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COVID-19 associated liver injury:A general review with special consideration of pregnancy and obstetric outcomes

World Journal of Gastroenterology 2022年第42期28卷 6017-6033页

作者： Katherine M.Cooper Alessandro Colletta Alison M.Asirwatham tiffany a.moore simas Deepika Devuni Department of Medicine University of Massachusetts Chan Medical SchoolWorcesterMA 01605United States Department of Obstetrics and Gynecology University of Massachusetts Chan Medical SchoolWorcesterMA 01605United States Departments of Pediatrics Psychiatryand Population&Quantitative Health SciencesUniversity of Massachusetts Chan Medical SchoolWorcesterMA 01605United States Division of Gastroenterology and Hepatology University of Massachusetts Chan Medical SchoolWorcesterMA 1605United States

Liver injury is an increasingly recognized extra-pulmonary manifestation of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)*** disease 2019(COVID-19)associated liver injury(COVALI)is a clinical syndrome encompassing all patients with biochemical liver injury identified in the setting of SARS-CoV-2 *** profound clinical implications,its pathophysiology is poorly ***,most information on COVALI is derived from the general population and may not be applicable to individuals under-represented in research,including pregnant *** manuscript reviews:Clinical features of COVALI,leading theories of COVALI,and existing literature on COVALI during pregnancy,a topic not widely explored in the ***,we synthesized data from the general and perinatal populations that demonstrates COVALI to be a hepatocellular transaminitis that is likely induced by systemic inflammation and that is strongly associated with disease severity and poorer clinical outcome,and offered perspective on approaching transaminitis in the potentially COVID-19 positive patient in the obstetric setting.

关键词： COVID-19 liver injury Pregnancy Perinatal liver disease Systemic inflammation Special populations

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Reconstructing landscapes of ungulate parturition and predation using vegetation phenology

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Current Zoology 2022年第3期68卷 275-283页

作者： Paige VAN DE VUURST Seth a.moore Edmund J.ISAAC Yvette CHENAUX-IBRAHIM tiffany M.WOLF Luis E.ESCOBAR Department of Fish and Wildlife Conservation Virginia TechBlacksburgVA 24061USA Department of Biology and Environment Grand Portage Band of Lake Superior ChippewaGrand PortageMN 55605USA Veterinary Population Medicine University of MinnesotaSt PaulMN 55108USA

Enhanced vegetation index(EVI)data can be used to identify and define the space in which ungulates practice parturition and encounter *** study explores the use of EVI data to identify landscapes linked to ungulate parturition and predation events across space,time,and environmental *** a case study,we used the moose population(Alces alces)of northern Minnesota in the *** remotely sensed EVI data rasters and global positioning system collar data,we quantified how vegetation phenology and moose movement shaped the births and predation of 52 moose calves from 2013 to 2020 on or adjacent to the Grand Portage Indian *** known sources of predation were American black bears(Ursus americanus,n=22)and gray wolves(Canis lupus,n=28).Satellite-derived data summarizing seasonal landscape features at the local level revealed that landscape heterogeneity use by moose can help to quantitatively identify landscapes of parturition and predation in space and time across large *** phenology proved to be differentiable between adult moose ranges,sites of cow parturition,and sites of calf *** characteristics of each moose group were consistent and tractable based on environment,suggesting that sites of parturition and predation of moose are predictable in space and *** is possible that moose selected specific landscapes for parturition despite risk of increased predation of their calves,which could be an example of an"ecological trap."This analytical framework can be employed to identify areas for future ungulate research on the impacts of landscape on parturition and predation dynamics.

关键词： bear EVI moose parturition predation prey vegetation phenology

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