Comprehensive analysis of SARS-COV-2 drug targets and pharmacological aspects in treating the COVID-19

Electroactive nanofibrous membrane with antibacterial and deodorizing properties for air filtration

Water vapor from respiration can severely accelerate the charge dissipation of the face mask, reducing filtration efficiency. Moreover, the foul odor from prolonged mask wear tends to make people remove their masks, leading to the risk of infection. In this study, an electro-blown spinning electroactive nanofibrous membrane (Zn/CB@PAN) with antibacterial and deodorization properties was prepared by adding zinc (Zn) and carbon black (CB) nanoparticles to the polyacrylonitrile (PAN) nanofibers, respectively. The filtration efficiency of Zn/CB@PAN for PM0.3 was > 99% and could still maintain excellent durability within 4 h in a high-humidity environment (25 ℃ and RH = 95%). Moreover, the bacterial interception rate of the Zn/CB@PAN could reach 99.99%, and it can kill intercepted bacteria. In addition, the deodorization rate of Zn/CB@PAN in the moist state for acetic acid was 93.75% and ammonia was 95.23%, respectively. The excellent filtering, antibacterial, and deodorizing performance of Zn/CB@PAN can be attributed to the synergistic effect of breath-induced Zn/CB galvanic couples' electroactivity, released metal ions, and generated reactive oxygen species. The developed Zn/CB@PAN could capture and kill airborne environmental pathogens under humid environments and deodorize odors from prolonged wear, holding promise for broad applications as personal protective masks.

Reviewing methods of deep learning for diagnosing COVID-19, its variants and synergistic medicine combinations

The COVID-19 pandemic has necessitated the development of reliable diagnostic methods for accurately detecting the novel coronavirus and its variants. Deep learning (DL) techniques have shown promising potential as screening tools for COVID-19 detection. In this study, we explore the realistic development of DL-driven COVID-19 detection methods and focus on the fully automatic framework using available resources, which can effectively investigate various coronavirus variants through modalities. We conducted an exploration and comparison of several diagnostic techniques that are widely used and globally validated for the detection of COVID-19. Furthermore, we explore review-based studies that provide detailed information on synergistic medicine combinations for the treatment of COVID-19. We recommend DL methods that effectively reduce time, cost, and complexity, providing valuable guidance for utilizing available synergistic combinations in clinical and research settings. This study also highlights the implication of innovative diagnostic technical and instrumental strategies, exploring public datasets, and investigating synergistic medicines using optimised DL rules. By summarizing these findings, we aim to assist future researchers in their endeavours by providing a comprehensive overview of the implication of DL techniques in COVID-19 detection and treatment. Integrating DL methods with various diagnostic approaches holds great promise in improving the accuracy and efficiency of COVID-19 diagnostics, thus contributing to effective control and management of the ongoing pandemic.