The paper analyzes the conditions of research on the current scientific literature text feature extraction methods,applies the TF-IDF method based on background knowledge in the medical text feature extraction,and conducts experimental comparison in four medical fields.The result indicates that this method can obviously improve the extraction effect when there are few vocabularies to be extracted,and is obviously superior to the IDF based TF-IDF method in the aspects of filtration of commonly-used words in the text set and identification of important feature words.

〔Abstract〕 The paper summarizes identification methods for research fronts , including the method based on bibliometrics and the au-tomatic and semi-automatic methods based on computers .Their respective advantages and disadvantages are noted and it is suggested that such tools as semantic network should be utilized to identify research fronts in a deeper and more accurate manner .

Objective To identify the frontiers and hotspots in studies on lung cancer therapy by text mining. Methods PubMed-covered papers on lung cancer therapy were retrieved from 2013 to 2014 . The terms with a broa-der meaning were excluded by mapping the UMLS concept with MetaMap and by limiting their semantic types. A LDA model was established to identify the topics. Results The LDA model could identify the frontiers and hotspots in studies on lung cancer therapy from 2013 to 2014 . Conclusion Frontiers and hotspots in studies on lung cancer therapy can be identified by analyzing the topics and reading the related literature, which can thus provide reference for related medical researchers and managers.

〔Abstract〕 Based on the data of funded papers from SCIE in 2013, the funded issues of high-level medical papers wrote by Chinese authors are discussed by bibliometrics method.The levels and sources of funds from domestic and foreign countries which funding the Chi-nese medical papers are analyzed.The present situation and existing problems of Chinese high-level medical papers funded by domestic and foreign fundations are revealed.

A co-word matrix was constructed with PubMed-covered papers on breast cancer-related enzyme from 2009 to 2011 and from 2012 to 2014 as its data source.The papers were double clustered by gCULTO to generate class groups and identify the breast cancer-related enzyme research frontiers and the evolution of different class groups.

Multidimensional features ( life cycle features, universal acceptability, authority, innovation and inter-disciplinarity) of frontier knowledge and their measurement methods were determined according to the current scien-tific achievements in order to help scientific workers and decision makers timely discover and accurately recognize frontier knowledge.The framework for studies on frontier knowledge discovery in medical field was proposed with its key problems explained in order to provide reference for its future study.

The paper puts forward the method for evaluation on the scientific influence of institution based on PageRank algorithm,configures the initial weight of each institution based on the problems of dividing equally the webpage weight number in the classical PageRank algorithm and cited frequency of the application chapter,constructs the network matrix cited by institution through the citation situation of medical SCI papers published by partial colleges and universities in 2015,respectively calculates the PageRank values of institution when self-citation is excluded or included,and conducts comparative analysis on the sequence of cited frequency indicators of the institution as well as H index.This method can be used to evaluate scientific influence of institution from two dimensions of quality and quantity.

After the factors that influence the selection of words were analyzed according to the problems in selec-tion of words for co-word analysis, a hypothesis for how to select the word sets with its verification analysis was pro-posed, the method for selecting words was thus decided according to the word frequency-covered literature area and the number of high frequency words. However, its practical use in large data sets needs to be further improved.

OBJECTIVE:To study the dermal pharmacokinetic difference of triptolide in normal and diabetic rats,and to pro-vide reference for rational drug use in the clinic. METHODS:12 Wistar rats were randomly divided into normal group and diabetic model group(0.1%streptozotocin intraperitoneally),with 6 rats in each group. Both group were given Triptolide cream 0.5 g to ab-dominal skin,and dialysate was collected by microdialysis every 30 min for consecutive 12 h. Subcutaneous concentration was de-tected by HPLC-MS,and subcutaneous concentration-time curves were analyzed and compared between two groups,and Winnon-lin 5.0.1 software was used to calculate pharmacokinetic parameters. RESULTS:The pharmacokinetic parameters of normal group and diabetic model group were that cmax were(1.54±0.37)and(5.12±1.34)μg/ml;tmax were(7.32±0.24)and(6.21±0.35)h;AUC0-12 h were (12.65 ± 4.64) and (37.43 ± 5.23)μg·h/ml,with statistical significance (P<0.05). CONCLUSIONS:The change of dermal structure caused by diabetes can increase percutaneous penetration amount of triptolide in rats,and drug dosage should be reduced according to circumstances so as to reduce side effects.

Objective To study the cotransfection mB7-1 and mCD1D gene into pancreatic cancer cells of rats and to observe its anti-tmor responses.Methods Recombinant retroviral vectors expressing mB7-1and mCD1D gene were packaged into GP2-293 cell lines and transfected.The expressions of mB7-1 and mCD1D were detected with PCR and Western blot.The positive cells of mB7-1 and mCD1D were used to induce the anti-tumor immunity in vitro.Results Anti-tumor immunity was induced after B7-1 and CD1D positive cells were coinoculated in syngeneic mice.Furthermore,the growth of tumor was inhibited.Conclusions Cotransfection of B7-1 and CD1D could induce anti-tumor effect.This study provide a foundation for the application of B7-1 and CD1D gene therapy in tumor.