Declines amongst breeding Eider Somateria mollissima numbers in the Baltic/Wadden Sea flyway

We report on the status of theBaltic/Wadden Sea flyway Eider population based on trends in breeding and wintering numbers throughout the region, supplemented by changes in the sex ratio and proportion of young Eiders as monitored in the Danish hunting bag. At the flyway scale, total numbers of breeding pairs decreased by 48% during 2000–2009, after relatively stable breeding numbers in 1991–2000. The majority of the population nest in Finland and Sweden,where the number of breeding pairs has halved over the same period. After initial declines in winter numbers between 1991 and 2000, during 2000–2009, national wintering numbers increased in the Baltic Sea, but decreased in the Wadden Sea. The annual proportion of adult females in the Danish hunting bag data de creased from ca.45%(1982) to ca.25%(2009) and simultaneously the proportion of firstwinter birds fell from ca. 70% to ca. 30%, indicating dramatic structural changes in the Danish wintering numbers. These results suggest that the total flyway populationwill experience further declines, unless productivity increases and the factors responsible for decreasing adult female survival are identified and ameliorated.We discuss potential population drivers and present some recommendations for improved flyway-levelmonitoring and management of Eiders

Land sparing versus land sharing:Moving forward

To address the challenges of biodiversity conservation and commodity production, a framework has been proposed that distinguishes between the integration (land sharing) and separation (land sparing) of conservation and production. Controversy has arisen around this framework partly because many scholars have focused specifically on food production rather than more encompassing notions such as land scarcity or food security. Controversy further surrounds the practical value of partial trade-off analyses, the ways in which biodiversity should be quantified, and a series of scale effects that are not readily accounted for. We see key priorities for the future in (1) addressing these issues when using the existing framework, and (2) developing alternative, holistic ways to conceptualise challenges related to food, biodiversity, and land scarcity

Lipidomics: A Tool for Studies of Atherosclerosis

Lipids, abundant constituents of both the vascular plaque and lipoproteins, play a pivotal role in atherosclerosis. Mass spectrometry-based analysis of lipids, called lipidomics, presents a number of opportunities not only for understanding the cellular processes in health and disease but also in enabling personalized medicine. Lipidomics in its most advanced form is able to quantify hundreds of different molecular lipid species with various structural and functional roles. Unraveling this complexity will improve our understanding of diseases such as atherosclerosis at a level of detail not attainable with classical analytical methods. Improved patient selection, biomarkers for gauging treatment efficacy and safety, and translational models will be facilitated by the lipidomic deliverables. Importantly, lipid-based biomarkers and targets should lead the way as we progress toward more specialized therapeutics

Lipidomics needs more standardization

Modern mass spectrometric technologies provide quantitative readouts for a wide variety of lipid specimens. However, many studies do not report absolute lipid concentrations and differ vastly in methodologies, workflows, and data presentation. Therefore, we appeal to researchers to engage with the Lipidomics Standards Initiative to develop common standards for minimum acceptable data quality and reporting for lipidomics data to take lipidomics research to the next level

Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics

Background: Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer. Methodology/Principal Findings: Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient's age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer. Conclusions/Significance: Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy

Smoking is a predictor of complications in all types of surgery : a machine learning-based big data study

Background: Machine learning algorithms are promising tools for smoking status classification in big patient data sets. Smoking is a risk factor for postoperative complications in major surgery. Whether this applies to all surgery is unknown. The aims of this retrospective cohort study were to develop a machine learning algorithm for clinical record-based smoking status classification and to determine whether smoking and former smoking predict complications in all surgery types. Methods: All surgeries performed in a Finnish hospital district from 1 January 2015 to 31 December 2019 were analysed. Exclusion criteria were age below 16 years, unknown smoking status, and unknown ASA class. A machine learning algorithm was developed for smoking status classification. The primary outcome was 90-day overall postoperative complications in all surgeries. Secondary outcomes were 90-day overall complications in specialties with over 10 000 surgeries and critical complications in all surgeries. Results: The machine learning algorithm had precisions of 0.958 for current smokers, 0.974 for ex-smokers, and 0.95 for never-smokers. The sample included 158 638 surgeries. In adjusted logistic regression analyses, smokers had increased odds of overall complications (odds ratio 1.17; 95 per cent c.i. 1.14 to 1.20) and critical complications (odds ratio 1.21; 95 per cent c.i. 1.14 to 1.29). Corresponding odds ratios of ex-smokers were 1.09 (95 per cent c.i. 1.06 to 1.13) and 1.09 (95 per cent c.i. 1.02 to 1.17). Smokers had increased odds of overall complications in all specialties with over 10 000 surgeries. ASA class was the most important complication predictor. Conclusion: Machine learning algorithms are feasible for smoking status classification in big surgical data sets. Current and former smoking predict complications in all surgery types.Peer reviewe

ApoCIII-Enriched LDL in Type 2 Diabetes Displays Altered Lipid Composition, Increased Susceptibility for Sphingomyelinase, and Increased Binding to Biglycan

Objective- Apolipoprotein CIII (apoCIII) is an independent risk factor for cardiovascular disease, but the molecular mechanisms involved are poorly understood. Here, we investigated potential proatherogenic properties of apoCIII-containing LDL from hypertriglyceridemic patients with type 2 diabetes. Research design and methods - LDL was isolated from controls and subjects with type 2 diabetes, and from apoB transgenic mice. LDL-biglycan binding was analyzed with a solid-phase assay using immunoplates coated with biglycan. Lipid composition was analyzed with mass spectrometry. Hydrolysis of LDL by sphingomyelinase was analyzed after labeling plasma LDL with [(3)H]sphingomyelin. ApoCIII isoforms were quantified after isoelectric focusing. Human aortic endothelial cells were incubated with desialylated apoCIII or with LDL enriched with specific apoCIII isoforms. Results- We showed that enriching LDL with apoCIII only induced a small increase in LDL-proteoglycan binding, and this effect was dependent on a functional Site A in apoB100. Our findings indicated that intrinsic characteristics of the diabetic LDL other than apoCIII per se are responsible for further increased proteoglycan binding of diabetic LDL with high endogenous apoCIII, and we showed alterations in the lipid composition of diabetic LDL with high apoCIII. We also demonstrated that high apoCIII increased susceptibility of LDL to hydrolysis and aggregation by SMase. In addition, we demonstrated that sialylation of apoCIII increased with increasing apoCIII content, and that sialylation of apoCIII was essential for its proinflammatory properties. Conclusions- We have demonstrated a number of features of apoCIII-containing LDL from hypertriglyceridemic patients with type 2 diabetes that could explain the proatherogenic role of apoCIII

hiPSC-derived hepatocytes closely mimic the lipid profile of primary hepatocytes: A future personalised cell model for studying the lipid metabolism of the liver

Peer reviewe

Reproducibility of exhaled nitric oxide in smokers and non-smokers: relevance for longitudinal studies

<p>Abstract</p> <p>Background</p> <p>Currently, there is much interest in measuring fractional exhaled nitric oxide (<b>FE_NO</b>) in populations. We evaluated the reproducibility of <b>FE_NO</b>in healthy subjects and determined the number of subjects necessary to carry out a longitudinal survey of <b>FE_NO</b>in a population containing smokers and non-smokers, based on the assessed reproducibility.</p> <p>Methods</p> <p>The reproducibility of <b>FE_NO</b>was examined in 18 healthy smokers and 21 non-smokers. <b>FE_NO</b>was assessed once at 9 AM on five consecutive days; in the last day this measurement was repeated at 2 PM. Respiratory symptoms and medical history were assessed by questionnaire. The within- and between-session repeatability of <b>FE_NO</b>and log-transformed <b>FE_NO</b>was described. The power of a longitudinal study based on a relative increase in <b>FE_NO</b>was estimated using a bilateral t-test of the log-transformed <b>FE_NO</b>using the between-session variance of the assay.</p> <p>Results</p> <p><b>FE_NO</b>measurements were highly reproducible throughout the study. <b>FE_NO</b>was significantly higher in males than females regardless of smoking status. <b>FE_NO</b>was positively associated with height (p < 0.001), gender (p < 0.034), smoking (p < 0.0001) and percent FEV₁/FVC (p < 0.001) but not with age (p = 0.987). The between-session standard deviation was roughly constant on the log scale. Assuming the between-session standard deviation is equal to its longitudinal equivalent, either 111 or 29 subjects would be necessary to achieve an 80% power in detecting a 3% or a 10% increase in <b>FE_NO</b>respectively.</p> <p>Conclusion</p> <p>The good reproducibility of <b>FE_NO</b>is not influenced by gender or smoking habits. In a well controlled, longitudinal study it should allow detecting even small increases in <b>FE_NO</b>with a reasonable population size.</p