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More than 1200 genes have been shown in the database to be expressed predominantly in the mouse testes. Advances in genome editing technologies such as the CRISPR/Cas9 system have made it possible to create genetically engineered mice more rapidly and efficiently than with conventional methods, which can be utilized to screen genes essential for male fertility by knocking out testis-enriched genes. Finding such genes related to male fertility would not only help us understand the etiology of human infertility but also lead to the development of male contraceptives. In this study, we generated knockout mice for 12 genes (Acrv1 , Adgrf3 , Atp8b5 , Cfap90 , Cfap276 , Fbxw5 , Gm17266 , Lrrd1 , Mroh7 , Nemp1 , Spata45 , and Trim36) that are expressed predominantly in the testis and examined the appearance and histological morphology of testes, sperm motility, and male fertility. Mating tests revealed that none of these genes is essential for male fertility at least individually. Notably, knockout mice for Gm17266 showed smaller testis size than the wild-type but did not exhibit reduced male fertility. Since 12 genes were not individually essential for male fertilization, it is unlikely that these genes could be the cause of infertility or contraceptive targets. It is better to focus on other essential genes because complementary genes to these 12 genes may exist. • Knockout mice of 12 testis-enriched genes were generated with the CRISPR/Cas9 system. • Knockout mice for Gm17266 exhibit smaller testis size than the wild-type. • Twelve testis-enriched genes are individually dispensable for male fertility. [ABSTRACT FROM AUTHOR]

The development of next-generation sequencing (NGS) has enabled the discovery of cancer-specific driver gene alternations, making precision medicine possible. However, accurate genetic testing requires a sufficient amount of tumor cells in the specimen. The evaluation of tumor content ratio (TCR) from hematoxylin and eosin (H&E)-stained images has been found to vary between pathologists, making it an important challenge to obtain an accurate TCR. In this study, three pathologists exhaustively labeled all cells in 41 regions from 41 lung cancer cases as either tumor, non-tumor or indistinguishable, thus establishing a "gold standard" TCR. We then compared the accuracy of the TCR estimated by 13 pathologists based on visual assessment and the TCR calculated by an AI model that we have developed. It is a compact and fast model that follows a fully convolutional neural network architecture and produces cell detection maps which can be efficiently post-processed to obtain tumor and non-tumor cell counts from which TCR is calculated. Its raw cell detection accuracy is 92% while its classification accuracy is 84%. The results show that the error between the gold standard TCR and the AI calculation was significantly smaller than that between the gold standard TCR and the pathologist's visual assessment ( p < 0.05 ). Additionally, the robustness of AI models across institutions is a key issue and we demonstrate that the variation in AI was smaller than that in the average of pathologists when evaluated by institution. These findings suggest that the accuracy of tumor cellularity assessments in clinical workflows is significantly improved by the introduction of robust AI models, leading to more efficient genetic testing and ultimately to better patient outcomes. [ABSTRACT FROM AUTHOR]