In this work, two different methods (sol-gel and biosynthesis) were adopted for the synthesis of zinc oxide (ZnO) nanoparticles. The leaf extract of Azadirachta Indica (Neem) was utilized in the biosynthesis scheme. Structural, antibacterial, photocatalytic and optical performances of the two variants were analyzed. Both variants demonstrated a wurtzite hexagonal structure. The biosynthesized variant (25.97 nm) exhibited smaller particles than that of the sol-gel variant (33.20 nm). The morphological analysis revealed that most of the particles of the sol-gel variant remained within the range of 15 nm to 68 nm while for the biosynthesized variant the range was 10-70 nm. The antibacterial assessment was redacted by using the agar well diffusion method in which the bacteria medium was Escherichia coli O157: H7. The zone of inhibition of bacterial growth was higher in the biosynthesized variant (14.5 mm). The photocatalytic performances of the nanoparticles were determined through the degradation of methylene blue dye in which the biosynthesized variant provided better performance. The electron spin resonance (EPR) analysis revealed that the free OH·radicals were the primary active species for this degradation phenomenon. The absorption band of the sol-gel and biosynthesized variants were 363 nm and 356 nm respectively. The optical band gap energy of the biosynthesized variant (3.25 eV) was slightly higher than that of the sol-gel variant (3.23 eV). Nevertheless, the improved antibacterial and photocatalytic responses of the biosynthesized variants were obtained due to the higher rate of stabilization mechanism of the nanoparticles by the organic chemicals (terpenoids) present in the Neem leaf extract.

Objectives: Extreme fear of academic delay (FAD) and psychological distress among students have arisen as great public health concerns worldwide due to the devastating actions of coronavirus disease 2019 . The precise aim of this study was to assess the impact of ongoing online education on current university students' FAD and psychological stress symptoms following one year of calamitous COVID-19 outbreak in Bangladesh. Methods: A cross-sectional web-based survey was conducted from March 15 to 30, 2021, for data collection through a snowball simple sampling technique among Bangladeshi University students, where a total of 1,299 respondents (age: ! 18 years) responded in the questionnaire. After obtaining informed consent from the participants, we evaluated the association of various sociodemographic factors and the effects of current e-Learning activities on FAD and subsequent psychological distress among university students in Bangladesh. After excluding the partial responses (n ¼ 177), we analyzed the clean data sheet (n ¼ 1,122) by three consecutive statistical methods: univariate, bivariate, and multivariate analyses. Results: Alarmingly, near 60% of the current students exerted extreme FAD and were suffering from severe stress. Besides, 78.1% of students having severe FAD were severely psychologically stressed. Logistic regression analyses revealed that the students of the female gender, rural area, lower-income families, and who suffered from the highest FAD were more significantly (p < 0.05) stressed than their reference groups. Conclusion:The current analysis demonstrates that most Bangladeshi university students are battling with the unrivaled trend of FAD and facing severe psychological stress symptoms, which must be alleviated by the concerted efforts of the Government, Universities, and educationalists.

Health professionals often prescribe patients to perform specific exercises for rehabilitation of several diseases (e.g., stroke, Parkinson, backpain). When patients perform those exercises in the absence of an expert (e.g., physicians/therapists), they cannot assess the correctness of the performance. Automatic assessment of physical rehabilitation exercises aims to assign a quality score given an RGBD video of the body movement as input. Recent deep learning approaches address this problem by extracting CNN features from co-ordinate grids of skeleton data (body-joints) obtained from videos. However, they could not extract rich spatio-temporal features from variable-length inputs. To address this issue, we investigate Graph Convolutional Networks (GCNs) for this task. We adapt spatiotemporal GCN to predict continuous scores(assessment) instead of discrete class labels. Our model can process variable-length inputs so that users can perform any number of repetitions of the prescribed exercise. Moreover, our novel design also provides self-attention of body-joints, indicating their role in predicting assessment scores. It guides the user to achieve a better score in future trials by matching the same attention weights of expert users. Our model successfully outperforms existing exercise assessment methods on KIMORE and UI-PRMD datasets.

The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a possible vaccine, early detection and containment are the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers’ interest. This survey marks a detailed inspection of the deep learning–based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets and others, along with probable solutions with different preprocessing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, normal, and pneumonia from X-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset . Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

Introduction:The pathogenic and highly transmissible etiological agent, SARS-CoV-2, has caused a serious threat COVID-19 pandemic. WHO has declared the epidemic a public health emergency of international concern owing to its high contagiosity, mortality rate, and morbidity. Till now, there is no approved vaccine or drug to combat the COVID-19 and avert this global crisis. Areas covered: In this narrative review, we summarized the updated results (January to August 2020) of the most promising repurposing therapeutic candidates to treat the SARS-CoV-2 viral infection. The repurposed drugs classified under four headlines like antivirals, anti-parasitic, immune-modulating, and miscellaneous drugs were discussed with their in vitro efficacy to recent clinical advancements against COVID-19. Expert opinion: Currently, palliative care, ranging from outpatient management to intensive care, including oxygen administration, ventilator support, intravenous fluids therapy, with some repurposed drugs, are the primary weapons to fight against COVID-19. Until a safe and effective vaccine is developed, an evidence-based drug repurposing strategy might be the wisest option to save people from this catastrophe. Several existing drugs are now under clinical trials, and some of them are approved in different places of the world for emergency use or as adjuvant therapy in COVID-19 with standard of care.