SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12528-022-09344-z.
METHODS: This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models.
RESULTS: The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset.
CONCLUSION: This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.
METHODS: A cross-sectional study was conducted from 21 February 2022 to 15 November 2022. Community pharmacists working in Klang Valley were given a self-administered questionnaire. This survey instrument facilitated the collection of information about their sociodemographic attributes, training background, and knowledge and attitude concerning medical marijuana. Through rigorous analysis of the accumulated data, discernible factors correlating with the levels of knowledge and attitudes surrounding medical marijuana were identified.
RESULTS: The majority (n=149, 53.8%) of participants had low knowledge of medical marijuana. Participants with lower knowledge of medical marijuana tend to have a negative attitude toward medical marijuana. Besides that, male participants showed higher knowledge of medical marijuana than female participants. Furthermore, it was found that atheists had the most negative attitude among other religions toward medical marijuana.
CONCLUSION: Most community pharmacists in Malaysia lack sufficient knowledge about medical marijuana. This indicates that Malaysian pharmacists are not future-ready and need to equip themselves with adequate knowledge of the indications and adverse effects of medical marijuana if it is to be legalised one day. Thus, there is a need for improved training and education of pharmacists around cannabis-based medicines.
METHODS: We used the Autoregressive Moving Average Models (ARIMA) to forecast the number of cases in the upcoming 14 days and the Spearman correlation analysis to analyze the relationship between B.1.1.7 cases and meteorological variables such as temperature, humidity, rainfall, sunshine, and wind speed.
RESULTS: The results of the study showed the fitted ARIMA models forecasted there was an increase in the daily cases in three provinces. The total cases in three provinces would increase by 36% (West Java), 13.5% (South Sumatra), and 30% (East Kalimantan) as compared with actual cases until the end of 14 days later. The temperature, rainfall and sunshine factors were the main contributors for B.1.1.7 cases with each correlation coefficients; r = -0.230; p < 0.05, r = 0.211; p < 0.05 and r = -0.418; p < 0.01, respectively.
CONCLUSIONS: We recapitulated that this investigation was the first preliminary study to analyze a short-term forecast regarding COVID-19 and B.1.1.7 cases as well as to determine the associated meteorological factors that become primary contributors to the virus spread.
METHODS: In this study, a dystrophin-deficient myoblast cell line established from the skeletal muscle of a dystrophic (mdx) mouse was used as a model. The dfd13 (dystrophin-deficient) and C2C12 (non-dystrophic) myoblasts were cultured in low mitogen conditions for 10 days to induce differentiation. The cells were subjected to total protein extraction prior to Western blotting assay technique. Protein sub-fractionation has been conducted to determine protein localization. The live-cell analysis of autophagy assay was done using a flow cytometer.
RESULTS: In our culture system, the dfd13 myoblasts did not achieve terminal differentiation. PTEN expression was profoundly increased in dfd13 myoblasts throughout the differentiation day subsequently indicates perturbation of PI3K/Akt/mTOR regulation. In addition, rictor-mTORC2 was also found inactivated in this event. This occurrence has caused FoxO3 misregulation leads to higher activation of autophagy-related genes in dfd13 myoblasts. Autophagosome formation was increased as LC3B-I/II showed accumulation upon differentiation. However, the ratio of LC3B lipidation and autophagic flux were shown decreased which exhibited dystrophic features.
CONCLUSION: Perturbation of the PTEN-PI3K/Akt pathway triggers excessive autophagosome formation and subsequently reduced autophagic flux within dystrophin-deficient myoblasts where these findings are of importance to understand Duchenne Muscular Dystrophy (DMD) patients. We believe that some manipulation within its regulatory signaling reported in this study could help restore muscle homeostasis and attenuate disease progression. Video Abstract.