Reproducibility assessment of rapid strains in cardiac MRI: Insights and recommendations for clinical application.

PURPOSE: Studies have shown the incremental value of strain imaging in various cardiac diseases. However, reproducibility and generalizability has remained an issue of concern. To overcome this, simplified algorithms such as rapid atrioventricular strains have been proposed. This multicenter study aimed to assess the reproducibility of rapid strains in a real-world setting and identify potential predictors for higher interobserver variation. METHODS: A total of 4 sites retrospectively identified 80 patients and 80 healthy controls who had undergone cardiac magnetic resonance imaging (CMR) at their respective centers using locally available scanners with respective field strengths and imaging protocols. Strain and volumetric parameters were measured at each site and then independently re-evaluated by a blinded core lab. Intraclass correlation coefficients (ICC) and Bland-Altman plots were used to assess inter-observer agreement. In addition, backward multiple linear regression analysis was performed to identify predictors for higher inter-observer variation. RESULTS: There was excellent agreement between sites in feature-tracking and rapid strain values (ICC ≥ 0.96). Bland-Altman plots showed no significant bias. Bi-atrial feature-tracking and rapid strains showed equally excellent agreement (ICC ≥ 0.96) but broader limits of agreement (≤18.0 % vs. ≤3.5 %). Regression analysis showed that higher field strength and lower temporal resolution (>30 ms) independently predicted reduced interobserver agreement for bi-atrial strain parameters (ß = 0.38, p = 0.02 for field strength and ß = 0.34, p = 0.02 for temporal resolution). CONCLUSION: Simplified rapid left ventricular and bi-atrial strain parameters can be reliably applied in a real-world multicenter setting. Due to the results of the regression analysis, a minimum temporal resolution of 30 ms is recommended when assessing atrial deformation.

Description, identification, and growth of Tuber borchii Vittad. mycorrhized Pinus sylvestris L. seedlings on different lime contents.

Tuber borchii forms ectomycorrhiza with oaks, hazel, and pines, including Pinus sylvestris. However, its ectomycorrhiza morphotype with P. sylvestris was not comprehensively described so far, and molecular analyses are missing despite a high danger of misidentification of T. borchii ectomycorrhiza with other closely related and less valuable truffle species. We described for the first time the morphology and anatomy of T. borchii-P. sylvestris ectomycorrhiza using differential interference contrast technique and semi-thin sections in combination with molecular confirmation of identity. Color of ectomycorrhiza is reddish to dark brown, and morphotypes are unevenly but densely covered by warts-bearing pin-like cystidia. All layers of the hyphal mantle are pseudoparenchymatous with outer mantle layer formed of epidermoid cells. T. borchii ectomycorrhiza was identified by a molecular comparison with fruitbodies used for inoculation and its respective ectomycorrhizae. T. borchii has a wide ecological amplitude. To get a better insight in mycorrhization requirements, we investigated growth of P. sylvestris and its ectomycorrhiza infection rate with T. borchii in substrate with different lime content. The mycorrhization of P. sylvestris with T. borchii in the mycorrhization substrate and cultivation in greenhouse conditions was successful, with colonization of P. sylvestris varying between 36.5 and 48.1%. There was no significant correlation of mycorrhization to applied lime contents, and consequently to pH in substrate, while the increased levels of lime improved growth of the P. sylvestris seedlings.

Matrix Effects of Urine Marker Substances in LC-MS/MS Analysis of Drug of Abuse.

BACKGROUND: Analysis of drug abuse is frequently performed using high-performance liquid chromatography with an MS/MS detector and electrospray ionization. In this context, matrix effects, like signal reduction by ion suppression of individual analytes, play an important role. In this study, the authors evaluated the matrix effect caused by polyethylene glycol (PEG) with chain lengths ranging from 6 to 12 repeating units in drug analysis by LC-MS/MS. Selected chain lengths were used in the Ruma urine marker system. METHODS AND RESULTS: Amphetamines, opiates, opioids, antidepressants, psychotics, benzodiazepines, z-substances, and individual drugs, including THCCOOH, cocaine, LSD, and some of their metabolites were investigated. The matrix effect was investigated at PEG concentrations of 500 mcg/mL and 20 mcg/mL. The effect of each PEG molecule was determined. Furthermore, the effects of different common sample preparations on the PEG matrix effects were evaluated. There was a strong correlation between the retention time of PEG and the drug that was ion-suppressed by PEG. The matrix effect decreased to the point where it was within an acceptable range at the lower PEG concentrations investigated in this study. CONCLUSIONS: Matrix effects were observed for drugs with approximately the same retention times as the individual PEGs. The influence of the different workup methods was not as clear, which may be because of the similar solubilities of the PEGs and some analytes. At low PEG concentrations, the matrix effect was always below 60%, except for nortilidine. All the drugs were detectable. The effect on quantification was less than 15% for substances with deuterated analytes as internal standards and less than 32% for analytes without their own internal standards.

How public catering accelerates sustainability: a German case study.

Public catering has become increasingly important in recent years. With increasing annual customers, the sector's impact on the environment is also growing continuously. At the same time, public catering offers a lever to promote sustainable nutrition that has rarely been used so far. Small changes in kitchen practices and food offers can thus be multiplied into a significant positive impact on environmental challenges, such as climate change or loss of biodiversity due to the large number of servings. In contrast to private households, management decisions in public catering can influence the food-related environmental impact of thousands of customers. This article deals with the nationwide level of greenhouse gas (GHG) emissions and resource use in the German public catering segment "business" and its saving potentials by different scenarios of unsupported and supported recipe revision. In this paper, we define "unsupported" as the intuitive optimization of recipes by employees of public catering businesses. In contrast, "supported" approaches had to meet specific target goals, for example of the Deutsche Gesellschaft für Ernährung; engl. German Nutrition Society or the sustainable level. Specifically, we will test how (A) an unsupported recipe revision, (B) a recipe revision based on dietary recommendations and (C) a recipe revision using scientific guidance affect the environmental impact of a dish. As a methodological framework, an online survey of public catering companies was conducted as well as a scenario analysis at menu level and at nationwide level. The results are based on empirical data on the one hand, and on extrapolations on the other. The results show that the nationwide implementation of recipe revision according to scientific guidance-such as concrete target goals for the GHG emissions per serving-can save up to 44% of resource use in the German business catering sector (which corresponds to 3.4 million tons of resources per year) and as much as 40% of GHG emissions (0.6 million tons GHG emissions per year). Even in the scenario of unsupported recipe revision, GHG and resource savings of up to 20% can be realized. The results show that public catering can reduce its material and carbon footprint by 20% overnight. Moreover, the findings show indications for the sustainable transformation of public catering. Nevertheless, it must be noted that these are some first steps of the transformation, which will require further changes with even greater impacts and political activities. Supplementary Information: The online version contains supplementary material available at 10.1007/s11625-022-01183-2.

Assessment of right ventricular size and function from cardiovascular magnetic resonance images using artificial intelligence.

BACKGROUND: Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, despite the complex RV geometry. However, in our recent study, commercially available deep learning (DL) algorithms for RVEF quantification performed poorly in some patients. The current study was designed to test the hypothesis that quantification of RV function could be improved in these patients by using more diverse CMR datasets in addition to domain-specific quantitative performance evaluation metrics during the cross-validation phase of DL algorithm development. METHODS: We identified 100 patients from our prior study who had the largest differences between manually measured and automated RVEF values. Automated RVEF measurements were performed using the original version of the algorithm (DL1), an updated version (DL2) developed from a dataset that included a wider range of RV pathology and validated using multiple domain-specific quantitative performance evaluation metrics, and conventional methodology performed by a core laboratory (CORE). Each of the DL-RVEF approaches was compared against CORE-RVEF reference values using linear regression and Bland-Altman analyses. Additionally, RVEF values were classified into 3 categories: ≤ 35%, 35-50%, and ≥ 50%. Agreement between RVEF classifications made by the DL approaches and the CORE measurements was tested. RESULTS: CORE-RVEF and DL-RVEFs were obtained in all patients (feasibility of 100%). DL2-RVEF correlated with CORE-RVEF better than DL1-RVEF (r = 0.87 vs. r = 0.42), with narrower limits of agreement. As a result, DL2 algorithm also showed increasing accuracy from 0.53 to 0.80 for categorizing RV function. CONCLUSIONS: The use of a new DL algorithm cross-validated on a dataset with a wide range of RV pathology using multiple domain-specific metrics resulted in a considerable improvement in the accuracy of automated RVEF measurements. This improvement was demonstrated in patients whose images were the most challenging and resulted in the largest RVEF errors. These findings underscore the critical importance of this strategy in the development of DL approaches for automated CMR measurements.