Convolutional neural network with parallel convolution scale attention module and ResCBAM for breast histology image classification.

Breast cancer is the most common cause of female morbidity and death worldwide. Compared with other cancers, early detection of breast cancer is more helpful to improve the prognosis of patients. In order to achieve early diagnosis and treatment, clinical treatment requires rapid and accurate diagnosis. Therefore, the development of an automatic detection system for breast cancer suitable for patient imaging is of great significance for assisting clinical treatment. Accurate classification of pathological images plays a key role in computer-aided medical diagnosis and prognosis. However, in the automatic recognition and classification methods of breast cancer pathological images, the scale information, the loss of image information caused by insufficient feature fusion, and the enormous structure of the model may lead to inaccurate or inefficient classification. To minimize the impact, we proposed a lightweight PCSAM-ResCBAM model based on two-stage convolutional neural network. The model included a Parallel Convolution Scale Attention Module network (PCSAM-Net) and a Residual Convolutional Block Attention Module network (ResCBAM-Net). The first-level convolutional network was built through a 4-layer PCSAM module to achieve prediction and classification of patches extracted from images. To optimize the network's ability to represent global features of images, we proposed a tiled feature fusion method to fuse patch features from the same image, and proposed a residual convolutional attention module. Based on the above, the second-level convolutional network was constructed to achieve predictive classification of images. We evaluated the performance of our proposed model on the ICIAR2018 dataset and the BreakHis dataset, respectively. Furthermore, through model ablation studies, we found that scale attention and dilated convolution play an important role in improving model performance. Our proposed model outperforms the existing state-of-the-art models on 200 × and 400 × magnification datasets with a maximum accuracy of 98.74 %.

miR-451a was selectively sorted into exosomes and promoted the progression of esophageal squamous cell carcinoma through CAB39.

Exosomes are emerging mediators of cell-cell communication, which are secreted from cells and may be delivered into recipient cells in cell biological processes. Here, we examined microRNA (miRNA) expression in esophageal squamous cell carcinoma (ESCC) cells. We performed miRNA sequencing in exosomes and cells of KYSE150 and KYSE450 cell lines. Among these differentially expressed miRNAs, 20 of the miRNAs were detected in cells and exosomes. A heat map indicated that the level of miR-451a was higher in exosomes than in ESCC cells. Furthermore, miRNA pull-down assays and combined exosomes proteomic data showed that miR-451a interacts with YWHAE. Over-expression of YWHAE leads to miR-451a accumulation in the exosomes instead of the donor cells. We found that miR-451a was sorted into exosomes. However, the biological function of miR-451a remains unclear in ESCC. Here, Dual-luciferase reporter assay was conducted and it was proved that CAB39 is a target gene of miR-451a. Moreover, CAB39 is related to TGF-ß1 from RNA-sequencing data of 155 paired of ESCC tissues and the matched tissues. Western Blot and qPCR revealed that CAB39 and TGF-ß1 were positively correlated in ESCC. Over-expression of CAB39 were cocultured with PBMCs from the blood from healthy donors. Flow cytometry assays showed that apoptotic cells were significantly reduced after CAB39 over-expression and significantly increased after treated with TGF-ß1 inhibitors. Thus, our data indicate that CAB39 weakens antitumor immunity through TGF-ß1 in ESCC. In summary, YWHAE selectively sorted miR-451a into exosomes and it can weaken antitumor immunity promotes tumor progression through CAB39.

Potential markers of cancer stem-like cells in ESCC: a review of the current knowledge.

In patients with esophageal squamous cell carcinoma (ESCC), the incidence and mortality rate of ESCC in our country are also higher than those in the rest of the world. Despite advances in the treatment department method, patient survival rates have not obviously improved, which often leads to treatment obstruction and cancer repeat. ESCC has special cells called cancer stem-like cells (CSLCs) with self-renewal and differentiation ability, which reflect the development process and prognosis of cancer. In this review, we evaluated CSLCs, which are identified from the expression of cell surface markers in ESCC. By inciting EMTs to participate in tumor migration and invasion, stem cells promote tumor redifferentiation. Some factors can inhibit the migration and invasion of ESCC via the EMT-related pathway. We here summarize the research progress on the surface markers of CSLCs, EMT pathway, and the microenvironment in the process of tumor growth. Thus, these data may be more valuable for clinical applications.