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Objective: The aim of this study was to reveal the exact changes during the occurrence of breast cancer to explore significant new and promising genes or factors related to this disease. Methods: We compared the gene expression profiles of breast cancer tissues with its uninvolved normal breast tissues as controls using the cDNA microarray analysis in seven breast cancer patients. Further, one representative gene, named IFI30, was quantitatively analyzed by real-time PCR to confirm the result of the cDNA microarray analysis. Results: A total of 427 genes were identified with significantly differential expression, 221 genes were up-regulated and 206 genes were down-regulated. And the result of cDNA microarray analysis was validated by detection of IFI30 mRNA level changes by real-time PCR. Genes for cell proliferation, cell cycle, cell division, mitosis, apoptosis, and immune response were enriched in the up-regulated genes, while genes for cell adhesion, proteolysis, and transport were significantly enriched in the down-regulated genes in breast cancer tissues compared with normal breast tissues by a gene ontology analysis. Conclusion: Our present study revealed a range of differentially expressed genes between breast cancer tissues and normal breast tissues, and provide candidate genes for further study focusing on the pathogenesis and new biomarkers for breast cancer. Keywords: Breast neoplasms, Candidate genes, Microarray

Abstract Background Magnetic resonance imaging (MRI) is the gold standard for evaluating body composition. However, the reference ranges have not been established. Methods Three lean tissue and seven adipose tissue parameters based on MRI data from the UK Biobank were used in this study. Participants with European ancestry and data on at least one parameter were screened. Age‐ and sex‐specific percentile curves were generated using the lambda–mu–sigma method. Three levels of reference ranges were provided, which were equivalent to the mean ± 1 standard deviation (SD), 2 SDs and 2.5 SDs. Results The final analysis set for each parameter ranged from 4842 to 14 148 participants (53.4%–56.6% women) with a median age of 61. For lean tissue parameters, compared with those at age 45, the median total lean tissue volume and total thigh fat‐free muscle volume at age 70 were 2.83 and 1.73 L, and 3.02 and 1.51 L lower in men and women, respectively. The median weight‐to‐muscle ratios at age 45 were 0.51 and 0.83 kg/L lower compared with those at age 70 in men and women, respectively. Adipose tissue parameters showed inconsistent differences. In men, the median muscle fat infiltration, visceral adipose tissue (VAT) volume, total abdominal adipose tissue index and abdominal fat ratio were 1.48%, 0.32 L, 0.08 L/m2 and 0.4 higher, and the median abdominal subcutaneous adipose tissue (ASAT) volume and total adipose tissue volume were 0.47 and 0.41 L lower, respectively, at age 70 than at age 45. The median total trunk fat volume was approximately 9.53 L at all ages. In women, the median muscle fat infiltration and VAT volume were 1.68% and 0.76 L higher, respectively, at age 70 than at age 45. The median ASAT volume, total adipose tissue volume, total trunk fat volume, total abdominal adipose tissue index and abdominal fat ratio were 0.35 L, 0.78 L, 1.12 L, 0.49 L/m2 and 0.06 higher, respectively, at age 60 than at age 45. The medians of the former three parameters were 0.33 L, 0.14 L and 0.20 L lower, at age 70 than at age 60. The medians of the latter two parameters were approximately 3.64 L/m2 and 0.55 at ages between 60 and 70. Conclusions We have established reference ranges for MRI‐measured body composition parameters in a large community‐dwelling population. These findings provide a more accurate assessment of abnormal adipose and muscle conditions.

Entropy images, representing the complexity of original fundus photographs, may strengthen the contrast between diabetic retinopathy (DR) lesions and unaffected areas. The aim of this study is to compare the detection performance for severe DR between original fundus photographs and entropy images by deep learning. A sample of 21,123 interpretable fundus photographs obtained from a publicly available data set was expanded to 33,000 images by rotating and flipping. All photographs were transformed into entropy images using block size 9 and downsized to a standard resolution of 100 × 100 pixels. The stages of DR are classified into 5 grades based on the International Clinical Diabetic Retinopathy Disease Severity Scale: Grade 0 (no DR), Grade 1 (mild nonproliferative DR), Grade 2 (moderate nonproliferative DR), Grade 3 (severe nonproliferative DR), and Grade 4 (proliferative DR). Of these 33,000 photographs, 30,000 images were randomly selected as the training set, and the remaining 3,000 images were used as the testing set. Both the original fundus photographs and the entropy images were used as the inputs of convolutional neural network (CNN), and the results of detecting referable DR (Grades 2–4) as the outputs from the two data sets were compared. The detection accuracy, sensitivity, and specificity of using the original fundus photographs data set were 81.80%, 68.36%, 89.87%, respectively, for the entropy images data set, and the figures significantly increased to 86.10%, 73.24%, and 93.81%, respectively (all p values