Obesity has become one of the important factors affecting the entire management process of liver transplantation. Its impact is not only reflected in the increased risk of liver steatosis and ischemia-reperfusion injury in the donor, but also involves the increased technical difficulty during the perioperative period for the recipient, the increase in postoperative metabolic complications and poorer long-term prognosis. This article, based on recent relevant studies, reviews aspects such as the utilization of obese donors, the characteristics of adult and pediatric recipients, pre-transplant risk assessment and weight loss intervention, and post-transplant weight and metabolic management. Current evidence suggests that simply judging transplantation risks based on body mass index has limitations. Analysis of body composition, assessment of cardiovascular function and identification of sarcopenic obesity are more clinically valuable. For obese liver transplant recipients, in a multidisciplinary collaboration framework, emphasis should be placed on individualized screening, perioperative optimization and long-term follow-up management to improve the outcomes of the graft and the recipient.

Objective: To investigate the spatio-temporal clustering characteristics of influenza in Yinzhou District, Ningbo City, Zhejiang Province from 2017 to 2021, so as to provide insights into prevention and control of influenza. Methods Data of influenza in Yinzhou District from 2017 to 2021 were collected from the Chinese Disease Prevention and Control Information System. The software ArcGIS 10.8 was employed for spatial autocorrelation analysis, and SaTScan 10.1 was employed for spatio-temporal scanning to analyze the temporal and spatial clustering characteristics of influenza incidence in Yinzhou District. Methods: Data of influenza in Yinzhou District from 2017 to 2021 were collected from the Chinese Disease Prevention and Control Information System. The software ArcGIS 10.8 was employed for spatial autocorrelation analysis, and SaTScan 10.1 was employed for spatio-temporal scanning to analyze the temporal and spatial clustering characteristics of influenza incidence in Yinzhou District. Results: Totally 60 543 influenza cases were reported in Yinzhou District from 2017 to 2021, with an incidence of 0.76%. The incidence of influenza peaked in December 2019 (9.35%) and January 2020 (9.28%) during the period between 2017 and 2021. Spatial autocorrelation analysis showed that there was a positive spatial correlation of influenza incidence in Yinzhou District from 2018 to 2021 (all P<0.05), and a high clustering in 2019 and 2021. Zhonghe Street showed a low-high clustering from 2017 to 2020; Jiangshan Town showed a low-high clustering in 2017 and 2020, and a high-high clustering in 2019 and 2021; Shounan Street showed a high-high clustering from 2018 to 2020; Yunlong Street showed a high-high clustering in 2021. Spatio-temporal scanning analysis showed that the class Ⅰ clusters were located in the central region which centered in Dongqianhu Town, with aggregation time in August 2017, in the northwest region with aggregation time in December and January from 2018 to 2020, and in the west region with aggregation time in August 2021. Conclusion The incidence of influenza in Yinzhou District from 2017 to 2021 showed a spatio-temporal clustering in the northwestern region in winter and summer.