Nonalcoholic steatohepatitis (NASH), a common clinical disease, is becoming a leading cause of hepatocellular carcinoma (HCC). Dual specificity phosphatase 22 (DUSP22, also known as JKAP or JSP-1) expressed in numerous tissues plays essential biological functions in immune responses and tumor growth. However, the effects of DUSP22 on NASH still remain unknown. Here, we find a significant decrease of DUSP22 expression in human and murine fatty liver, which is mediated by reactive oxygen species (ROS) generation. Hepatic-specific DUSP22 deletion particularly exacerbates lipid deposition, inflammatory response and fibrosis in liver, facilitating NASH and non-alcoholic fatty liver disease (NAFLD)-associated HCC progression. In contrast, transgenic over-expression, lentivirus or adeno-associated virus (AAV)-mediated DUSP22 gene therapy substantially inhibit NASH-related phenotypes and HCC development in mice. We provide mechanistic evidence that DUSP22 directly interacts with focal adhesion kinase (FAK) and restrains its phosphorylation at Tyr397 (Y397) and Y576 + Y577 residues, subsequently prohibiting downstream activation of extracellular signal-regulated kinase 1/2 (ERK1/2) and nuclear factor-κB (NF-κB) cascades. The binding of DUSP22 to FAK and the dephosphorylation of FAK are indispensable for DUSP22-meliorated NASH progression. Collectively, our findings identify DUSP22 as a key suppressor of NASH-HCC, and underscore the DUSP22-FAK axis as a promising therapeutic target for treatment of the disease.

Background: A panel of experts proposed a new definition of metabolic dysfunction-associated fatty liver disease (MAFLD) in 2020. To date, the associations between adipokines, such as adiponectin, adipsin, and visfatin and MAFLD remain unclear. Therefore, we aimed to evaluate the associations between each of these three adipokines and MAFLD using different diagnostic criteria. Methods: In total, 221 participants were included in our study based on medical examination. Detailed questionnaire information, physical examination, abdominal ultrasound, and blood-biochemical-test indexes were collected. The levels of adipokines were tested by using an enzyme immunoassay. Logistic regression models were used to assess the associations of the adipokines with MAFLD. Results: In total, 122 of the participants were diagnosed with MAFLD. Higher levels of adipsin and lower levels of adiponectin were found in the MAFLD group than in the non-MAFLD group (all p < 0.05). According to the logistic regression analysis, the ORs were 0.11 (95% CI: 0.05–0.23) for adiponectin, 4.46 (95% CI: 2.19–9.12) for adipsin, and 0.51 (95% CI: 0.27–0.99) for visfatin when comparing the highest tertile with the lowest tertile (all p-trend < 0.05). The inverse association between adiponectin and MAFLD was strongest when T2DM was used as the diagnostic criterion alone, and the positive association between adipsin and MAFLD was strongest when BMI was used as the diagnostic criterion alone. There was no significant association between visfatin and MAFLD, regardless of whether each of BMI, T2DM, or metabolic dysregulation (MD) was used as the diagnostic criterion for MAFLD alone. Conclusion: Adipsin levels were positively associated with MAFLD and adiponectin levels were inversely associated with MAFLD. The strength of these associations varied according to the different diagnostic criteria for MAFLD.

Abstract. Arctic sea ice type (SITY) variation is a sensitive indicator of climate change. However, systematic inter-comparison and analysis for SITY products are lacking. This study analysed eight daily SITY products from five retrieval approaches covering the winters of 1999–2019, including purely radiometer-based (C3S-SITY), scatterometer-based (KNMI-SITY and IFREMER-SITY) and combined ones (OSISAF-SITY and Zhang-SITY). These SITY products were inter-compared against a weekly sea ice age product (i.e. NSIDC-SIA – National Snow and Ice Data Center sea ice age) and evaluated with five synthetic aperture radar (SAR) images. The average Arctic multiyear ice (MYI) extent difference between the SITY products and NSIDC-SIA varies from -1.32×106 to 0.49×106 km2. Among them, KNMI-SITY and Zhang-SITY in the QuikSCAT (QSCAT) period (2002–2009) agree best with NSIDC-SIA and perform the best, with the smallest bias of -0.001×106 km2 in first-year ice (FYI) extent and -0.02×106 km2 in MYI extent. In the Advanced Scatterometer (ASCAT) period (2007–2019), KNMI-SITY tends to overestimate MYI (especially in early winter), whereas Zhang-SITY and IFREMER-SITY tend to underestimate MYI. C3S-SITY performs well in some early winter cases but exhibits large temporal variabilities like OSISAF-SITY. Factors that could impact performances of the SITY products are analysed and summarized. (1) The Ku-band scatterometer generally performs better than the C-band scatterometer for SITY discrimination, while the latter sometimes identifies FYI more accurately, especially when surface scattering dominates the backscatter signature. (2) A simple combination of scatterometer and radiometer data is not always beneficial without further rules of priority. (3) The representativeness of training data and efficiency of classification are crucial for SITY classification. Spatial and temporal variation in characteristic training datasets should be well accounted for in the SITY method. (4) Post-processing corrections play important roles and should be considered with caution.