Renal Tissue-Derived Exosomal miRNA-34a in Diabetic Nephropathy Induces Renal Tubular Cell Fibrosis by Promoting the Polarization of M1 Macrophages.

Background: Diabetic nephropathy (DN) is the leading cause of chronic kidney disease, and the activation and infiltration of phagocytes are critical steps of DN. This study aimed to explore the mechanism of exosomes in macrophages and diabetes nephropathy and the role of miRNA-34a, which might provide a new path for treating DN. Materials and Methods: The DN model was established, and the success of the model establishment was confirmed by detecting general indicators, HE staining, and immunohistochemistry. Electron microscopy and NanoSight Tracking Analysis (NTA) were used to see the morphology and size of exosomes. MiRNA-34a inhibitor, miRNA-34a mimics, pc-PPARGC1A, and controls were transfected in macrophages with or without kidney exosomal. A dual-luciferase reporter gene experiment verifies the targeting relationship between miRNA-34a and PPARGC1A. After exosomal culture, macrophages are co-cultured with normal renal tubular cells to detect renal tubular cell fibrosis. Q-PCR and western blot were undertaken to detect related RNA and proteins. Results: An animal model of diabetic nephropathy was successfully constructed. Macrophages could phagocytose exosomes. After ingesting model exosomes, M1 macrophages were activated, while M2 macrophages were weakened, indicating the model mice's kidney exosomes caused the polarization. MiRNA-34a inhibitor increased PPARGC1A expression. MiRNA-34a expressed higher in diabetic nephropathy Model-Exo. MiRNA-34a negatively regulated PPARGC1A. PPARGC1A rescued macrophage polarization and renal tubular cell fibrosis. Conclusion: Exosomal miRNA-34a of tubular epithelial cells promoted M1 macrophage activation in diabetic nephropathy via negatively regulating PPARGC1A expression, which may provide a new direction for further exploration of DN treatment.

Using deep learning-based artificial intelligence electronic images in improving middle school teachers' literacy.

With the rapid development of societal information, electronic educational resources have become an indispensable component of modern education. In response to the increasingly formidable challenges faced by secondary school teachers, this study endeavors to analyze and explore the application of artificial intelligence (AI) methods to enhance their cognitive literacy. Initially, this discourse delves into the application of AI-generated electronic images in the training and instruction of middle school educators, subjecting it to thorough analysis. Emphasis is placed on elucidating the pivotal role played by AI electronic images in elevating the proficiency of middle school teachers. Subsequently, an integrated intelligent device serves as the foundation for establishing a model that applies intelligent classification and algorithms based on the Structure of the Observed Learning Outcome (SOLO). This model is designed to assess the cognitive literacy and teaching efficacy of middle school educators, and its performance is juxtaposed with classification algorithms such as support vector machine (SVM) and decision trees. The findings reveal that, following 600 iterations of the model, the SVM algorithm achieves a 77% accuracy rate in recognizing teacher literacy, whereas the SOLO algorithm attains 80%. Concurrently, the spatial complexities of the SVM-based and SOLO-based intelligent literacy improvement models are determined to be 45 and 22, respectively. Notably, it is discerned that, with escalating iterations, the SOLO algorithm exhibits higher accuracy and reduced spatial complexity in evaluating teachers' pedagogical literacy. Consequently, the utilization of AI methodologies proves highly efficacious in advancing electronic imaging technology and enhancing the efficacy of image recognition in educational instruction.

The use of deep learning integrating image recognition in language analysis technology in secondary school education.

This work aims to investigate the application of advanced deep learning algorithms and image recognition technologies to enhance language analysis tools in secondary education, with the goal of providing educators with more effective resources and support. Based on artificial intelligence, this work integrates data mining techniques related to deep learning to analyze and study language behavior in secondary school education. Initially, a framework for analyzing language behavior in secondary school education is constructed. This involves evaluating the current state of language behavior, establishing a framework based on evaluation comments, and defining indicators for analyzing language behavior in online secondary school education. Subsequently, data mining technology and image and character recognition technology are employed to conduct data mining for online courses in secondary schools, encompassing the processing of teaching video images and character recognition. Finally, an experiment is designed to validate the proposed framework for analyzing language behavior in secondary school education. The results indicate specific differences among the grouped evaluation scores for each analysis indicator. The significance p values for the online classroom discourse's speaking rate, speech intelligibility, average sentence length, and content similarity are -0.56, -0.71, -0.71, and -0.74, respectively. The aim is to identify the most effective teaching behaviors for learners and enhance the support for online course instruction.