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Abstract Background Liver cancer is one of the most common cancers in China. To understand the basic death situation and disease burden change trend, we analyze the death information of liver cancer among Chinese residents from 2008 to 2021. Methods Data was collected from the Cause-of-Death Surveillance dataset of the National Cause-of-Death Surveillance System from 2008 to 2021. Excel 2016 was used for data entry and to calculate the Crude Mortality Rate (CMR), Age-Standardized Mortality Rate (ASMR), Potential Years of Life Lost (PYLL), and Potential Years of Life Lost Rate (PYLLR). SPSS 25.0 was used to statistically analyze CMR, ASMR, PYLL, and other indicators. Annual percent change (APC) and average APC(AAPC) was used for trend analysis and tested by t tests. Joinpoint 4.9.1.0 was used to calculate APC and AAPC. Age-Period-Cohort model was used to assess the effects of age, period, and birth cohort on liver cancer mortality. Results From 2008 to 2021, 491,701 liver cancer deaths were reported in the National Disease Surveillance Points System. The ASMR of liver cancer in Chinese residents decreased from 27.58/100,000 in 2008 to 17.95/100,000 in 2021 at an average annual rate of 3.40% (t = -5.10, P

Abstract Background There are abundant studies on COVID-19 but few on its impact on hepatitis E. We aimed to assess the effect of the COVID-19 countermeasures on the pattern of hepatitis E incidence and explore the application of time series models in analyzing this pattern. Methods Our pivotal idea was to fit a pre-COVID-19 model with data from before the COVID-19 outbreak and use the deviation between forecast values and actual values to reflect the effect of COVID-19 countermeasures. We analyzed the pattern of hepatitis E incidence in China from 2013 to 2018. We evaluated the fitting and forecasting capability of 3 methods before the COVID-19 outbreak. Furthermore, we employed these methods to construct pre-COVID-19 incidence models and compare post-COVID-19 forecasts with reality. Results Before the COVID-19 outbreak, the Chinese hepatitis E incidence pattern was overall stationary and seasonal, with a peak in March, a trough in October, and higher levels in winter and spring than in summer and autumn, annually. Nevertheless, post-COVID-19 forecasts from pre-COVID-19 models were extremely different from reality in sectional periods but congruous in others. Conclusions Since the COVID-19 pandemic, the Chinese hepatitis E incidence pattern has altered substantially, and the incidence has greatly decreased. The effect of the COVID-19 countermeasures on the pattern of hepatitis E incidence was temporary. The incidence of hepatitis E was anticipated to gradually revert to its pre-COVID-19 pattern.

Background: Dexmedetomidine (DEX), an adjuvant anesthetic, may improve the clinical outcomes of liver transplantation (LT).Methods: We summarized the relevant clinical trials of DEX in patients undergoing LT. As of 30 January 2023, we searched The Cochrane Library, MEDLINE, EMBASE, Clinical Trial.gov and the WHO ICTRP. The main outcomes were postoperative liver and renal function. The random effect model or fixed effect model was used to summarize the outcomes across centers based on the differences in heterogeneity.Results: The meta-analysis included nine studies in total. Compared with the control group, the DEX group had a reduced warm ischemia time (MD-4.39; 95% CI-6.74−‐2.05), improved postoperative liver (peak aspartate transferase: MD-75.77, 95% CI-112.81−‐38.73; peak alanine transferase: MD-133.51, 95% CI-235.57−‐31.45) and renal function (peak creatinine: MD-8.35, 95% CI-14.89−‐1.80), and a reduced risk of moderate-to-extreme liver ischemia-reperfusion injury (OR 0.28, 95% CI 0.14-0.60). Finally, the hospital stay of these patients was decreased (MD-2.28, 95% CI-4.00−‐0.56). Subgroup analysis of prospective studies showed that DEX may have better efficacy in living donors and adult recipients.Conclusion: DEX can improve short-term clinical outcomes and shorten the hospital stay of patients. However, the long-term efficacy of DEX and its interfering factors deserves further study.Systematic Review: identifier CRD42022351664.