Pusat Pengajian Sains Farmasi - Tesis

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Development And Impact Assessment Of Consumer Medication Information On Health Outcomes Among Type II Diabetes Mellitus Patients In Qatar
(2019-06)
Munsour, Emad Eldin Osman Ali
Health literacy (HL) is fundamental to patient engagement, medication - taking behavior, and decision - making about treatment . Patients with limited HL have difficulty in reading and interpreting medication instructions outlined in the patient information leaflets (PILs). P IL is the written document produced by the pharmaceutical manufacturers and packed with the medicine; they are often of poor quality and hard to comprehend by the patients. Additionally, no documented evidence that the available PILs consider cultural and behavioral perspectives of diverse populations residing in a country like Qatar. The objective of Phase 1 of this thesis was to evaluate the readability and comprehensibility of PILs supplied with medications used for the treatment of type 2 diabetes melli tus (T2DM) in Qatar using the Flesch Reading Ease (FRE) score for readability, and Flesch - Kincaid Grade Level, Gunning - Fog Index, and SMOG Grading for comprehensibility in terms of school grade levels. Nine (20%) of the PILs were in English only; 17.8% wer e in English, Arabic, and French; and 62.2% were in English and Arabic. The mean FRE score of the 45 PILs was 37.71 (±15.85), and the most readable PIL had FRE score of 62. Furthermore, only 2.2% of PILs had acceptable FRE readability scores (i.e. 60 - 70% o r higher). All the PILs could be comprehended by at least an 11th grade student, which exceeded the recommended 6 th to 8 th grade level for health - related materials. Phase 2 of this thesis aimed at comparing written medicine information (WMI) of antidiabeti c medications authorized for marketing in Qatar, Australia, and
Analysis And Production Of Artemisinin, An Antimalarial From Artemisia Annua L
(1996)
Sunil U.A., Jinadasa
Artemisia amma derived from a heterogeneous seed population obtained from north viet nam, grew well in the local climate but did not produce seeds upon flowering. The plant was successfully propagated using a tissue culture technique. The artemisinin content varied among the plantlets. The plantiets were selected and cloned to give 3 major yielding lines of high, moderate and low artemisinin content. The high yielding plantlets, were found to have a mean artemisinin content of 0.41 % ± 0.02 sd, the moderate yielders, a mean arternisinin content of 0.25 % ± 0.02 sd, whereas the low yielders, a mean artemisinin content of 0.13 % ± 0.03 sd. Low and high yielding plants in the field contained a mean artemisinin content of 0.13 % ± 0.0 l sd and 0.33 % ± 0.01 sd, respectively. A high performance liquid chromatography method with electrochemical detection using a glassy carbon electrode was developed and was found to be sensitive and selective for the detection of artemisinin in plasma. The mean recovery values of arternisinin from the extraction, over a range of 25-800 ng ml-l were 88.86-97.68 % with a coefficient variation of 1.57-9.31 % (n=6). The detection limit was 12.5 ng ml-l for artemisinin at a signal to noise ratio of 5: 1.
DLP 3D Printing In The Development Of Carboxymethyl Cellulose Hydrogel For Dermal Drug Delivery
(2025-07)
Ong, Rong Rong
Hydrogel is attractive for dermal drug delivery due to its huge drug reservoir and biocompatibility. To allow rapid fabrication of customisable hydrogels, the development of carboxymethyl cellulose (CMC) hydrogel using digital light processing (DLP) 3D printing is worth to be explored. Yet, the printing accuracy of hydrogel is affected by the light scattering effect in DLP 3D printing. Hence, this thesis aims to develop 3D printed CMC hydrogels using DLP by studying the effect of tartrazine as the photoabsorber on hydrogel properties and exploring its dermal drug delivery potential. Cylindrical hydrogels were fabricated using DLP 3D printer with customised resin consisting of methacrylated CMC (M-CMC) synthesised from CMC, lithium phenyl(2,4,6-trimethylbenzoyl)phosphinate as photoinitiator and tartrazine (0.005 – 0.025%w/v). The printed hydrogels were characterised for dimension accuracy and mechanical properties. Selected formulations were added with niacinamide (4%w/v) and solvents for further characterisation and in vitro drug release and skin permeation studies.
Neuroprotective Effects Of Diterpenoids-Enriched Fraction Of Andrographis Paniculata And Andrographolide In Ischaemia-Induced Neuronal Death And Cognitive Impairment: In Vitro And In Vivo Models
(2025-08)
Che Lamin, Roz Azinur
Andrographis paniculata (Burm.f.) Wall. ex Nees, known as “hempedu bumi”, has been studied for its cognitive-enhancing and neuroprotective effects. However, the study on the potential of A. paniculata diterpenoids-enriched fraction against learning and memory impairment is limited, particularly in vascular-pathology-related dementia. Thus, this study aimed to enhance the methanolic extract of A. paniculata via the enrichment of diterpenoids through a fractionation process and evaluate its cognitive-enhancing and neuroprotective effects along with andrographolide, the major diterpenoid of A. paniculata, using in vitro and in vivo models.
Integrating Artificial Intelligence For Personalized Cancer Pain Management: A Comprehensive Study On Healthcare Professionals’ Perspectives, Development Of Pain Detection Method And User’s Acceptance
(2025-09)
Tashtoush, Noor Hasan Ibrahim
Cancer pain is a prevalent and distressing symptom that significantly impairs the quality of life in oncology patients. Traditional pain assessments rely heavily on subjective self-reporting, which may be limited by communication, or cognitive impairment. With the rise of wearable technologies, smartwatches offer a promising avenue for non-invasive, continuous monitoring of physiological signals associated with pain, enabling more objective assessment and real-time support. This research explores the integration of Artificial Intelligence (AI) in personalized cancer pain management across three phases. Phase I investigated healthcare professionals’ Knowledge, Attitudes, and Practices (KAP) and barriers regarding cancer pain and AI tool adoption through semi-structured interviews with thirteen purposively selected doctors and nurses from Advanced Medical and Dental Institute. In-depth interviews revealed strong understanding of conventional pain assessment methods and openness to AI-supported solutions. However, concerns about trust, training, and infrastructure emerged as critical barriers to implementation. Phase II focused on the development and validation of AI models for objective pain classification based on continuous, real-time physiological data (heart rate, respiratory rate, and blood pressure) collected via smartwatches over a 72-hour period.

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