Nickel-Ion Activating Discarded COVID-19 Medical Surgical Masks for Forming Carbon-Nickel Composite Nanowires and Using as a High-Performance Lithium Battery Anode

With the prevalence of COVID-19, wearing medical surgical masks has become a requisite measure to protect against the invasion of the virus. Therefore, a huge amount of discarded medical surgical masks will be produced, which will become a potential hazard to pollute the environment and endanger the health of organisms without our awareness. Herein, a green and cost-effective way for the reasonable disposal of waste masks becomes necessary. In this work, we realized the transformation from waste medical surgical masks into high-quality carbon-nickel composite nanowires, which not only benefit the protection of the environment and ecosystem but also contribute to the realization of economic value. The obtained composite carbon-based materials demonstrate 70 S m-1 conductivity, 5.2 nm average pore diameters, 234 m2 g-1 surface areas, and proper graphitization degree. As an anode material for lithium-ion batteries, the prepared carbon composite materials demonstrate a specific capacity of 420 mA h g-1 after 800 cycles at a current density of 0.2 A g-1. It also displays good rate performance and decent cycling stability. Therefore, this study provides an approach to converting the discarded medical surgical masks into high-quality carbon nanowire anode materials to turn waste into treasure.

Traditional Chinese medicines against COVID-19: A global overview

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2, continues to be a global concern. Traditional Chinese medicines (TCMs) are an important element of the fight against COVID-19 in China. The combined application of TCMs and conventional medicines in the treatment of COVID-19 has achieved beneficial results, including the resolution of symptoms, prevention of disease progression, and reduced mortality. In this review, we summarize and discuss the current applications of TCMs with respect to COVID-19, as well as update the preclinical and clinical research, including chemical analysis, molecular mechanisms, quality control, drug development, and studies of clinical efficacy. The expectation is that a better understanding of the roles of TCMs against COVID-19 will improve the response to COVID-19, both in China and globally. © 2022 World Journal of Traditonal Chinese Medicine Published by Wolters Kluwer - Medknow.

Direct visualization of single-cell non-repetitive genes by in situ activation of collateral activity of CRISPR/Cas12a inside cells

The spread of drug-resistance bacteria is a serious issue of environment. Tools allowing to image single-cell genes can provide key information about the spatial pattern and heterogeneity of cell population. Herein, we explored the possibility of in situ activation of collateral trans-cleavage activity of CRISPR/Cas12a inside cells, to achieve a direct detection of single-cell non-repetitive genes. CRISPR/Cas12a allows to recognize target genes without the need for denaturation or digestion process. Particularly, the target gene-activated trans-cleavage by CRISPR/ Cas12a inside cells outputs an amplified signal for the gene recognition, allowing to visualize non-repetitive genes. The signal-to-background ratio for imaging drug-resistance gene, oqxB in the Salmonella enterica subsp. enterica serovar Typhimurium (S. Typhimurium) was further improved by combining multiple binding of Cas12a, enabled imaging of drug-resistance S. Typhimurium isolated from poultry farm and in the intestinal tract sec-tions. Single-cell investigation of S. Typhimurium under salt stress indicated that drug-sensitive strain owned a survival advantage over drug-resistance strain at high-content salt environment. This gene imaging methods holds potential for detecting the spread of drug resistance in the environment and serves as a means to inves-tigate the relationship between genotype and phenotype at single-cell level.

Exploration of bioactive constituents and immunoregulatory mechanisms of a hanshi-yufei formulation for treating COVID-19

Objective: The objective of this study was to characterize the chemical compounds of a Hanshi-Yufei formulation (HSYF;a modified formulation of a traditional Chinese medicine used for treating COVID-19) to elucidate the mechanism of action and to evaluate potential anti-inflammatory effects of HSYF. Materials and Methods: The chemical constituents of HSYF extract were characterized using UPLC-Q-TOF/MS. Subsequently, a set of TCM network pharmacology methods was applied to identify disease-associated genes and to predict target profiles and pharmacological actions associated with the constituents of HSYF. Then, the antiviral effects of HSYF on H1N1 were assessed in RAW264.7 cells using MTT assays. Expression levels of pro-inflammatory cytokines interleukin (IL)-6 and tumor necrosis factor (TNF)-α following infection of RAW264.7 cells with H1N1 were measured using an enzyme-linked immune sorbent assay (ELISA), and expression levels of inflammatory-related factors were detected using western blotting. Results: In total, 165 chemical constituents (including glycosides, tannins, volatile oils, amino acids, triterpenoids, polyphenols, phenylpropanoids, sesquiterpenes, alkaloids, and flavonoids, among others) were tentatively identified in HSYF. Network pharmacology demonstrated that HSYF can regulate immunomodulatory- and anti-inflammatory-related targets of multiple pathways through its active ingredients, suggesting potential anti-COVID-19 effects. Furthermore, cell viability assays and ELISA showed that HSYF significantly inhibited H1N1 replication in RAW64.7 cells and markedly reduced expression of pro-inflammatory cytokines TNF-α and IL-6 at the proteins level. Conclusions: The results of the present study help improve our understanding of the therapeutic effects of HSYF in COVID-19 treatment from multi-level perspectives.

Detection and Classification of Pneumonia from Lung Ultrasound Images

The lungs are the primary organs of the respiratory system in humans. Meanwhile, lungs are also vulnerable and are easily damaged by inflammation or impact lesions during the course of our daily lives. Due to the epidemic of COVID-19 pneumonia, the confirmed and suspected cases often grow rapidly beyond the capabilities of medical institutions, rapid and accurate diagnosis for patients have become the first priority. Hence, ultrasound images have started to be adopted in lung diagnosis as they are more convenient, flexible, cheaper, and without ionizing radiation as compared with CT and CXR. This paper aims to use VGG, ResNet and EfficientNet networks to accurately classify Lung Ultrasound images of pneumonia according to different clinical stages based on self-made LUS datasets. The hyperparameters of the three networks were tuned and their performances were carefully compared. Our results indicate that the EfficientNet model outperformed the others, providing the best classification accuracies for 3 and 4 clinical stages of pneumonia are 94.62% and 91.18%, respectively. The best classification accuracy of 8 imagological features of pneumonia is 82.75%. This result is a proof of the promising potential of the LUS device to be used in pneumonia diagnosis and prove the viability of deep learning for LUS classification of pneumonia.