MPC-STANet: Alzheimer’s Disease Recognition Method based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism

Alzheimer\u27s disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.), the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer\u27s disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50

MPC-STANet: Alzheimer’s Disease Recognition Method based on Multiple Phantom Convolution and Spatial Transformation Attention Mechanism

Alzheimer\u27s disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.), the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer\u27s disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50

PCBP-1 regulates alternative splicing of the CD44 gene and inhibits invasion in human hepatoma cell line HepG2 cells

<p>Abstract</p> <p>Background</p> <p>PCBP1 (or alpha CP1 or hnRNP E1), a member of the PCBP family, is widely expressed in many human tissues and involved in regulation of transcription, transportation process, and function of RNA molecules. However, the role of PCBP1 in CD44 variants splicing still remains elusive.</p> <p>Results</p> <p>We found that enforced PCBP1 expression inhibited CD44 variants expression including v3, v5, v6, v8, and v10 in HepG2 cells, and knockdown of endogenous PCBP1 induced these variants splicing. Invasion assay suggested that PCBP1 played a negative role in tumor invasion and re-expression of v6 partly reversed the inhibition effect by PCBP1. A correlation of PCBP1 down-regulation and v6 up-regulation was detected in primary HCC tissues.</p> <p>Conclusions</p> <p>We first characterized PCBP1 as a negative regulator of CD44 variants splicing in HepG2 cells, and loss of PCBP1 in human hepatic tumor contributes to the formation of a metastatic phenotype.</p

Pharmacological Targeting of Bcl-2 Induces Caspase 3-Mediated Cleavage of HDAC6 and Regulates the Autophagy Process in Colorectal Cancer

Compound 6d, a spiroindoline compound, exhibits antiproliferative capability against cancer cell lines. However, the exact underlying mechanism of this compound-mediated inhibitory capability remains unclear. Here, we showed that compound 6d is an inhibitor of Bcl-2, which suppresses CRC growth by inducing caspase 3-mediated intrinsic apoptosis of mitochondria. Regarding the underlying mechanism, we identified HDAC6 as a direct substrate for caspase 3, and caspase 3 activation induced by compound 6d directly cleaves HDAC6 into two fragments. Moreover, the cleavage site was located at D1088 in the DMAD-S motif HDAC6. Apoptosis stimulated by compound 6d promoted autophagy initiation by inhibiting interaction between Bcl-2 and Beclin 1, while it led to the accumulation of ubiquitinated proteins and the reduction of autophagic flux. Collectively, our findings reveal that the Bcl-2-caspase 3-HDAC6 cascade is a crucial regulatory pathway of autophagy and identify compound 6d as a novel lead compound for disrupting the balance between apoptosis and autophagy

Identification of Novel Protein-Protein Interactions of <em>Yersinia pestis</em> Type III Secretion System by Yeast Two Hybrid System

<div><p>Type III secretion system (T3SS) of the plague bacterium <em>Y. pestis</em> encodes a syringe-like structure consisting of more than 20 proteins, which can inject virulence effectors into host cells to modulate the cellular functions. Here in this report, interactions among the possible components in T3SS of <em>Yersinia pestis</em> were identified using yeast mating technique. A total of 57 genes, including all the pCD1-encoded genes except those involved in plasmid replication and partition, pseudogenes, and the putative transposase genes, were subjected to yeast mating analysis. 21 pairs of interaction proteins were identified, among which 9 pairs had been previously reported and 12 novel pairs were identified in this study. Six of them were tested by GST pull down assay, and interaction pairs of YscG-SycD, YscG-TyeA, YscI-YscF, and YopN-YpCD1.09c were successfully validated, suggesting that these interactions might play potential roles in function of <em>Yersinia</em> T3SS. Several potential new interactions among T3SS components could help to understand the assembly and regulation of <em>Yersinia</em> T3SS.</p> </div

Schematic representation of <i>Yersinia</i> type III secretion apparatus and the protein-protein interactions among the different components.

<p>Symbols in dotted line indicate that interactions between these proteins have not been previously reported and were newly identified in this study.</p

Protein-protein interactions of the <i>Y. pestis</i> TTSS measured byY2H.

<p>Protein-protein interactions of the <i>Y. pestis</i> TTSS measured byY2H.</p