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.
METHODS: C. tropicalis isolates from sterile specimens were collected over a 12-month period. Conclusive identification was achieved biochemically with the ID 32 C kit. Susceptibility to nine antifungal agents was carried out using the colourimetric broth microdilution kit Sensititre YeastOne YO10. Biofilm-producing capability was evaluated by quantifying biomass formation spectrophotometrically following staining with crystal violet.
RESULTS: Twenty-four non-repetitive isolates of C. tropicalis were collected. The resistance rates to the triazole agents were 29.2% for fluconazole, 16.7% for itraconazole, 20.8% for voriconazole and 8.3% for posaconazole-the pan-azole resistance rate was identical to that of posaconazole. No resistance was recorded for amphotericin B, flucysosine or any of the echinocandins tested. A total of 16/24 (66.7%) isolates were categorized as high biomass producers and 8/24 (33.3%) were moderate biomass producers. None of our isolates were low biomass producers.
CONCLUSION: The C. tropicalis isolates from our centre were resistant only to triazole agents, with the highest resistance rate being recorded for fluconazole and the lowest for posaconazole. While this is not by itself alarming, the fact that our isolates were prolific biofilm producers means that even azole-susceptible isolates can be paradoxically refractory to antifungal therapy.
MATERIALS AND METHODS: A cross-sectional study was conducted among children who received the COVID-19 vaccine between 3 February and 8 May 2022. Data were collected using a self-administered questionnaire filled out by the parent or legal guardian.
RESULTS: The mean age of the study participants was 9 years old and 43.1% were males. Out of the 195 participants in the study, 62 (31.8%) reported side effects after vaccination. The most frequently reported side effects were pain at the injection site (29.7%, n=58), fever (15.9%, n=31), localised inflammation (10.8%, n=21) and arthralgia/myalgia (9.2%, n=18). There were no reported severe adverse events such as anaphylaxis or myocarditis. Most side effects occurred within the first two days post-vaccination. There was a higher proportion of side effects among children with underlying co-morbidities. No significant differences were observed based on age, weight, ethnicity and the presence of allergies, or the use of premedication.
CONCLUSION: The BNT162b2 vaccine was generally welltolerated in children, with most side effects being mild and self-limiting. These findings support the safety of the COVID-19 vaccine and would guide healthcare professionals, parents and policy-makers in making informed decisions about COVID-19 vaccination, especially among high-risk groups.