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Fibronectin type III domain-containing protein 1 (FNDC1) is a protein that contains a major component of the structural domain of fibronectin. Although many studies have indicated that FNDC1 serves vital roles in the development of various diseases, the role of FNDC1 in the progression of breast cancer (BC) remains elusive. The aim of the present study was to investigate the biological functions of FNDC1 in BC cells and the associated mechanisms. The expression levels of FNDC1 in BC tissues and normal breast tissues were analyzed using The Cancer Genome Atlas database (TCGA). Kaplan-Meier curves were mined from TCGA to examine the clinical prognostic significance of FNDC1 mRNA in patients with BC. The expression of FNDC1 was knocked down by transfection with shRNA in BC cells. Cell viability, colony formation ability, migration and invasion were assayed following the silencing of FNDC1 in BC cells. The expression of proteins was measured using a western blotting assay. The bioinformatic data indicated that the FNDC1 mRNA expression levels were significantly upregulated in BC tissues compared with normal breast tissues, and the high mRNA expression levels of FNDC1 were associated with a lower overall survival in patients with BC. The downregulation of FNDC1 inhibited the proliferation, colony formation, migration and invasion of BC cells. Investigation of the mechanisms revealed that the silencing of FNDC1 decreased the protein expression levels of MMPs and epithelial-to-mesenchymal markers. Furthermore, the silencing of FNDC1 led to the inactivation of the PI3K/Akt signaling pathway. FNDC1 was highly upregulated and acted as an oncogene in BC. Therefore, targeting FNDC1 may be a potential strategy for the treatment of BC.

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Background Lymph node enlargement is a common clinical finding in clinical practice with different treatment strategies. Purpose To investigate the application of Virtual Touch Image Quantification (VTIQ) to diagnose benign and malignant superficial enlarged lymph nodes. Material and Methods Between December 2015 and August 2016, 116 superficial enlarged lymph nodes were examined by VTIQ. Maximum (V), minimum (V), and average (V) shear wave velocities (SWV) were obtained from the lymph nodes and from normal muscular tissues (V) located at the same level and within 5 mm from the target lymph node. The pathological results were used as the gold standard to evaluate VTIQ. Results All 116 patients underwent fine-needle aspiration biopsy for pathological examination. Forty patients had malignant lymph nodes and 76 patients had benign lymph nodes. Lymph node characteristics on B-mode ultrasound showed no differences between malignant and benign lymph nodes, but there were differences in VTIQ parameters (all P < 0.001). Compared with pathological diagnosis as the gold standard, the area under the ROC curves of V, V, and V were 0.815, 0.746, and 0.795. The V cutoff value to diagnose benign from malignant lymph nodes was 3.045 m/s. The sensitivity, specificity, and positive and negative predictive values were 70%, 78.9%, 63.6%, and 83.3%. Conclusion VTIQ has a clinical application in the differential diagnosis of superficial enlarged lymph nodes.

In order to obtain accurate information on the degree of plaque development in patients' blood vessels, and to assist clinicians in judging and recognizing atherosclerotic areas, a deep learning-based study of intravascular ultrasound atherosclerotic plaque development was performed (CPCA). First, different types of ROIs are extracted for plaque images. Secondly, according to different ROI regions, the size of the sliding neighborhood block is determined, and the central pixel traverses the plaque region to obtain a small image slice of the plaque developing region. Then, based on PCAnet based on principal component analysis vector as convolution kernel, a clustering PCA network is designed to cluster small image slices and calculate principal component vectors by category to generate multiple sets of convolution kernels. The multi-plaque visualization feature enables the input image to adaptively select the feature extractor to achieve classification recognition of the degree of plaque development. The result of manual labeling by doctors is taken as the standard true value. The experimental results show that the proposed algorithm can effectively extract the features of plaque developed images and achieve high-efficiency recognition of plaque development. INDEX TERMS Deep learning, plaque, degree of development, recognition.

Background: We evaluated the clinical outcomes of the stability of carotid and femoral arterial plaques and determined by contrast-enhanced ultrasound (CEUS) for accurate prediction of the artery sclerosis type of ischemic stroke in elderly patients. Methods: We analyzed 24 consecutive patients (mean, 66 ± 19) with carotid and femoral arterial plaques within 48 hours from onset. The carotid and femoral arterial plaques were both observed. The patients were divided into two groups: the carotid plaque (CAP) group and the femoral plaque (FAP) group. Each subject was examined using contrast-enhanced ultrasound (CEUS). Relevant data were recorded, and the microvascular distribution of the plaques was analyzed. Results: Data were analyzed using paired t-test. The maximal plaque thickness and intima-media thickness in the CAP and FAP groups were statistically different (P = 0.027, P < 0.001). Quantitative analysis of CEUS revealed that the enhancement of the time to start and the time to peak intensity of the plaques were statistically significant (P < 0.001), although the differences in mean enhanced intensity of the plaques and the area under the curve obtained by the quantitative analysis of CEUS were not statistically different between the groups (P = 0.078 and P = 0.401, respectively). Wilcoxon rank sum test revealed no statistical difference (P = 0.251) between the carotid and femoral arterial plaque ultrasound contrast rating. Conclusions: The carotid and femoral arterial plaques found in the same patient were stable. The stability of femoral arterial plaques may be used to predict the possible onset of arteriosclerotic ischemic stroke.