Though many countries have already launched COVID-19 mass vaccination programs to control the disease outbreak quickly, numerous countries around worldwide are grappling with unprecedented surges of new COVID-19 cases due to a more contagious and deadly variant of coronavirus. As the number of new cases is skyrocketing, pandemic fatigue and public apathy towards different intervention strategies pose new challenges to government officials to combat the pandemic. Henceforth, it is indispensable for the government officials to understand the future dynamics of COVID-19 flawlessly to develop strategic preparedness and resilient response planning. In light of the above circumstances, probable future outbreak scenarios in Brazil, Russia, and the United kingdom have been sketched in this study with the help of four deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN) and multivariate convolutional neural network (MCNN). In our analysis, the CNN algorithm has outperformed other deep learning models in terms of validation accuracy and forecasting consistency. It is unearthed in our study that CNN can provide robust long-term forecasting results in time-series analysis due to its capability of essential features learning, distortion invariance, and temporal dependence learning. However, the prediction accuracy of the LSTM algorithm has been found to be poor as it tries to discover seasonality and periodic intervals from any time-series dataset, which were absent in our studied countries. Our study has highlighted the promising validation of using convolutional neural networks instead of recurrent neural networks when forecasting with very few features and less amount of historical data.

When the entire world is waiting restlessly for a safe and effective COVID-19 vaccine that could soon become a reality, numerous countries around the globe are grappling with unprecedented surges of new COVID-19 cases. As the number of new cases is skyrocketing, pandemic fatigue and public apathy towards different intervention strategies are posing new challenges to the government officials to combat the pandemic. Henceforth, it is indispensable for the government officials to understand the future dynamics of COVID-19 flawlessly in order to develop strategic preparedness and resilient response planning. In light of the above circumstances, probable future outbreak scenarios in Bangladesh, Brazil, India, Russia and the United kingdom have been sketched in this study with the help of four deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN) and multivariate convolutional neural network (MCNN). In our analysis, CNN algorithm has outperformed other deep learning models in terms of validation accuracy and forecasting consistency. Importantly, CNN model showed clear indications of imminent second wave of COVID-19 in the above-mentioned countries. It has been unearthed in our study that CNN can provide robust long term forecasting results in time series analysis due to its capability of essential features learning, distortion invariance and temporal dependence learning. However, the prediction accuracy of LSTM algorithm has been found to be poor as it tries to discover seasonality and periodic intervals from any time series dataset, which were absent in our studied countries. Our results enlighten some surprising results that the studied countries are going to witness dreadful consequences of second wave of COVID-19 in near future. Quick responses from government officials and public health experts are required in order to mitigate the future burden of the pandemic in the above-mentioned countries.

Like all other sectors of the economy, the halt in economic activities due to the outbreak of COVID-19 in Bangladesh has adversely affected SMEs. Despite constituting over 90 percent of business enterprises in the country, this sector has not grown enough due to a variety of reasons. Coupled with these, the pandemic has had a major effect on the operation of SMEs. Using a descriptive analysis method, this study tries to find the effect on enterprises, the way they have tried to cope with the situation, and the extent to which they have recovered from the phase. While the Government announced stimulus packages in different rounds, the study has found that commercial banks' perception and reluctant attitude towards small and medium entrepreneurs worked as the main reason behind enterprises' failure to receive the money. The study argues that the SME Foundation of Bangladesh needs to play a proactive role in minimizing the gap between the enterprises and banks, and a separate state-owned bank needs to be determined through further research and analysis.

Objective: Was to assess the mandibular asymmetry on panoramic radiograph (OPG) as compared to PA (posterior anterior) cephalogram. Materials and method: A total of 15 participant’s records with age above 12 years; both genders; Pakistani nationals; clear OPG and PA cephalogram available; and full biodata available were included. Radiographs of participants with unerupted or missing teeth in anterior or posterior region were excluded. Three linear measurements (condylar, ramus, and body length) and one angular measurement (gonial angle) were performed on both OPG and PA cephalograms. Paired t test and Pearson correlation test were applied between OPG and PA cephalogram for condylar, ramus, and body length and gonial angle to see the relationship. Results: The females were 6(40%) and males were 9(60%). The mean condylar length measured on OPG was 8.44 ± 2.96mm and on PA cephalogram was 9.98 ± 2.73mm with mean difference of 1.5mm and no statistical difference (p=0.1007). Similarly the ramus length (p=0.706), corpus length (p=0.066) and gonial angle (p=0.333) were not statistically different measured on OPG and PA cephalogram. Very high correlation was found for measurements on OPG and PA cephalogram for condylar length(r=0.97), ramus length (r=0.96), body length (r=0.93) and was very highly statistically significant (p<0.001). But the correlation for gonial angle was moderate and not statistically significant (p=0.035). Conclusion: The panoramic radiograph can be used for initial diagnosis of mandibular asymmetry. Keywords: Mandibular asymmetry, panoramic radiograph, posterior-anterior cephalogram

Background: Hepatitis C is a diverse illness that causes significant death and morbidity. The hepatitis C virus infects hundreds of millions of individuals globally (HCV). More than 80% of those infected develop chronic infection; the remaining 10–20% recover spontaneously through natural immunity. Acute hepatitis is only icteric in 20% of individuals and is seldom severe. Methods: A pilot study was conducted at INOR hospital Abbottabad. Eleven hepatitis C positive and 10 hepatitis C negative participants were included in the study. Results: A significant difference correlation was found between viral load and SWE quantification for fibrosis stage in Kilo-Pascal, r= 0.904 (p-value=0.000 <a=0.05). HCV positive patients showed a viral load of (Mean±SD) 128,185.8±153,719.1. Conclusion: Although a biopsy is considered to be gold standard for determining the degree of damage caused by chronic viral hepatitis, it is far from perfect. Liver elastography is an intriguing technique that can help physicians make difficult decisions while treating viral hepatitis. This study showed that fibrotic changes of liver are directly proportional to the presence of viral load in blood. The greater the viral load more severe fibrosis will be seen. Age also plays a role in severity of fibrosis, however, more studies on larger population is required to support this statement.