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Objective. To investigate the roles and possible mechanisms of SIRT2 and KLF4 in the development and progression of myeloma. Methods. Rt-PCR was used to detect SIRT2 in myeloma samples from patients and myeloma cells, the expression level of KLF4 in myeloma cells, and the effect of downregulation of SIRT2 expression on KLF4 expression level. MTT assay and wound-healing assay were used to observe the proliferation and migration of U266cells transient transfected with Sirt2 inhibitors. Results. SIRT2 is highly expressed in myeloma, but KLF4 was down. Downregulation of SIRT2 expression stimulated the expression level of KLF4. Reduced SIRT2 activity results in the release of KLF4 expression, which inhibits the proliferation and migration of myeloma cells. Conclusion. SIRT2-KLF4 combination plays an important role in the occurrence and development of myeloma. [ABSTRACT FROM AUTHOR]

Objective. To investigate the role of nestin in regulating Keap1-nuclear factor erythroid-2-related factor 2 (Nrf2)-heme oxygenase-1-(HO-1-) mediated antioxidant responses in stress and malignant hematopoiesis. Methods. The mRNA of peripheral blood mononuclear cells was extracted from 20 leukemia patients and 20 healthy people who were hospitalized in the Hematology Department of our hospital from September 2020 to December 2021, and the mRNA levels of nestin, Keap1, Nrf2, and HO-1 were detected by real-time- (RT-) PCR. Results. Compared with healthy controls, the mRNA of nestin, Keap1, Nrf2, and HO-1 in peripheral blood mononuclear cells of leukemia patients was significantly upregulated. Conclusion. The occurrence and development of leukemia are closely related to nestin regulating Keap1-Nrf2-Ho-1 signal pathway. Research Significance. This study determined the effect of nestin on the biological behavior of leukemia cells and its possible mechanism and confirmed that nestin may be a marker of tumor and tumor blood vessels. [ABSTRACT FROM AUTHOR]

Stroke is one of the fatal diseases worldwide, and its primary mechanism is produced by cerebrovascular stenosis, blockages, or embolisms. Computer-aided diagnosis can assist clinical practitioners in identifying cerebrovascular anomalies, elucidating the precise lesions' location in the patients, and providing guidance for clinical therapy. Due to different portions of the cerebrovascular possessing diverse morphological properties and the limited narrow area, the detection effect is unsatisfactory. A retrained two-stage algorithm for detecting cerebral arterial stenosis in CTA images is proposed to solve these problems by further fusing image features and improving the quality of regions of interest. In Faster R-CNN and Libra R-CNN, the backbone network was Resnet50, with deformable convolutional and nonlocal neural networks introduced in the third, fourth, and fifth stages of the backbone network. Deformable convolutional networks learned offsets to extract morphological features of blood vessels in different tomographic planes. Nonlocal neural networks fused global information and extracted global features from location information of feature maps. A cascade detector refined object classification and bounding box regression before prediction. The experimental results show that the retained algorithm increases mAP by 7.3% and 7.5%, respectively, compared with Faster R-CNN and Libra R-CNN. Deformable convolutional networks, nonlocal neural networks, and cascade detectors are incorporated into further feature fusion; thus, semantic information about the cerebrovascular structure is learned, demonstrating more accurate stenotic region detection and demonstrating generalizability across different two-stage algorithms. [ABSTRACT FROM AUTHOR]

Sugar price forecasting has attracted extensive attention from policymakers due to its significant impact on people's daily lives and markets. In this paper, we present a novel hybrid deep learning model that utilizes the merit of a time series decomposition technology empirical mode decomposition (EMD) and a hyperparameter optimization algorithm Tree of Parzen Estimators (TPEs) for sugar price forecasting. The effectiveness of the proposed model was implemented in a case study with the price of London Sugar Futures. Two experiments are conducted to verify the superiority of the EMD and TPE. Moreover, the specific effects of EMD and TPE are analyzed by the DM test and improvement percentage. Finally, empirical results demonstrate that the proposed hybrid model outperforms other models. [ABSTRACT FROM AUTHOR]