study manifested controlled drug release at two different pH (5.5 and 7.4) for a period of 24h. Furthermore, antibacterial activity of nano-formulation as well as the gel was observed against both +Ve and -Ve bacterial strains with better zones of inhibition. The obtained data significantly pointed out that the cefepime nanoparticles loaded carbopol gel would be an encouraging choice for skin infections. 30cm.

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Customization of 3D-Printed Knee Implants: Design Optimization and Lattice Structure Integration fo Enhanced Performance /

By Ahmad, Fatima Ajaz . . 93p. , Total Knee Replacement (TKR) surgeries are becoming increasingly common globally as an effective measure to counter knee arthritis. Total knee replacement implants are very advantageous in a sense that they offer 99% success rate to patients. This thesis presents the design, simulation and additive manufacturing of a patient specific, Functionally Graded Lattice Structure (FGLS) knee implant in Ti 6Al-4V alloy with specific reference to the healthcare situation in Pakistan. This was to explore the local manufacturing facilities of Pakistan as all knee implants are imported from abroad. The strategy involved a high degree of workflow consisting of Computer-Aided Design (CAD), finite element analysis (FEA), and topology optimization using nTopology to create Gyroid-based lattice work. The structures were to resemble the trabecular bone structure to ensure that stiffness discrepancies were minimized. This helped counter only one drawback of solid knee implants, stress shielding. The simulations of the physiological loading conditions (static and cyclic) demonstrated a Von Mises peak of 620.45 Mpa and safety factor of 12.66 on the average and unlimited predicted life of fatigue of over 10^7 cycles. The use of FGLS was effective in making the weight of 490 g to 292, leading to a 40 percent weight reduction, with no structural integrity lost. Selective Laser Melding (SLM) was used to fabricate the implant and the heat treatment allowed stress relieving of the additively manufactured implant. Compressive testing was also mechanically vindicated to be on an of average 95.02 kN with little variation and Micro-CT scanning confirmed high dimensional fidelity and showed internal lattice geometries without defects. According to this research, SLM produced FGLS implants usage has proven to be an option to traditional prosthetics, which is mechanically stable, biologically desirable, and cost-effective, and has a bright future of being a locally manufactured orthopedics product. 30cm.

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Social Media Analytics for Mental Health Assessment /

By Liaquat, Umna . . 94p. , Social media has emerged as a tool for exploring the human psyche, offering exceptional access to real-time behavioral signals that are transforming the landscape of computational psychiatry. Among these, bipolar disorder is difficult to detect because of its volatility, requiring robust modeling of emotion, behavior, and timing. Prior studies have largely focused on text-based sentiment analysis and linguistic features to classify mental health conditions; however, these approaches often neglect non-verbal markers such as circadian rhythms and affective variability. Existing models primarily depend on static textual cues, limiting their ability to capture the dynamic, multimodal nature of psychiatric expression. This study addresses these limitations by integrating temporal rhythms, emotional dynamics, and behavioral signals extracted from Reddit user histories to develop predictive models of bipolar disorder and high-risk psychological states. We propose a series of interpretable multimodal architectures employing classical machine learning (Logistic regression, Random Forest, and XGBoost), deep sequence models (LSTM, GRU), and transformer-based frameworks (Roberta, GPT). Our approach incorporates temporal posting features, emotional entropy, and community-level interaction structures. Compared to benchmark studies, our models demonstrate significant improvements in both classification (F1 > 0.99) and regression (R² > 0.89), highlighting the predictive power of fused behavioral signals. This work advances the field by providing a scalable, languageindependent framework for the early detection of psychiatric risk. It also holds broader implications for public health by offering a foundation for real-time, ethically deployable digital mental health tools. 30cm.

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