Disrupted blood-brain barrier (BBB) integrity contributes to cerebral edema during central nervous system infection. The current study explored the mechanism of lipopolysaccharide- (LPS-) induced dysregulation of tight junction (TJ) proteins. Human cerebral microvascular endothelial cells (hCMEC/D3) were exposed to LPS, SB203580 (p38MAPK inhibitor), or SP600125 (JNK inhibitor), and cell vitality was determined by MTT assay. The proteins expressions of p38MAPK, JNK, and TJs (occludin and zonula occludens- (ZO-) 1) were determined by western blot. The mRNA levels of TJ components and MMP-2 were measured with quantitative real-time polymerase chain reaction (qRT-PCR), and MMP-2 protein levels were determined by enzyme-linked immunosorbent assay (ELISA). LPS, SB203580, and SP600125 under respective concentrations of 10, 7.69, or 0.22 µg/mL had no effects on cell vitality. Treatment with LPS decreased mRNA and protein levels of occludin and ZO-1 and enhanced p38MAPK and JNK phosphorylation and MMP-2 expression. These effects were attenuated by pretreatment with SB203580 or SP600125, but not in ZO-1 expression. Both doxycycline hyclate (a total MMP inhibitor) and SB-3CT (a specific MMP-2 inhibitor) partially attenuated the LPS-induced downregulation of occludin. These data suggest that MMP-2 overexpression and p38MAPK/JNK pathways are involved in the LPS-mediated alterations of occludin in hCMEC/D3; however, ZO-1 levels are not influenced by p38MAPK/JNK.
Background
This paper attempts to conduct a systematic review and meta-analysis of deep learning (DLs) models for cervical cancer CT image segmentation.
Methods
Relevant studies were systematically searched in PubMed, Embase, The Cochrane Library, and Web of science. The literature on DLs for cervical cancer CT image segmentation were included, a meta-analysis was performed on the dice similarity coefficient (DSC) of the segmentation results of the included DLs models. We also did subgroup analyses according to the size of the sample, type of segmentation (i.e., two dimensions and three dimensions), and three organs at risk (i.e., bladder, rectum, and femur). This study was registered in PROSPERO prior to initiation (CRD42022307071).
Results
A total of 1893 articles were retrieved and 14 articles were included in the meta-analysis. The pooled effect of DSC score of clinical target volume (CTV), bladder, rectum, femoral head were 0.86(95%CI 0.84 to 0.87), 0.91(95%CI 0.89 to 0.93), 0.83(95%CI 0.79 to 0.88), and 0.92(95%CI 0.91to 0.94), respectively. For the performance of segmented CTV by two dimensions (2D) and three dimensions (3D) model, the DSC score value for 2D model was 0.87 (95%CI 0.85 to 0.90), while the DSC score for 3D model was 0.85 (95%CI 0.82 to 0.87). As for the effect of the capacity of sample on segmentation performance, no matter whether the sample size is divided into two groups: greater than 100 and less than 100, or greater than 150 and less than 150, the results show no difference (P > 0.05). Four papers reported the time for segmentation from 15 s to 2 min.
Conclusion
DLs have good accuracy in automatic segmentation of CT images of cervical cancer with a less time consuming and have good prospects for future radiotherapy applications, but still need public high-quality databases and large-scale research verification.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.