Clinical impact and predictors of carotid artery in-stent restenosis

To assess the incidence and clinical significance as well as predictors of in-stent restenosis (ISR) after carotid artery stenting (CAS) diagnosed with serial duplex sonography investigations. We analyzed 215 CAS procedures that had clinical and serial carotid duplex ultrasound investigations. The incidence of in-stent restenosis (ISR) and periprocedural as well as long-term clinical complications were recorded. The influence of an ISR on clinical complication was analyzed using Kaplan-Meier curves and clinical risk factors for the development of an ISR with multivariate logistic regression. During a median follow-up time of 33.4 months (interquartile range 15.3–53.7) an ISR of ≥70% was detected in 12 (6.1%) of 215 arteries (mean age of 68.1 ± 9.8 years, 71.6% male). The combined stroke and death rate during long-term follow-up was significantly higher in the group with an ISR [odds ratio (OR): 3.59, 95% confidence interval (CI): 1.50–8.59, p = 0.004]. After applying multivariate logistic regression analysis contralateral carotid occlusion (OR 10.11, 95% CI 2.06–49.63, p = 0.004), carotid endarterectomy (CEA) restenosis (OR 8.87, 95% CI 1.68–46.84, p = 0.010) and postprocedural carotid duplex ultrasound with a PSV ≥120 cm/s (OR 6.33, 95% CI 1.27–31.44, p = 0.024) were independent predictors of ISR. ISR after CAS during long-term follow-up is associated with a higher proportion of clinical complications. A close follow-up is suggested especially in those patients with the aforementioned independent predictors of an ISR. Against the background of a lacking established treatment of ISR, these findings should be taken into account when offering CAS as a treatment alternative to CEA

Effects of land use and climate on carbon and nitrogen pool partitioning in European mountain grasslands

European mountain grasslands are increasingly affected by land-use changes and climate, which have been suggested to exert important controls on grassland carbon (C) and nitrogen (N) pools. However, so far there has been no synthetic study on whether and how land-use changes and climate interactively affect the partitioning of these pools amongst the different grassland compartments. We analyzed the partitioning of C and N pools of 36 European mountain grasslands differing in land-use and climate with respect to above- and belowground phytomass, litter and topsoil (top 23 cm). We found that a reduction of management intensity and the abandonment of hay meadows and pastures increased above-ground phytomass, root mass and litter as well as their respective C and N pools, concurrently decreasing the fractional contribution of the topsoil to the total organic carbon pool. These changes were strongly driven by the cessation of cutting and grazing, a shift in plant functional groups and a related reduction in litter quality. Across all grasslands studied, variation in the impact of land management on the topsoil N pool and C/N-ratio were mainly explained by soil clay content combined with pH. Across the grasslands, below-ground phytomass as well as phytomass- and litter C concentrations were inversely related to the mean annual temperature; furthermore, C/N- ratios of phytomass and litter increased with decreasing mean annual precipitation. Within the topsoil compartment, C concentrations decreased from colder to warmer sites, and increased with increasing precipitation. Climate generally influenced effects of land use on C and N pools mainly through mean annual temperature and less through mean an- nual precipitation. We conclude that site-specific conditions need to be considered for understanding the effects of land use and of current and future climate changes on grassland C and N pools.Peer reviewe

Inflammation and In-Stent Restenosis: The Role of Serum Markers and Stent Characteristics in Carotid Artery Stenting

BACKGROUND: Carotid angioplasty and stenting (CAS) may currently be recommended especially in younger patients with a high-grade carotid artery stenosis. However, evidence is accumulating that in-stent restenosis (ISR) could be an important factor endangering the long-term efficacy of CAS. The aim of this study was to investigate the influence of inflammatory serum markers and procedure-related factors on ISR as diagnosed with duplex sonography. METHODS: We analyzed 210 CAS procedures in 194 patients which were done at a single university hospital between May 2003 and June 2010. Periprocedural C-reactive protein (CRP) and leukocyte count as well as stent design and geometry, and other periprocedural factors were analyzed with respect to the occurrence of an ISR as diagnosed with serial carotid duplex ultrasound investigations during clinical long-term follow-up. RESULTS: Over a median of 33.4 months follow-up (IQR: 14.9-53.7) of 210 procedures (mean age of 67.9±9.7 years, 71.9% male, 71.0% symptomatic) an ISR of ≥70% was detected in 5.7% after a median of 8.6 months (IQR: 3.4-17.3). After multiple regression analysis, leukocyte count after CAS-intervention (odds ratio (OR): 1.31, 95% confidence interval (CI): 1.02-1.69; p = 0.036), as well as stent length and width were associated with the development of an ISR during follow-up (OR: 1.25, 95% CI: 1.05-1.65, p = 0.022 and OR: 0.28, 95% CI: 0.09-0.84, p = 0.010). CONCLUSIONS: The majority of ISR during long-term follow-up after CAS occur within the first year. ISR is associated with periinterventional inflammation markers and influenced by certain stent characteristics such as stent length and width. Our findings support the assumption that stent geometry leading to vessel injury as well as periprocedural inflammation during CAS plays a pivotal role in the development of carotid artery ISR

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km² resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e., offset) between in-situ soil temperature measurements, based on time series from over 1200 1-km² pixels (summarized from 8500 unique temperature sensors) across all the world’s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world\u27s major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0-5 and 5-15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (-0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications

Periprocedural variables.

<p>pre = preprocedural (within 24 hours before CAS), post = postprocedural (within 24 hours after CAS), CRP = C-reactive protein.</p><p>*factors included into multiple regression analysis.</p>†<p>factors remained significant after multiple regression analysis.</p

Statistically significant results of the multiple regression analysis.

<p>Corrected for: status of the carotid artery stenosis (symptomatic or asymptomatic), age and gender.</p

Baseline patient characteristics (n = 210).

<p>Baseline patient characteristics (n = 210).</p