OBJECTIVE: The aim of this study was to evaluate the effectiveness of new formulation of lidocaine topical anaesthetic using palm oil base, HAMIN® and to determine how fast this new formulation produces adequate numbness compared to the currently used EMLA cream, in the University of Malaya Medical Centre (UMMC) set-up.
METHOD: The skin permeation test was conducted by using Franz type diffusion cell and pain assessment was carried out in healthy subject by using Verbal Rating Score (VRS) and Visual Analogue Score (VAS) evaluation.
RESULT: Result of permeation test demonstrated that the cumulative amount of lidocaine released from HAMIN® cream was increased with time and slightly higher than EMLA cream. The clinical study showed that HAMIN® single lidocaine cream can produces numbness through venepuncture procedure and comparable with EMLA cream which is a combination therapy for local anaesthetic (lidocaine and prilocaine).
CONCLUSION: It can be concluded that HAMIN® Lidocaine cream is suitable for cream preparation especially for topical application and it can be regarded as an achievement in palm oil and medical industries.
MATERIALS AND METHODS: A cross-sectional study was conducted using an online survey between February and May 2022, with 423 respondents. The questionnaire consisted of socio-demographic, assessment of knowledge level and acceptance level towards COVID-19 vaccine. The descriptive analysis and non-parametric tests were employed to investigate the study outline objectives.
RESULTS: Of all 423 participants, 293 (69.3%) of the participants had a high level of knowledge about the COVID- 19 vaccine (median knowledge score 6; IQR = 3), and 239 (56.5%) were reported to have a low level of vaccine acceptance (median acceptance scores 4; IQR=2). The knowledge level towards the COVID-19 vaccine was significantly associated with the vaccine acceptance level (p<0.001).
CONCLUSION: The community's level of knowledge towards COVID-19 vaccine was high; however, the vaccine acceptance was low.
MATERIALS AND METHODS: A literature search was carried out to gather eligible studies from the following widely sourced electronic databases such as Scopus, PubMed and Google Scholar using the combination of the following keywords: AD, MRS, brain metabolites, deep learning (DL), machine learning (ML) and artificial intelligence (AI); having the aim of taking the readers through the advancements in the usage of MRS analysis and related AI applications for the detection of AD.
RESULTS: We elaborate on the MRS data acquisition, processing, analysis, and interpretation techniques. Recommendation is made for MRS parameters that can obtain the best quality spectrum for fingerprinting the brain metabolomics composition in AD. Furthermore, we summarise ML and DL techniques that have been utilised to estimate the uncertainty in the machine-predicted metabolite content, as well as streamline the process of displaying results of metabolites derangement that occurs as part of ageing.
CONCLUSION: MRS has a role as a non-invasive tool for the detection of brain metabolite biomarkers that indicate brain metabolic health, which can be integral in the management of AD.