Acupuncture and Tuina are the main non-drug therapies for low back pain, which are recommended by the guidelines. Acupuncture and Tuina can alleviate pain, which is regarded as conditional specific outcome, and improve mental, emotional problems, as non-conditional specific outcomes. There are some problems of the outcome assessment of acupuncture and Tuina treatment for pain such as insufficient evaluation of specific effect and unclear evaluation of characteristic outcome. Therefore, the key to above problems is to construct a Specific ouTcomE Assessment Modal of acupuncture and Tuina treatment for pain (STEAM-A&T) based on the qualitive and quantitative methods. By describing the experience, narrative expression, feelings and needs of patients who receiving acupuncture and Tuina treatment, the item banks of acupuncture and Tuina treatment effect are constructed, and the characteristic outcome of acupuncture and Tuina for pain will be screened, and then the relationship model among outcomes is constructed and optimized, which reflected the characteristics of acupuncture and Tuina for pain from multiple dimensions, multiple levels and multiple views. We reveal the relationship between the outcome of acupuncture and Tuina for pain. It will provide a new theory and methods for the construction of specific outcome assessment modal of Traditional Chinese Medicine.

To investigate the material basis and mechanism of Liupao tea on preventing COVID-19 by network pharmacology and molecular docking.The active ingredients and targets of Liupao tea were searched through the literature and the TCMSP databases and the network between the two was built by Cytoscape 3.7.1.Then using GenCards platform to predict the disease targets,mapping the common targets between Liupao tea and disease.The common targets were imported into the STRING database for exploring the protein-protein interaction.Core targets were enriched by gene ontology (GO) enrichment analysis and KEGG (kyoto encyclopedia of genes and genomes) pathway enrichment analysis using DAVID database etc..Finally,the screened active components were docked with the receptor protein SARS-CoV-2 3CL hydrolase (Mpro).Six active ingredients of Liupao tea were screened,such as (-)-epigallocatechin gallate (EGCG),(+)-catechin,(-)-catechin gallate,α-spinasterol,pelargonidin chloride and squalene,and 156 targets were identified.Among them,there were 112 common targets and 38 core targets with COVID-19.GO enrichment analysis (P<0.01) involved lipopolysaccharide,cell response to hypoxia,etc..And the KEGG pathway enrichment analysis (P<0.01)was conducted to obtain the HIF-1,IL-17,T cell receptor and other signaling pathways associated with COVID-19.The results of molecular docking showed that the active ingredients of Liupao tea were well bound to the receptor protein Mpro.The active ingredients of Liupao tea may control HIF-1,IL-17,T cell receptors signaling pathways by binding Mpro hydrolase and acting on inflammation and immune related targets such as MAPK1,TNF to prevent COVID-19.The EGCG of Mpro activity was determined ,and the IC50 was 3.4 μmol/L,which confirmed that EGCG was a certain inhibition effect on Mpro.

Pediatric Tuina is an important part of traditional Chinese medicine, but the operation technical standard is less developed. It is favored by TCM practitioners in China and abroad, due to its simple operation and clinical effects. The specific acupoint of pediatric tuina is the core of its therapy, but due to the numerous schools of pediatric tuina in China, the consensus of location and operation on some specific acupoints have not been reached. To meet the clinical practice needs of pediatric tuina practitioners and follow the evidence-based principles, we determined to develop International Pediatric Tuina Specific Acupoints and Operation Technical standaeds, considering the diversity of pediatric tuina schools. We hope to lay a foundation for evidence-based practice, trials design and guidelines development of pediatric tuina.

Objective To summary the hospitalization costs of lung cancer patients, and analyze the influence factors in these patients, and provide basis for controlling hospitalization costs of lung cancer patients. Methods The hospitalization costs data of hospitalized lung cancer cases in Wuhan from 2018 to 2020 were collected from medical records. Nonparametric test was used to analysis the data for single factor analysis. The patients were divided into two groups according on the upper quartile value of hospitalization cost, that is high-cost group (the cost ≥ the upper quartile value) and normal cost group (the cost “four major hospitals in Hubei^” respectively. The hospital type is an independent influencing factors, compared with specialized hospital, the OR is 4.726 for general hospital. The hospitalization days is the independent influencing factors, more hospitalization days has high cost. The treatment mode is the independent influencing factors, compared with non-operative treatment, the OR is 556.129, 18.156 and 5.212 for surgical model, radio therapy model and interventional model respectively. Conclusion The age, hospital level, hospital type, hospitalization days and treatment mode are the independent influencing factors of hospitalization costs. To reduce the hospitalization cost of lung cancer patients, we should standardize the diagnosis of lung cancer patients, and focus on standardizing the treatment mode, also considering other influencing factors, such as hospital level, hospital type.

The standardization of pediatric Tuina is beneficial to pediatric Tuina practitioners in a norm practices. The paper collects the content from teaching textbooks, TCM ancient books and database literature, and tries to develop the technical specifications of pediatric Tuina by four rounds Delphi surveys and expert consensus. This specification covers the manipulation of pediatric Tuina, the position of acupoints, the effects of acupoints and the diagnosis and treatment of pediatric Tuina, including indications, contraindications, cautious use, operation steps and methods.