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Background and objective: Hypertension is a potentially dangerous health condition that can be detected by measuring blood pressure (BP). Blood pressure monitoring and measurement are essential for preventing and treating cardiovascular diseases. Cuff-based devices, on the other hand, are uncomfortable and prevent continuous BP measurement. Methods: In this study, a new non-invasive and cuff-less method for estimating Systolic Blood Pressure (SBP), Mean Arterial Pressure (MAP), and Diastolic Blood Pressure (DBP) has been proposed using characteristic features of photoplethysmogram (PPG) signals and nonlinear regression algorithms. PPG signals were collected from 219 participants, which were then subjected to preprocessing and feature extraction steps. Analyzing PPG and its derivative signals, a total of 46 time, frequency, and time-frequency domain features were extracted. In addition, the age and gender of each subject were also included as features. Further, correlation-based feature selection (CFS) and Relief F feature selection (ReliefF) techniques were used to select the relevant features and reduce the possibility of over-fitting the models. Finally, support vector regression (SVR), K-nearest neighbour regression (KNR), decision tree regression (DTR), and random forest regression (RFR) were established to develop the BP estimation model. Regression models were trained and evaluated on all features as well as selected features. The best regression models for SBP, MAP, and DBP estimations were selected separately. Results: The SVR model, along with the ReliefF-based feature selection algorithm, outperforms other algorithms in estimating the SBP, MAP, and DBP with the mean absolute error of 2.49, 1.62 and 1.43 mmHg, respectively. The proposed method meets the Advancement of Medical Instrumentation standard for BP estimations. Based on the British Hypertension Society standard, the results also fall within Grade A for SBP, MAP, and DBP. Conclusion: The findings show that the method can be used to estimate blood pressure non-invasively, without using a cuff or calibration, and only by utilizing the PPG signal characteristic features.

The ongoing pandemic COVID-19 caused by Severe Acute Respiratory Coronavirus-2 (SARS-CoV-2) has wreaked havoc globally by affecting millions of lives. Although different countries found the implementation of emergency measures useful to combat the viral pandemic, many countries are still experiencing the resurgence of COVID-19 cases with new variants even after following strict containment guidelines. Country-specific lessons learned from the ongoing COVID-19 pandemic can be utilized in commencing a successful battle against the potential future outbreaks. In this article, we analyzed the overall scenario of the COVID-19 pandemic in Bangladesh from Alpha to Omicron variant and discussed the demographic, political, economic, social, and environmental influences on the mitigation strategies employed by the country to combat the pandemic. We also tried to explore the preparedness and precautionary measures taken by the responsible authorities, the choice of strategies implemented, and the effectiveness of the response initiated by the government and relevant agencies. Finally, we discussed the possible strategies that might help Bangladesh to combat future COVID-19 waves and other possible pandemics based on the experiences gathered from the ongoing COVID-19 pandemic.

The Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) became a pandemic, resulting in an exponentially increased mortality globally and scientists all over the world are struggling to find suitable solutions to combat it. Multiple repurposed drugs have already been in several clinical trials or recently completed. However, none of them shows any promising effect in combating COVID-19. Therefore, developing an effective drug is an unmet global need. RdRp (RNA dependent RNA polymerase) plays a pivotal role in viral replication. Therefore, it is considered as a prime target of drugs that may treat COVID-19. In this study, we have screened a library of compounds, containing approved RdRp inhibitor drugs that were or in use to treat other viruses (favipiravir, sofosbuvir, ribavirin, lopinavir, tenofovir, ritonavir, galidesivir and remdesivir) and their structural analogues, in order to identify potential inhibitors of SARS-CoV-2 RdRp. Extensive screening, molecular docking and molecular dynamics show that five structural analogues have notable inhibitory effects against RdRp of SARS-CoV-2. Importantly, comparative protein-antagonists interaction revealed that these compounds fit well in the pocket of RdRp. ADMET analysis of these compounds suggests their potency as drug candidates. Our identified compounds may serve as potential therapeutics for COVID-19.