1.Review on Progress of Treatment of Chronic Renal Failure by Traditional Chinese Medicine
Liping LIU ; Zhiping WANG ; Meiping XIN
International Journal of Traditional Chinese Medicine 2008;30(2):109-111
Objective This paper reviewed the researching progress on chronic renal failure(CRF)of traditional Chinese medicine in the recent ten years,hoping to provide theoretical evidence for both clinical treatment and scientific study.Methods Literatures on CRF researched by traditional medicine in the recent ten years were summarized and analyzed.Results At present,the studies on CRF by traditional medicine focus on its etiological factors,pathogenesis and treatment according to syndrome differentiation,which revealed the mechanism of its curative effect on different levels.Conclusions Traditional medicine has good curative effect on CRF,and it is worthy of being developed and investigated.
2.A practical guide to amplicon and metagenomic analysis of microbiome data.
Yong-Xin LIU ; Yuan QIN ; Tong CHEN ; Meiping LU ; Xubo QIAN ; Xiaoxuan GUO ; Yang BAI
Protein & Cell 2021;12(5):315-330
Advances in high-throughput sequencing (HTS) have fostered rapid developments in the field of microbiome research, and massive microbiome datasets are now being generated. However, the diversity of software tools and the complexity of analysis pipelines make it difficult to access this field. Here, we systematically summarize the advantages and limitations of microbiome methods. Then, we recommend specific pipelines for amplicon and metagenomic analyses, and describe commonly-used software and databases, to help researchers select the appropriate tools. Furthermore, we introduce statistical and visualization methods suitable for microbiome analysis, including alpha- and beta-diversity, taxonomic composition, difference comparisons, correlation, networks, machine learning, evolution, source tracing, and common visualization styles to help researchers make informed choices. Finally, a step-by-step reproducible analysis guide is introduced. We hope this review will allow researchers to carry out data analysis more effectively and to quickly select the appropriate tools in order to efficiently mine the biological significance behind the data.