Photodynamic therapy (PDT) is a promising new method to eliminate microbial infection and promote wound healing. Its effectiveness has been confirmed by some studies; however, the mechanisms of PDT in wound healing remain obscure. We used mouse skin wounds infected with Pseudomonas aeruginosa as a research object to explore the therapeutic effects and mechanisms of 5-aminolevulinic acid photodynamic therapy (ALA-PDT). ALA-PDT treatment significantly reduced the load of P. aeruginosa in the wound and surrounding tissues and promoted the healing of skin wounds in mice. Hematoxylin-eosin (HE) and Sirius red staining showed that ALA-PDT promoted granulation tissue formation, angiogenesis, and collagen regeneration and remodeling. After ALA-PDT treatment, the expression of inflammatory factors (TNF-α and IL-1β) first increased and then decreased, while the secretion of growth factors (TGF-β-1 and VEGF) increased gradually after treatment. Furthermore, ALA-PDT affected the polarization state of macrophages, activating and promoting macrophages from an M1 to an M2 phenotype. In conclusion, ALA-PDT can not only kill bacteria but also promote wound healing by regulating inflammatory factors, collagen remodeling and macrophages. This study further clarifies the mechanism of PDT in the healing of infectious skin wounds and provides further experimental evidence for its clinical treatment of skin wounds infected by P. aeruginosa.

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Abstract. Polysaccharides isolated from Scutellaria barbata (PSB) have been reported to have anti-tumor effects. To investigate the underlying mechanism, a highly invasive, metastatic and phospho-c-Met overexpression lung carcinoma cell, 95-D cell line was used. The results showed that in vitro, PSB not only could inhibit the proliferation of 95-D cell line (IC 50 = 35.2 mg/mL), but also down-regulated the expression of phospho-c-Met and its downstream signaling molecules including phospho-Erk and phospho-Akt. In vivo, PSB inhibited tumor growth in the 95-D subcutaneous xenograft model in a dose-dependent manner; after once-daily intraperitoneal injection for 3 weeks, tumor growth inhibition T/C ratio for 100 and 200 mg/kg treatments was 42.72% and 13.6%, respectively. In the end of the in vivo study, tumor tissues were harvested for further evaluation of the phosphorylation level of c-Met, AKT, and ERK. Ex vivo results demonstrated that the phosphorylation of c-Met and its downstream signaling molecules were also significantly inhibited by PSB. Immunohistochemistry analysis showed dose-dependent inhibition of tumor cell proliferation (Ki67) and reduction of microvessel density (CD31). In summary, the results indicated that PSB exerted anti-tumor growth activity on human lung cancer 95-D in vitro and in vivo by directly regulating the c-Met signaling pathway and the anti-tumor effects were mainly based on its anti-proliferation and anti-angiogenesis action.

Background The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. Methods A total of 2169 adult COVID-19 patients were enrolled from Wuhan, China, from February 10th to April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups, as well as between survivors and non-survivors. In addition, we developed a decision tree model to predict death outcome in severe patients. Results Of the 2169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed as severe illness, and 75 patients died. An older median age and a higher proportion of male patients were found in severe group or non-survivors compared to their counterparts. Significant differences in clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A decision tree, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in training and test datasets. The accuracy of this model were 0.98 in both datasets. Conclusion We performed a comprehensive analysis of COVID-19 patients from the outbreak in Wuhan, China, and proposed a simple and clinically operable decision tree to help clinicians rapidly identify COVID-19 patients at high risk of death, to whom priority treatment and intensive care should be given.