Immunohistochemical study of the Landolt\u27s club cells in the chicken retina

The immunohistochemical specificity of the Landolt\u27s club cells of the chicken retina were studied by the indirect immunofluorescence and immunoperoxidase bridge methods with specific antiserum against neuronal filament proteins of these cells. In the tissue, the Landolt\u27s club cells were selectively stained with the specific antiserum (antigen : isoelectric point=6.29, molecular weight=69,000). It appeared that the cells were bipolar and that the cell body lay in the superficial part of the internal nuclear layer. However, it was difficult to observe the dendrites like those of typical bipolar cells which synapse with photoreceptors. No reaction was found in ganglion cells, amacrine cells, horizontal cells and other types of bipolar cells which occupy the zone just inside the external plexiform layer. From the 6th day of tissue culture, the selectively stained bipolar cells appeared on the surface of the monolayer cells. They were small and oval with large, spherical and eccentric nuclei. The cytoplasm was slightly less dense than other neuron cells

The apelin‑apelin receptor signaling pathway in fibroblasts is involved in tumor growth via p53 expression of cancer cells

Cancer-associated fibroblasts (CAFs) are pivotal in tumor progression. TP53-deficiency in cancer cells is associated with robust stromal activation. The apelin-apelin receptor (APJ) system has been implicated in suppressing fibroblast-to-myofibroblast transition in non-neoplastic organ fibrosis. The present study aimed to elucidate the oncogenic role of the apelin-APJ system in tumor fibroblasts. APJ expression and the effect of APJ suppression in fibroblasts were investigated for p53 status in cancer cells using human cell lines (TP53-wild colon cancer, HCT116, and Caco-2; TP53-mutant colon cancer, SW480, and DLD-1; and colon fibroblasts, CCD-18Co), resected human tissue samples of colorectal cancers, and immune-deficient nude mouse xenograft models. The role of exosomes collected by ultracentrifugation were also analyzed as mediators of p53 expression in cancer cells and APJ expression in fibroblasts. APJ expression in fibroblasts co-cultured with p53-suppressed colon cancer cells (HCT116sh p53 cells) was significantly lower than in control colon cancer cells (HCT116sh control cells). APJ-suppressed fibroblasts treated with an antagonist or small interfering RNA showed myofibroblast-like properties, including increased proliferation and migratory abilities, via accelerated phosphorylation of Sma- and Mad-related protein 2/3 (Smad2/3). In addition, xenografts of HCT116 cells with APJ-suppressed fibroblasts showed accelerated tumor growth. By contrast, apelin suppressed the upregulation of phosphorylated Smad2/3 in fibroblasts. MicroRNA 5703 enriched in exosomes derived from HCT116sh p53 cells inhibited APJ expression, and inhibition of miR-5703 diminished APJ suppression in fibroblasts caused by cancer cells. APJ suppression from a specific microRNA in cancer cell-derived exosomes induced CAF-like properties in fibroblasts. Thus, the APJ system in fibroblasts in the tumor microenvironment may be a promising therapeutic target.Saiki H., Hayashi Y., Yoshii S., et al. The apelin‑apelin receptor signaling pathway in fibroblasts is involved in tumor growth via p53 expression of cancer cells. International Journal of Oncology 63, 139 (2023); https://doi.org/10.3892/ijo.2023.5587

A novel artificial intelligence-based endoscopic ultrasonography diagnostic system for diagnosing the invasion depth of early gastric cancer

The version of record of this article, first published in Journal of Gastroenterology, is available online at Publisher’s website: https://doi.org/10.1007/s00535-024-02102-1.Background: We developed an artificial intelligence (AI)-based endoscopic ultrasonography (EUS) system for diagnosing the invasion depth of early gastric cancer (EGC), and we evaluated the performance of this system. Methods: A total of 8280 EUS images from 559 EGC cases were collected from 11 institutions. Within this dataset, 3451 images (285 cases) from one institution were used as a development dataset. The AI model consisted of segmentation and classification steps, followed by the CycleGAN method to bridge differences in EUS images captured by different equipment. AI model performance was evaluated using an internal validation dataset collected from the same institution as the development dataset (1726 images, 135 cases). External validation was conducted using images collected from the other 10 institutions (3103 images, 139 cases). Results: The area under the curve (AUC) of the AI model in the internal validation dataset was 0.870 (95% CI: 0.796–0.944). Regarding diagnostic performance, the accuracy/sensitivity/specificity values of the AI model, experts (n = 6), and nonexperts (n = 8) were 82.2/63.4/90.4%, 81.9/66.3/88.7%, and 68.3/60.9/71.5%, respectively. The AUC of the AI model in the external validation dataset was 0.815 (95% CI: 0.743–0.886). The accuracy/sensitivity/specificity values of the AI model (74.1/73.1/75.0%) and the real-time diagnoses of experts (75.5/79.1/72.2%) in the external validation dataset were comparable. Conclusions: Our AI model demonstrated a diagnostic performance equivalent to that of experts

Lamarckian GA with Genetic Supervision

The evolutionary theory advocated by Lamarck [3], focuses on the inheritance of characteristics acquired for self-adaptation to environment. In the domain of the purpose of acquiring adaptive strategies, it is important to make use of the information of experiences through adaptation. Therefore, the Lamarckian mechanism is an effective approach and is expected to augment the power of many kinds of evolving or learning algorithms. In this paper, we propose the Lamarckian Lookup-Table type Genetic Algorithm (LLT-GA). In general, the effectiveness of the characteristics useful for adaptation depends on a class or rather a landscape of problems to be applied. In order to demolish this barrier, the proposed LLT-GA is armed with a control mechanism for acquired characteristics based on a concept of Genetic Supervision. In this paper we discuss first Lamarckian effect and demonstrate that it is dependent on a landscape of a problem. Then, we develop an adaptive evaluation module for ..

Lamarckian GA with Genetic Supervision

The evolutionary theory advocated by Lamarck [3], focuses on the inheritance of characteristics acquired for self-adaptation to environment. In the domain of the purpose of acquiring adaptive strategies, it is important to make use of the information of experiences through adaptation. Therefore, the Lamarckian mechanism is an effective approach and is expected to augment the power of many kinds of evolving or learning algorithms. In this paper, we propose the Lamarckian Lookup-Table type Genetic Algorithm (LLT-GA). In general, the effectiveness of the characteristics useful for adaptation depends on a class or rather a landscape of problems to be applied. In order to demolish this barrier, the proposed LLT-GA is armed with a control mechanism for acquired characteristics based on a concept of Genetic Supervision. In this paper we discuss first Lamarckian effect and demonstrate that it is dependent on a landscape of a problem. Then, we develop an adaptive evaluation module for Lamarckia..

Artificial Ecological System For Evolving Computational Procedures

: This paper explores a model where computational procedures themselves are evolved for the development of the next generation of emergent computing. The proposed model is an artificial ecosystem consisting of Turing machines which are a mathematical model of computing or algorithm. These Turing machines interact with each other by reading the other machines' descriptions as an input tape. As the simulation proceeds, a series of effective computational procedures emerges from an initial set. This evolutionary process does not require any mutation or static fitness function. This paper demonstrates the self-organizational evolution of these computational procedures through computer simulations. INTRODUCTION In recent years, new methodologies of emergent computing paradigms have been studied in order to enhance our understanding of complex systems and to further the development of emergent design theory for artifacts. In fact, evolutionary computations (Goldberg, 1989 and Koza, 1992) ..