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Colorectal cancer is a malignant tumor that mainly occurs in the tissues of the colon and rectum, and its early detection and treatment are of great significance. The early detection and prevention of colorectal cancer mainly involves visual examination of the patient??s intestines to screen for colorectal polyps, but manual examination has the disadvantage of high misdiagnosis rate. The auxiliary diagnostic system based on convolutional neural networks (CNN) has shown the most advanced performance in the diagnosis of colorectal polyps, and is currently a research hotspot in the field of computer-aided diagnosis. Based on important literature published in recent years, a systematic review of the application of convolutional neural networks in the auxiliary diagnosis of colorectal polyps is conducted. Firstly, the commonly used datasets in the field of colorectal polyp diagnosis are introduced, including image and video datasets. Secondly, the application of CNN in colorectal polyp detection, segmentation, and classification is systematically elaborated. The main improvement ideas, advantages and disadvantages, and performance of each algorithm are analyzed in depth, aiming to provide researchers with a more systematic reference, and summarize the interpretability of deep learning models. Finally, a summary of various algorithms for assisting the diagnosis of colorectal polyps based on CNN is provided, and future research directions are prospected.

Breast cancer is the most common malignant tumor in women and its early detection is decisive. Breast imaging plays an important role in early detection of breast cancer as well as monitoring and evaluation during treatment, but manual detection of medical images is usually time-consuming and labor-intensive. Recently, deep learning algorithms have made significant progress in early breast cancer diagnosis. By combing the relevant literature in recent years, a systematic review of the application of deep learning techniques in breast cancer diagnosis with different imaging modalities is conducted, aiming to provide a reference for in-depth research on deep learning-based breast cancer diagnosis. Firstly, four breast cancer imaging modalities, namely mammography, ultrasonography, magnetic resonance imaging and positron emission tomography, are outlined and briefly compared, and the public datasets corresponding to multiple imaging modalities are listed. Focusing on the different tasks (lesion detection, segmentation and classification) of deep learning architectures based on the above four different imaging modalities, a systematic review of the algorithms is conducted, and the performance of each algorithm, improvement ideas, and their advantages and disadvantages are compared and analyzed. Finally, the problems of the existing techniques are analyzed and the future development direction is prospected with respect to the limitations of the current work.

Objective To explore the molecular mechanism of amifostine in relieving cisplatin nephrotoxicity. Methods CCK-8 assay was used to detect the inhibitory effect of amifostine on cisplatin cytotoxicity in HK-2 and primary renal proximal tubular epithelial cells. The expression of PKM2 was analyzed by online database tabula muris. The proportion of apoptotic cells was detected by flow cytometry. The expression of PKM2 gene was detected by fluorescence quantitative PCR and Western blotting. PKM2 gene expression was knocked down by small RNA interference. Results Amifostine significantly enhanced the resistance of renal proximal tubular epithelial cells to cisplatin toxicity. In cisplatin treated cells, with the increase of the concentration of amifostine, the cell viability was increased from 0.37 to 0.45 to 0.77 (P < 0.001). In addition, with the increase of amifostine concentration, PKM2 gene expression in HK-2 cells was increased in turn. The expression of PKM2 gene was increased to 8.76 times higher at 1 μmol/L of amifostine (P < 0.001). PKM2 gene expression in HK-2 cells was increased with the increase of amifostine concentration. When PKM2 gene expression was depleted in HK-2 cells, and the protective effect of amifostine on cisplatin cytotoxicity was significantly inhibited. The proportion of apoptotic cells was reduced and the levels of apoptotic markers Cleaved-PARP and Cleaved-Caspase3 induced by cisplatin were significantly inhibited by amifostine. Single-cell data analysis indicated that the expression of PKM2 in renal proximal tubular epithelial cells was the lowest compared with other cell subsets in renal tissue. Conclusion Amifostine inhibits cell apoptosis induced by cisplatin by up-regulating PKM2 in renal proximal tubular epithelial cells, and thus alleviates the cytotoxicity of cisplatin.

RAB42 is a member of the RAS family. However, the roles and driving forces for RAB42 in tumors remain elusive. In this study, we performed a comprehensive pan-cancer analysis of the roles and regulatory mechanisms of RAB42 using bioinformatics and experiments. Online databases such as Sanger Box, ACLBI and TIDE were used to search for the expression levels, prognostic value and immune features of RAB42. We observed that RAB42 expression was upregulated in most tumors and was closely associated with poor prognosis. Enrichment analysis indicated that RAB42 was related to multiple biological functions, especially the immune process. RAB42 expression had a positive correlation with immune cell infiltration and immune checkpoint gene expression. RAB42 had a high predictive value for immunotherapy efficiency. Our study screened out susceptible drugs for the RAB42 protein by sensitivity analysis and virtual screening. Many key driver genes such as TP53 contributed to RAB42 expression. DNA methylation, super-enhancer and non-coding RNAs were the epigenetic factors responsible for RAB42 expression. In brief, RAB42 could serve as a diagnostic and prognostic biomarker in many tumor types. RAB42 might be a predictive biomarker and a new target for immunotherapy. Genetic and epigenetic factors were essential for RAB42 overexpression in tumors. [ABSTRACT FROM AUTHOR]