Skeletal muscle-related symptoms are common in both acute coronavirus disease (Covid)-19 and post-acute sequelae of Covid-19 (PASC). In this narrative review, we discuss cellular and molecular pathways that are affected and consider these in regard to skeletal muscle involvement in other conditions, such as acute respiratory distress syndrome, critical illness myopathy, and post-viral fatigue syndrome. Patients with severe Covid-19 and PASC suffer from skeletal muscle weakness and exercise intolerance. Histological sections present muscle fibre atrophy, metabolic alterations, and immune cell infiltration. Contributing factors to weakness and fatigue in patients with severe Covid-19 include systemic inflammation, disuse, hypoxaemia, and malnutrition. These factors also contribute to post-intensive care unit (ICU) syndrome and ICU-acquired weakness and likely explain a substantial part of Covid-19-acquired weakness. The skeletal muscle weakness and exercise intolerance associated with PASC are more obscure. Direct severe acute respiratory syndrome coronavirus (SARS-CoV)-2 viral infiltration into skeletal muscle or an aberrant immune system likely contribute. Similarities between skeletal muscle alterations in PASC and chronic fatigue syndrome deserve further study. Both SARS-CoV-2-specific factors and generic consequences of acute disease likely underlie the observed skeletal muscle alterations in both acute Covid-19 and PASC.
Permanent grassland soils can act as a sink for carbon and may therefore positively contribute to climate change mitigation and adaptation. We compared young (5–15 years since latest grassland renewal) with old (>20 years since latest grassland renewal) permanent grassland soils in terms of carbon stock, carbon sequestration, drought tolerance and flood resistance. The research was carried out on marine clay soil at 10 dairy farms with young and old permanent grassland. As hypothesized, the carbon stock was larger in old grassland (62 Mg C ha−1) topsoil (0–10 cm) than in young grassland topsoil (51 Mg C ha−1). The carbon sequestration rate was greater in young (on average 3.0 Mg C ha−1 year−1) compared with old grassland (1.6 Mg C ha−1 year−1) and determined by initial carbon stock. Regarding potential drought tolerance, we found larger soil moisture and soil organic matter (SOM) contents in old compared with young grassland topsoils. As hypothesized, the old grassland soils were more resistant to heavy rainfall as measured by water infiltration rate and macroporosity (at 20 cm depth) in comparison with the young grassland soils. In contrast to our hypothesis we did not find a difference in rooting between young and old permanent grassland, probably due to large variability in root biomass and root tip density. We conclude that old grasslands at dairy farms on clay soil can contribute more to the ecosystem services climate change mitigation and climate change adaptation than young grasslands. This study shows that under real farm conditions on a clay topsoil, carbon stock increases with grassland age and even after 30 years carbon saturation has not been reached. Further study is warranted to determine by how much extending grassland age can contribute to climate change mitigation and adaptation.
MULTIFILE
Background & aims: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in transcatheter aortic valve implantation (TAVI) patients, using automatic deep learning algorithms to assess muscle quality on procedural computed tomography (CT) scans. Methods: This study included 1199 patients with severe aortic stenosis who underwent transcatheter aortic valve implantation (TAVI) between January 2010 and January 2020. A procedural CT scan was performed as part of the preprocedural-TAVI evaluation, and the scans were analyzed using deep-learning-based software to automatically determine skeletal muscle density (SMD) and intermuscular adipose tissue (IMAT). The association of SMD and IMAT with all-cause mortality was analyzed using a Cox regression model, adjusted for other known mortality predictors, including muscle mass. Results: The mean age of the participants was 80 ± 7 years, 53% were female. The median observation time was 1084 days, and the overall mortality rate was 39%. We found that the lowest tertile of muscle quality, as determined by SMD, was associated with an increased risk of mortality (HR 1.40 [95%CI: 1.15–1.70], p < 0.01). Similarly, low muscle quality as defined by high IMAT in the lowest tertile was also associated with increased mortality risk (HR 1.24 [95%CI: 1.01–1.52], p = 0.04). Conclusions: Our findings suggest that deep learning-assessed low muscle quality, as indicated by fat infiltration in muscle tissue, is a practical, useful and independent predictor of mortality after TAVI.
