Differential persistence of neutralizing antibody against SARS-CoV-2 in post immunized Bangladeshi population.

Development of effective vaccines have been immensely welcomed by the world to prevent the transmission of SARS-CoV-2. However, the duration and clinical implications of antibody-mediated natural immunity in SARS-CoV-2 have not been adequately elucidated alongside some other immune system transforming factors. In a cohort study, we measured NAb titer following the 2nd immunization dosage of the CoviShield (AZD1222) vaccine. The enzyme-linked immunoassay was used to look for SARS-CoV-2-specific NAb. We measured NAb at 30 days after the 2nd dosage of immunization and > 96% titer was detected in 42.9% of subjects, but only 5.1% of subjects retained the same level after 180 days. The median NAb titer dropped significantly, from 92% at 30 days to 58% at 180 days (p < 0.001). Besides, there were significant differences observed in NAb titer after 180 days by age, sex, COVID-19 infection, tobacco use, and asthma patients. However, SARS-CoV-2 infection along with two dosages of immunization upheld NAb titer (p < 0.001) even at the end of the study period.

Development of a Smartphone-Based Expert System for COVID-19 Risk Prediction at Early Stage.

COVID-19 has imposed many challenges and barriers on traditional healthcare systems due to the high risk of being infected by the coronavirus. Modern electronic devices like smartphones with information technology can play an essential role in handling the current pandemic by contributing to different telemedical services. This study has focused on determining the presence of this virus by employing smartphone technology, as it is available to a large number of people. A publicly available COVID-19 dataset consisting of 33 features has been utilized to develop the aimed model, which can be collected from an in-house facility. The chosen dataset has 2.82% positive and 97.18% negative samples, demonstrating a high imbalance of class populations. The Adaptive Synthetic (ADASYN) has been applied to overcome the class imbalance problem with imbalanced data. Ten optimal features are chosen from the given 33 features, employing two different feature selection algorithms, such as K Best and recursive feature elimination methods. Mainly, three classification schemes, Random Forest (RF), eXtreme Gradient Boosting (XGB), and Support Vector Machine (SVM), have been applied for the ablation studies, where the accuracy from the XGB, RF, and SVM classifiers achieved 97.91%, 97.81%, and 73.37%, respectively. As the XGB algorithm confers the best results, it has been implemented in designing the Android operating system base and web applications. By analyzing 10 users' questionnaires, the developed expert system can predict the presence of COVID-19 in the human body of the primary suspect. The preprocessed data and codes are available on the GitHub repository.

An optimum balance among the reduction in ordering cost, product deterioration and carbon emissions: a sustainable green warehouse.

This study involves an optimum balance between ordering cost reduction and product deterioration in controllable carbon emissions for a sustainable green warehouse. The sensitivity analysis is to simulate the impact of those attributes. Industries are foraging to find a proper balance between the use of fossil fuels and reducing carbon emissions, as burning fossil fuels is also indispensable for industrialization. Carbon can emit through inevitable logistic activities in the chains (e.g., lighting, heating, air-conditioning, product deterioration). An industry always attempts to curb those emissions through energy-efficient green technology. The green warehouse is a popular store system in present supply chains to limit the carbons. Product deterioration, particularly for perishable items, is also important for a practitioner to decide how to preserve a perishable product for maximum shelf-life. There is a common tendency among industries to increase order frequencies and volumes in search of a better preservation strategy, increasing the ordering cost and the probability of carbon emissions due to increased transportation. A realistic mathematical model is proposed based on those decision parameters by a sensitivity analysis to demonstrate the impacts. The results showed an increase of 46.30% profit is achieved when all three proposed reduction attributes, but shortages are considered. This improvement is significant without shortage, whereas the increased profit is 94.75%.

Spontaneous skin damage and delayed wound healing in SOD1-deficient mice.

Superoxide dismutase 1 (SOD1) is an important antioxidative enzyme that protects skin from oxidative stress. SOD1 (-/-) mice with a genetic background of b129Sv mice showed facial skin damage after 15 weeks of age. Eyelid swelling occurred as the initial symptom and caused impairment by triggering self-scratching. The period required for wound healing in the back was markedly delayed in 20-week SOD1 (-/-) mice. Oxidative stress markers, 4-hydroxynonenal and thiobarbituric acid-reactive substances, were unexpectedly lower in SOD1 (-/-) mice at day 1 after wounding. The decay rate of electron paramagnetic resonance signal intensity of intravenously injected nitroxide radical indicated that the half-life of the signal intensity was significantly prolonged in the wounded skin of SOD1 (+/+) mice. However, while the half-life of the signal intensity in control skin was a little longer in SOD1 (-/-) mice, it did not change in wounded skin. Taken together, these data suggest that the skin of SOD1 (-/-) mice is in redox imbalance and prone to damage by wounding.