A number of histone demethylases have been identified and biochemically characterized, but the pathological roles of their dysfunction in human disease like cancer have not been well understood. Here, we demonstrate important roles of lysine‐specific demethylase 1 (LSD1) in human carcinogenesis. Expression levels of LSD1 are significantly elevated in human bladder carcinomas compared with nonneoplastic bladder tissues (p < 0.0001). cDNA microarray analysis also revealed its transactivation in lung and colorectal carcinomas. LSD1‐specific small interfering RNAs significantly knocked down its expression and resulted in suppression of proliferation of various bladder and lung cancer cell lines. Concordantly, introduction of exogenous LSD1 expression promoted cell cycle progression of human embryonic kidney fibroblast cells. Expression profile analysis showed that LSD1 could affect the expression of genes involved in various chromatin‐modifying pathways such as chromatin remodeling at centromere, centromeric heterochromatin formation and chromatin assembly, indicating its essential roles in carcinogenesis through chromatin modification.

In the rapidly evolving field of medical image analysis utilizing artificial intelligence (AI), the selection of appropriate computational models is critical for accurate diagnosis and patient care. This literature review provides a comprehensive comparison of vision transformers (ViTs) and convolutional neural networks (CNNs), the two leading techniques in the field of deep learning in medical imaging. We conducted a survey systematically. Particular attention was given to the robustness, computational efficiency, scalability, and accuracy of these models in handling complex medical datasets. The review incorporates findings from 36 studies and indicates a collective trend that transformer-based models, particularly ViTs, exhibit significant potential in diverse medical imaging tasks, showcasing superior performance when contrasted with conventional CNN models. Additionally, it is evident that pre-training is important for transformer applications. We expect this work to help researchers and practitioners select the most appropriate model for specific medical image analysis tasks, accounting for the current state of the art and future trends in the field.

Protein arginine methylation is a novel post‐translational modification regulating a diversity of cellular processes, including histone functions, but the roles of protein arginine methyltransferases (PRMTs) in human cancer are not well investigated. To address this issue, we first examined expression levels of genes belonging to the PRMT family and found significantly higher expression of PRMT1 and PRMT6, both of which are Type I PRMTs, in cancer cells of various tissues than in non‐neoplastic cells. Abrogation of the expression of these genes with specific siRNAs significantly suppressed growth of bladder and lung cancer cells. Expression profile analysis using the cells transfected with the siRNAs indicated that PRMT1 and PRMT6 interplay in multiple pathways, supporting regulatory roles in the cell cycle, RNA processing and also DNA replication that are fundamentally important for cancer cell proliferation. Furthermore, we demonstrated that serum asymmetric dimethylarginine (ADMA) levels of a number of cancer cases are significantly higher than those of nontumor control cases. In summary, our results suggest that dysregulation of PRMT1 and PRMT6 can be involved in human carcinogenesis and that these Type I arginine methyltransferases are good therapeutic targets for various types of cancer.

Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the performance of a CNN trained with only clinical images of a pigmented skin lesion in a clinical image classification task, in competition with dermatologists, has not been reported to date. In this study, we extracted 5846 clinical images of pigmented skin lesions from 3551 patients. Pigmented skin lesions included malignant tumors (malignant melanoma and basal cell carcinoma) and benign tumors (nevus, seborrhoeic keratosis, senile lentigo, and hematoma/hemangioma). We created the test dataset by randomly selecting 666 patients out of them and picking one image per patient, and created the training dataset by giving bounding-box annotations to the rest of the images (4732 images, 2885 patients). Subsequently, we trained a faster, region-based CNN (FRCNN) with the training dataset and checked the performance of the model on the test dataset. In addition, ten board-certified dermatologists (BCDs) and ten dermatologic trainees (TRNs) took the same tests, and we compared their diagnostic accuracy with FRCNN. For six-class classification, the accuracy of FRCNN was 86.2%, and that of the BCDs and TRNs was 79.5% (p = 0.0081) and 75.1% (p < 0.00001), respectively. For two-class classification (benign or malignant), the accuracy, sensitivity, and specificity were 91.5%, 83.3%, and 94.5% by FRCNN; 86.6%, 86.3%, and 86.6% by BCD; and 85.3%, 83.5%, and 85.9% by TRN, respectively. False positive rates and positive predictive values were 5.5% and 84.7% by FRCNN, 13.4% and 70.5% by BCD, and 14.1% and 68.5% by TRN, respectively. We compared the classification performance of FRCNN with 20 dermatologists. As a result, the classification accuracy of FRCNN was better than that of the dermatologists. In the future, we plan to implement this system in society and have it used by the general public, in order to improve the prognosis of skin cancer.