Gallic Acid Attenuates Sepsis-Induced Liver Injury through C/EBPß-Dependent MAPK Signaling Pathway.

SCOPE: Liver injury is a major complication associated with sepsis. Together with others, the study has shown that gallic acid (GA) exerts anti-inflammatory and antioxidant effects in vivo. However, the role of GA in sepsis-mediated hepatic impairment and the underlying mechanisms remains to be elucidated. METHODS AND RESULTS: C57BL/6J mice are pretreated with saline or GA and subjected to sham or cecal ligation and puncture (CLP). The pathological alterations are assessed by hematoxylin and eosin staining as well as immunohistochemical staining. RNA sequencing is employed to analyze hepatic transcriptome modifications. The study finds that GA supplementation significantly ameliorates CLP-induced mortality, liver dysfunction, and inflammation. RNA sequencing reveals that 1324 genes are markedly differentially regulated in livers of saline- or GA-treated sham or CLP mice. Gene ontology analysis demonstrates that the differentially expressed genes regulated by GA are predominantly correlated with the immune system process, oxidation-reduction process, and inflammatory response. Furthermore, mitogen-activated protein kinase (MAPK) signaling is localized in the center of the GA-mediated pathway network. Notably, activation of MAPK by C16-PAF significantly blocks GA-mediated protective effects on hepatic injury, inflammation, as well as CCAAT/enhancer-binding protein-ß (C/EBPß) dependent extracellular signal-regulated kinase 1/2 (ERK1/2) and nuclear factor-κB (NF-κB) signaling. CONCLUSION: Therefore, this study indicates that GA may offer a promising therapeutic opportunity for sepsis-associated liver injury.

Real-Time Cerebral Vessel Segmentation in Laser Speckle Contrast Image Based on Unsupervised Domain Adaptation.

Laser speckle contrast imaging (LSCI) is a full-field, high spatiotemporal resolution and low-cost optical technique for measuring blood flow, which has been successfully used for neurovascular imaging. However, due to the low signal-noise ratio and the relatively small sizes, segmenting the cerebral vessels in LSCI has always been a technical challenge. Recently, deep learning has shown its advantages in vascular segmentation. Nonetheless, ground truth by manual labeling is usually required for training the network, which makes it difficult to implement in practice. In this manuscript, we proposed a deep learning-based method for real-time cerebral vessel segmentation of LSCI without ground truth labels, which could be further integrated into intraoperative blood vessel imaging system. Synthetic LSCI images were obtained with a synthesis network from LSCI images and public labeled dataset of Digital Retinal Images for Vessel Extraction, which were then used to train the segmentation network. Using matching strategies to reduce the size discrepancy between retinal images and laser speckle contrast images, we could further significantly improve image synthesis and segmentation performance. In the testing LSCI images of rodent cerebral vessels, the proposed method resulted in a dice similarity coefficient of over 75%.