A novel pixel value space statistics map of the pulmonary nodule for classification in computerized tomography images.

Accurate assessment of pulmonary nodules can help to diagnose the serious degree of lung cancer. In most computed aided diagnosis (CADx) systems, the feature extraction module plays quite an important role in classifying pulmonary nodules based on different attributes of them. To precisely evaluate the malignancy of an unknown pulmonary nodule, this paper first proposes a novel pixel value space statistics map (PVSSM) for pulmonary nodules classification. By means of PVSSM this study can transform an original two-dimensional (2D) or three-dimensional (3D) pulmonary nodule into a 2D feature matrix, which contributes to better classifying a pulmonary nodule. To validate the proposed method, this study assembled 5385 valid 3D nodules from 1006 cases in LIDC-IDRI database. This study extracts sets of features from the created feature matrixes by singular value decomposition (SVD) method. Using several popular classifiers including KNN, random forest and SVM, we acquire the classification accuracies of 77.29%, 80.07% and 84.21%, respectively. Moreover, this study also utilizes the convolutional neural network (CNN) to assess the malignancy of nodules and the sensitivity, specificity and area under the curve (AUC) reach up to 86.0%, 88.5% and 0.913, respectively. Experiments demonstrate that the PVSSM has a benefit for nodules classification.

Content-based retrieval for lung nodule diagnosis using learned distance metric.

Similarity metric of the lung nodules can be useful in differentiating between benign and malignant lung nodule lesions on computed tomography (CT). Unlike previous computerized schemes, which focus on the features extracting, we concentrate on similarity metric of the lung nodules. In this study, we first assemble a lung nodule dataset which is from LIDC-IDRI lung CT images. This dataset includes 746 lung nodules in which 375 domain radiologists identified malignant nodules and 371 domain radiologists-identified benign nodules. Each nodule is represented by a vector of 26 texture features. We then propose a content-based image retrieval (CBIR) scheme to classify between benign and malignant lung nodules with a learned Mahalanobis distance metric. The Mahalanobis distance metric as a similarity metric can preserve semantic relevance and visual similarity of lung nodules. The CBIR approach uses this Mahalanobis distance to search for most similar reference nodules for each queried nodule. The majority of votes are then computed to predict the likelihood of the queried nodule depicting a malignant lesion. For the classification accuracy, the area under the ROC curve (AUC) can achieve as 0.942±0.008. The recall and precision of benign nodules are 0.860 and 0.889, respectively. The recall and precision of malignant nodules are 0.893 and 0.866, respectively.

Identification and characterization of immune-related microRNAs in blunt snout bream, Megalobrama amblycephala.

MicroRNAs (miRNAs) play vital roles in diverse biological processes, including in immune response. Blunt snout bream (Megalobrama amblycephala) is a prevalent and important commercial endemic freshwater fish species in China's intensive polyculture systems. To identify immune-related miRNAs of M. amblycephala, two small RNA (sRNA) libraries from immune tissues with or without lipopolysaccharide (LPS) stimulation were constructed and sequenced using the high-throughput sequencing technology. Totally, 16,425,543 and 15,076,813 raw reads, corresponding to 14,156,755 and 13,445,869 clean reads, were obtained in the normal and infected libraries, respectively. A total of 324 miRNAs, including 218 known miRNAs and 106 putative novel miRNAs were identified by bioinformatic analysis. We analyzed differentially expressed miRNAs between two libraries using pairwise comparison. 113 (34.88%) miRNAs were found to be significantly differentially expressed between two libraries, with 63 (55.75%) exhibiting elevated expression in LPS stimulation sample. Thereinto, a number of known miRNAs were identified immune-related. Real-time quantitative PCR (RT-qPCR) were implemented for 12 miRNAs of two samples, and agreement was confirmed between the sequencing and RT-qPCR data. Target genes likely regulated by these differentially expressed miRNAs were predicted using computational prediction. The functional annotation of target genes by Gene Ontology enrichment (GO) and Kyoto Encyclopedia of Genes and Genomes pathway analysis (KEGG) indicated that a majority of differential miRNAs might involved in immune response. To our knowledge, this is the first comprehensive study of miRNAs in response to LPS stimulation in M. amblycephala, even in fish. These results deepened our understanding of the role of miRNAs in the intricate host's immune system, and should be useful to develop new control strategies for host immune defense against various bacterial invasions in M. amblycephala.

Quantitative evaluation of the degrees of traditional Chinese medicine qualitative syndromes of osteoporosis / 中西医结合学报

To establish a quantitative estimate model for diagnosing traditional Chinese medicine (TCM) syndromes of patients with osteoporosis.

A study of diagnostic criteria for traditional Chinese medicine syndromes in osteoporosis / 中西医结合学报

To establish diagnostic criteria for common traditional Chinese medicine (TCM) syndromes in osteoporosis.