RESUMO
Background: COVID-19 pandemic has indeed plunged the global community especially African countries into an alarming difficult situation culminating into a great deal amounts of catastrophes such as economic recession, political instability and loss of jobs. The pandemic spreads exponentially and causes loss of lives. Following the outbreak of the omicron new variant of concern, forecasting and identification of the COVID-19 infection cases is very vital for government at various levels. Hence, having knowledge of the spread at a particular point in time, swift actions can be taken by government at various levels with a view to accordingly formulate new policies and modalities towards minimizing the trajectory of the consequences of COVID-19 pandemic to both public health and economic sectors. Methods: Here, a potent combination of Convolutional Neural Network (CNN) learning algorithm along with Long Short Term Memory (LSTM) learning algorithm has been proposed in this work in order to produce a hybrid of a deep learning algorithm Convolutional Neural Network - Long Short Term Memory (CNN-LSTM) for forecasting COVID-19 infection cases particularly in Nigeria, South Africa and Botswana. Forecasting models for COVID-19 infection cases in Nigeria, South Africa and Botswana, were developed for 10 days using deep learning-based approaches namely CNN, LSTM and CNN-LSTM deep learning algorithm respectively. Results: The models were evaluated on the basis of four standard performance evaluation metrics which include accuracy, MSE, MAE and RMSE respectively. However, the CNN-LSTM deep learning-based forecasting model achieved the best accuracy of 98.30%, 97.60%, and 97.74% for Nigeria, South Africa and Botswana respectively; and in the same manner, achieved lesser MSE, MAE and RMSE values compared to models developed with CNN and LSTM respectively. Conclusions: Taken together, the CNN-LSTM deep learning-based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana dramatically surpasses the two other DL based forecasting models (CNN and LSTM) for COVID-19 infection cases in Nigeria, South Africa and Botswana in terms of not only the best accuracy of with 98.30%, 97.60%, and 97.74% but also in terms of lesser MSE, MAE and RMSE.
RESUMO
BACKGROUND: Iron-deficiency anemia (IDA) or iron deficiency (ID) is by far the most common form of disorder affecting the cognitive development, physical growth and school performance of children in developing countries including Nigeria. OBJECTIVES: In the present study, we aimed to examine whether IDA or ID, or both are associated with oxidative stress or otherwise by assessing the perturbations in oxidative stress markers including malondialdehyde (MDA), catalase (CAT) and superoxide dismutase (SOD). METHODS: Here, a total of eighty-one IDA, ID, and healthy control subjects of twenty-seven replicates each, were recruited and investigated. Human serum MDA, CAT and SOD levels were quantitatively analyzed using Enzyme-Linked Immunosorbant Assay. RESULTS: Mean serum MDA levels of IDA (5.10 ± 2.35 mmol/L) and ID (4.05 ± 1.35 mmol/L) groups were found to perturb significantly (p < 0.05), being higher than those of control (3.30 ± 0.95 mmol/L) subjects. Similarly, mean serum MDA levels of IDA (5.10 ± 2.35 mmol/L) group was found to be significantly (p < 0.05) higher when compared with ID (4.05 ± 1.35 mmol/L) subjects. Conversely, mean serum CAT and SOD activities of IDA (8.35 ± 2.21 ng/mL and 340.70 ± 153.65 ng/mL) group were found to differ significantly (p < 0.05), and those of ID (9.40 ± 1.47 ng/mL and 435.00 ± 144.75 ng/mL) subjects were found to perturb slightly (p > 0.05), being lower than those of control (10.40 ± 4.31 ng/mL and 482.12 ± 258.37 ng/mL) subjects. CONCLUSIONS: Taken together, the results of the present study showed that lipid peroxidation was dramatically increased in both IDA and ID subjects in hydroperoxide-superoxide-dependent manner; in contrast, enzymatic antioxidant capacity was drastically decreased in both IDA and ID groups as evidenced by biochemical markers.
