Xuanfei Baidu Formula (XBF) is an effective traditional Chinese medicine (TCM) remedy for treating coronavirus disease 2019 (COVID-19) in China. This herbal medicine has shown effects in reducing clinical symptoms and shortening the average length of hospital stay for COVID-19 patients. Previous studies have demonstrated that XBF alleviates acute lung injury (ALI) by regulating macrophage-mediated immune inflammation, but the mechanisms of action remain elusive. This study aimed to evaluate the lung-protective and anti-inflammatory effects of XBF and its underlying mechanisms. Here, XBF's effects were investigated in an ALI mouse model induced by inhalation of atomized lipopolysaccharide (LPS). Besides, the LPS-induced inflammation model in RAW264.7 cells was used to clarify the underlying mechanisms of XBF against ALI. Our results showed that XBF treatment alleviated LPS-induced lung injury, as evidenced by reduced histopathological changes, pulmonary alveoli permeability, fibrosis, and apoptosis in the lung tissues. In addition, inflammation was alleviated as shown by decreased levels of tumor necrosis factor (TNF)-α, interleukin (IL)-6, IL-1β in serum and bronchoalveolar lavage fluid (BALF), and reduced white blood cell (WBC) count in BALF. Furthermore, consistent with the in vivo assay, XBF inhibited LPS-induced inflammatory cytokines release and pro-inflammatory polarization in RAW264.7 cells. Mechanistically, XBF increased mitochondrial fusion by upregulating Mfn1 and attenuated NLRP3 inflammasome activation by repressing Casp11, respectively, to inhibit NF-κB and MAPK pathways, thus repressing pro-inflammatory macrophage polarization. In this study, we demonstrate that XBF exerts anti-ALI and -inflammatory effects by recovering mitochondrial dynamics and reducing inflammasome activation, providing a biological illustration of the clinical efficacy of XBF in treating COVID-19 patients.

Dietary supplements are currently being used to ameliorate metabolic alterations via maintaining gut microflora balance. Mulberry leaf is known as medicine homologous food for its glucose‐ and lipid‐modulating properties. However, the effects of mulberry leaf polysaccharide (MP) on metabolic dysbiosis and gut microbiota are still poorly understood. After extraction and characterization, the beneficial effects of water‐soluble MP were evaluated in high‐fat diet‐induced obese mice. MP treatment could reduce adipose tissue, improve insulin resistance, and alleviate the pathological lesions in colon. Investigation of the underlying mechanism showed that MP modulated gut microbial community by 16S rRNA analysis and reversed the elevation of lipid indexes by plasma lipidomics analysis. Correlation analysis indicated that the abundance of seven key bacterial species and six lipids were closely associated with the metabolic traits, respectively. Overall, MP could ameliorate metabolic disorders, and modify the gut microbiota and lipids. This would greatly facilitate the utilization of MP as a functional food.

In the clinical application of Traditional Chinese Medicine (TCM) substitutes, the consistency evaluation of TCM substitutes from different sources is recognized as the main bottleneck. As the most widely used analytical method in TCM consistency evaluation, fingerprint similarity evaluation suffers from insufficient method sensitivity and poor conformity with the actual characteristics of TCM, which is difficult to adapt to the analytical needs of complex substance systems of TCM. This work aims to develop an effective and more accurate method for consistency evaluation using omics strategy and machine learning algorithms. The natural calculus bovis (NCB) were graded into three groups according to the similarity to in vitro cultured bovis (IVCB), and chemical markers between samples of each grade were screened out. Support vector machine (SVM) models with different kernels were then constructed by using the chemical markers as feature variables. The results showed that the classification accuracy of the SVM classifier of NCB and the consistency evaluation SVM model classifier was 95.74% and 100.0%, respectively. The approach demonstrated in the study presented a good analytical performance with higher sensitivity, accuracy for consistency evaluation of TCM.