Disruption of the structure of regular sleep is a common cause of neurodegenerative diseases such as Alzheimer′s disease and Parkinson′s disease, and its pathogenesis may be related to the deposition of waste products in the central nervous system. The glymphatic pathway, which is essentially a periarterial cerebrospinal fluid inflow pathway and peripheral venous clearance pathway, is functionally dependent on interstitial bulk flow coupling supported by aquaporin-4 on the astrocyte end-foot, also known as the lymphoid glial system. The glymphatic pathway, which removes waste proteins from the brain, is active primarily during sleep, and sleep quality declines with age, while the glymphatic pathway system also deteriorates with age, suggesting a relationship between sleep disturbances and symptom progression in neurodegeneration, and glymphatic system as a link closely links the two. The interaction of sleep, aging, metabolic waste and glymphatic pathway reticulation provides new clues to the pathogenesis of central nervous system degenerative diseases, and the glymphatic pathway may constitute a new target on treatment. The recent research progress on the effects of sleep and sleep disorders on the circulation of the glymphatic system, and proposes the possibility of sleep intervention to slow down the impairment of the lymphoid system function or even restore the function of the lymphoid system and thus improve the disease development process were reviewed in this paper.

Objective:To explore the effect of endoplasmic reticulum stressed (ER) on colorectal cancer (CRC) cell proliferation and invasion via ATG5-mediated autophagy pathway and the underlying mechanism.Methods:We performed bioinformatics analysis to identify the expression level of PERK, ATF6 and ATG5 in CRC tissues and adjacent tissues and the correlation between PERK and ATG5 expression in CRC tissues.The expression level of PERK in CRC cell lines was examined by qRT-PCR assay. Cell proliferation was quantified by CCK-8.The invasion of the cells was detected by Transwell.Western blot assay was performed to verify the levels of protein. The levels of autophagy were examined by electron microscopy.Results:PERK and ATF6 expression in CRC tissues was higher than that in the adjacent tissues and PERK expression was higher in CRC cells than intestinal mucosal cells. Expression level of PERK in CRC cell lines HCT116,SW480,HT29,LoVo and colonic mucosa cell lines FHC was 1.51±0.04,3.12±0.05,2.19±0.04,2.38±0.06 and 0.98±0.04 ( P<0.001) .The increased expression of PERK promoted CRC cell proliferation and invasion. PERK expression levels was positively associated with ATG5 expression levels ( r=0.52, P<0.001) and overexpression of PERK accelerated the protein expression of ATG5 (1.00±0.04,3.53±0.07, t=74.61, P<0.001) . ATG5 was highly expressed in CRC tissues. Overexpression of ATG5 could promote proliferation,invasion and accelerate autophagy of CRC cells (the number of autophagosomes in the blank control group,the negative control group and ATG5-Overexpression group was 4.33±1.53, 4.00±1.00, 9.67±2.52, and t=3.14,3.62, P=0.035,0.022, respectively) .ATG5 promoted colorectal cancer cell proliferation and invasion through autophagy pathway. Conclusion:ER stressed-CRC cells could promote CRC cell proliferation and invasion through ATG5-mediated autophagy pathway.

Objective:To investigate the expression of Toll-like receptor 4(TLR4)and extracellular signal-regulated protein kinase 1/2(ERK1/2)in decidua tissue of patients with unexplained spontaneous abortion and their correlation.Methods:The expres-sions of TLR4,ERK1/2 and p-ERK1/2 in decidua tissues of 32 patients with unexplained spontaneous abortion(abortion group)and 32 normal pregnancy(control group)were detected by immunohistochemistry and Western blot,respectively.The correlation between TLR4 and p-ERK1/2 in abortion group were analyzed by Pearson hierarchical correlation analysis.Results:In immunohistochemical experiments,the cytoplasm of decidua cells in the two groups is the expression locus of TLR4,ERK1/2 and p-ERK1/2,the expression of the three proteins were different,and the expressions of TLR4 and p-ERK1/2 in abortion group were significantly higher than that in control group(P<0.01).There was no significant difference in ERK1/2 expression between abortion group and control group(P>0.05);In decidua tissue samples of abortion group,the protein level of TLR4 was higher than that of control group(P<0.05);the pro-tein level of p-ERK1/2 was significantly higher than that of control group(P<0.01),and the protein level of ERK1/2 in decidua tissue of abortion group was not statistically different from that of control group(P>0.05).TLR4 was positively correlated with p-ERK1/2 expression in abortion group(r=0.890,P<0.01).Conclusion:Abnormal activation of TLR4/ERK1/2 signaling pathway may be one of the mechanisms of unexplained spontaneous abortion.

OBJECTIVE: To investigate the consistency and diagnostic performance of magnetic resonance imaging (MRI) for detecting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) and the validity of deep learning attention mechanisms and clinical features for MVI grade prediction. METHODS: This retrospective study was conducted among 158 patients with HCC treated in Shunde Hospital Affiliated to Southern Medical University between January, 2017 and February, 2020. The imaging data and clinical data of the patients were collected to establish single sequence deep learning models and fusion models based on the EfficientNetB0 and attention modules. The imaging data included conventional MRI sequences (T₁WI, T₂WI, and DWI), enhanced MRI sequences (AP, PP, EP, and HBP) and synthesized MRI sequences (T1mapping-pre and T1mapping-20 min), and the high-risk areas of MVI were visualized using deep learning visualization techniques. RESULTS: The fusion model based on T1mapping-20min sequence and clinical features outperformed other fusion models with an accuracy of 0.8376, a sensitivity of 0.8378, a specificity of 0.8702, and an AUC of 0.8501 for detecting MVI. The deep fusion models were also capable of displaying the high-risk areas of MVI. CONCLUSION The fusion models based on multiple MRI sequences can effectively detect MVI in patients with HCC, demonstrating the validity of deep learning algorithm that combines attention mechanism and clinical features for MVI grade prediction.
Humans ; Carcinoma, Hepatocellular ; Retrospective Studies ; Liver Neoplasms ; Magnetic Resonance Imaging ; Algorithms