Objective To explore the clinical symptom clusters in breast cancer patients with anthracycline treatment, which could provide evidence for prevention. Methods The M.D.Anderson Symptom Inventory of Chinese version (MDASI-C) was applied to assess clinical symptoms in 506 breast cancer patients received anthracycline therapy during their 1stto 4thcycle chemotherapy.Thirteen symptoms were analyzed using main-component analysis and variance orthogonal rotation. The exploratory factor analysis was conducted to find factors value greater than 1. Results The number of symptoms with incidence rate more than 50% was 5, 6, 7 and 9 during the 1stto the 4thcycle, respectively. Fatigue, poor appetite, and nausea were the most common symptoms, and the incidence of these symptoms were 92.5% to 97.1% ,84.8% to 95.1% and 81.1% to 91.3% with the increasing cycle of chemotherapy.Three factors value greater than 1 were detected during the 1stto 2ndcycle chemotherapy by exploratory factor analysis.The cumulative variance contribution rates were 63.233% and 61.434% in the 1stand 2ndcycle, respectively. The main symptom clusters concentrate on fatigue and digestive tract symptoms, including fatigue, sleep disturbance, hypersomnia, nausea, vomit, poor appetite, dry mouth. Two factors value greater than 1 were detected during 3rdto4thcycle in chemotherapy. The cumulative variance contribution rates were 62.660% and 61.148% in the 3rdand 4thcycle, respectively. The main symptom clusters concentrate on psychological and nervous system symptoms including sadness, pain, dry mouth, numbness, hypersomnia, shortness of breath, amnesia and so on. The Cronbach α of cluster symptoms from the 1stto the 4thcycle chemotherapy was between 0.829 to 0.911. Conclusions Symptom clusters vary with the cycles of chemotherapy in breast cancer patients treated with anthracycline. Nurses should provide targeted intervention measures to improve symptom and enhance quality of life, according to specific situation.

0);while the gene frequency of HLA-DQA1 * 0401 allele in children FC was 0.9 %,which was lower than that of the control group(8.5 %,P = 0.0350).Conclusion HLA-DQA1 0101 allele maybe a susceptible gene and HLA-DQA1 * 0401 allele maybe a protective gene of FC in children FC in Han nationality in Baotou.There was no correlation between HLA-DQB1 and FC.

BACKGROUND:Rapid developments in the field of bioinformatics have provided new methods for the diagnosis of osteoarthritis.Artificial neural networks have powerful data computing and classification capabilities,which have shown better performance in disease diagnosis. OBJECTIVE:To establish a new diagnostic predictive model of osteoarthritis based on artificial neural network and to verify the diagnostic value of the model in osteoarthritis with an external dataset. METHODS:The eligible osteoarthritis-related data sets were downloaded through GEO database search and divided into Train group and Test group.The gene expression matrix of the Train group was analyzed to screen the differentially expressed genes.GO and KEGG enrichment analyses were performed on the differentially expressed genes.Through Lasso regression model,support vector machine model and random forest tree model,the key genes of osteoarthritis were further identified from the differentially expressed genes.The R software"Neuralnet"package was then used to construct the osteoarthritis diagnosis model based on artificial neural network,and the model performance was evaluated by the five-fold cross-validation.Two independent data sets in the Test group were used to verify their diagnostic results. RESULTS AND CONCLUSION:A total of 90 differentially expressed genes related to osteoarthritis were obtained by differential analysis,of which 33 were down-regulated and 57 were up-regulated.GO enrichment analysis showed that the differentially expressed genes were mainly involved in the following biological processes,including leukocyte-mediated immunity,leukocyte migration in bone marrow and chemokine production.KEGG enrichment analysis showed that these genes were mainly enriched in rheumatoid arthritis,interleukin-17 signaling pathway and osteoclast differentiation pathway.Five key genes for the diagnosis of osteoarthritis,HMGB2,GADD45A,SLC19A2,TPPP3 and FOLR2,were identified by three machine learning methods.The artificial neural network model of five key genes in the Train group showed that the accuracy was 96.36%and the area under the curve was 0.997.The five-fold cross validation of the neural network model showed that the average area under the curve was greater than 0.9 and the model was of robustness.Two independent data sets in the Test group showed its area under the curve was 0.814 and 0.788 respectively.Therefore,the establishment of an artificial neural network model for the diagnosis of osteoarthritis has a certain diagnostic value.