ATG7, one of the core proteins of autophagy, plays an important role in various biological processes, including the regulation of autophagy. While clear that autophagy drives angiogenesis, the role ...of ATG7 in angiogenesis remains less defined. Several studies have linked ATG7 with angiogenesis, which has long been underappreciated. The knockdown of ATG7 gene in cerebrovascular development leads to angiogenesis defects. In addition, specific knockout of ATG7 in endothelial cells results in abnormal development of neovascularization. Notably, the autophagy pathway is not necessary for ATG7 regulation of angiogenesis, while the ATG7-dependent non-autophagic pathway plays a critical role in the regulation of neovascularization. In order to gain a better understanding of the non-autophagic pathway-mediated biological functions of the autophagy-associated protein ATG7 and to bring attention to this expanding but understudied research area, this article reviews recent developments in the ATG7-dependent non-autophagic pathways regulating angiogenesis.
Breast cancer is a common malignancy with the highest mortality rate among women worldwide. Its incidence is on the rise year after year, accounting for more than one-tenth of new cancers worldwide. ...Increasing evidence suggests that forkhead box (FOX) transcription factors play an important role in the occurrence and development of breast cancer. However, little is known about the relationship between the expression, prognostic value, function, and immune infiltration of FOX transcription factors in tumor microenvironment. We used bioinformatics to investigate expression and function of FOX factor in breast cancer. Our results revealed the expression levels of FOXA1 and FOXM1 were significantly higher in breast cancer tissues than in normal tissues. The high expression of mRNA in FOXA1 (P < .05), FOXM1 (P < .01), and FOXP1 (P < .05) groups was related to tumor stage. Survival analysis results showed that increased FOXP1 mRNA levels were significantly associated with overall survival (OS), recurrence-free survival (RFS), and distant metastasis-free survival (DMFS) in all patients with breast cancer (P < .05). Patients with the FOXA1 high-expression group had better RFS and DMFS than the low-expression group (P < .05), while patients with FOXM1 high-expression group had worse RFS, OS, and DMFS than the low-expression group (P < .05). Meanwhile, mutation analysis showed that genetic alterations in FOX transcription factors were significantly associated with shorter OS and progression-free survival (P < .05), but not with disease-free survival (P = .710) in patients with breast cancer. FOXP1, FOXA1, and FOXM1 may be used as potential biomarkers to predict the prognosis of patients with breast cancer. Functional enrichment indicated that FOX was mainly involved in cell division, cell senescence, cell cycle, and prolactin signaling pathway. In patients with breast cancer, FOXC2 expression was negatively correlated with the infiltration of B cells and positively correlated with the infiltration of neutrophils and dendritic cells. However, FOXM1 was negatively correlated with the infiltration of CD8 + T cells and macrophages and positively correlated with the infiltration of neutrophils and dendritic cells. These findings provided novel insights into the screening of prognostic biomarkers of the FOX family in breast cancer and laid a foundation for further research on the immune infiltration of the FOX transcription factor family members in tumors.
Diabetes is a group of chronic diseases with blood glucose imbalance, and long-term hyperglycaemia causes sustained damage to various organs of the body, resulting in vascular lesions, neuropathy and ...impaired wound healing. Diabetic wound formation involves a variety of complex mechanisms, and they are characterized by a persistent chronic inflammatory response, degradation of angiogenesis and imbalance of extracellular matrix regulation, all of which are related to oxidative stress. Additionally, repair and healing of diabetic wounds require the participation of a variety of cells, cytokines, genes, and other factors, which together constitute a complex biological regulatory network. Recent studies have shown that long noncoding RNAs (lncRNAs) can be involved in the regulation of several key biological pathways and cellular functions demonstrating their critical role in diabetic wound healing. LncRNAs are a major family of RNAs with limited or no protein-coding function. Numerous studies have recently reported a strong link between oxidative stress and lncRNAs. Given that both lncRNAs and oxidative stress have been identified as potential drivers of diabetic wound healing, their link in diabetic wound healing can be inferred. However, the specific mechanism of oxidative stress related to lncRNAs in diabetic wound healing is still unclear, and elucidating the functions of lncRNAs in these processes remains a major challenge. This article reviews the mechanisms of lncRNAs related to oxidative stress in several stages of diabetic wound healing and discusses diagnostic and treatment potential of lncRNAs to treat diabetic wounds by improving oxidative stress, as well as the challenges of using lncRNAs for this purpose. It is hoped that these results will provide new targets and strategies for the diagnosis and treatment of impaired wound healing in diabetic patients.
This paper focuses on accelerating long short-term memory (LSTM), which is one of the popular types of recurrent neural networks (RNNs). Because of the large number of weight memory accesses and high ...computation complexity with the cascade-dependent structure, it is a big challenge to efficiently implement the LSTM on field-programmable gate arrays (FPGAs). To speed up the inference on FPGA, considering its limited resource, a structured pruning method that can not only reduce the LSTM model's size without loss of prediction accuracy but also eliminate the imbalance computation and irregular memory accesses is proposed. Besides that, the hardware architecture of the compressed LSTM is designed to pursue high performance. As a result, the implementation of an LSTM language module on Stratix V GXA7 FPGA can achieve 85.2 GOPS directly on the sparse LSTM network by our method, corresponding to 681.6-GOPS effective throughput on the dense one, which shows that the proposed structured pruning algorithm makes 7.82 times speedup when only 1/8 parameters are reserved. We hope that our method can give an efficient way to accelerate the LSTM and similar recurrent neural networks when the resource-limited environment is emphasized.
PDF