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Cytokine concentrations in biological fluids are widely used markers for activation of immunological processes. Confirming the reproducibility of measurements is important, especially in longitudinal or multicenter studies where time between analyses or different analyzing laboratories increases the intra-assay variability. In this study, the reproducibility of the cytokine analysis conducted with different assay platforms was studied by comparing the results of two cytokines [interleukin (IL)-6 in serum and nasal lavage fluid (NAL) and IL-8 in NAL] analyzed with Meso Scale Discovery (MSD) ultra-sensitive single and multiplex assay kits (n = 76). In addition, the difference in cytokine levels between two biological sample matrices was studied by comparing the results of altogether 9 cytokines [IL-6, IL-2, IL-8, IL12p70, IL-1β, granulocyte–macrophage colony-stimulating factor (GM-CSF), interferon (IFN)γ, IL-10 and tumor necrosis factor (TNF)α] measured from serum and NAL of the same study subjects (n = 460). The results show that the cytokine concentrations analyzed with single and multiplex assays are concordant but not equal. Comparison of the different matrices revealed that cytokine concentrations in serum do not correspond with concentrations detected in nasal lavage fluid. It can be concluded that comparability of the results from single and multiplex analysis of cytokines is high, but the concentrations should not be compared directly with each other. The differences between concentrations analyzed from serum and nasal lavage fluid indicate that the levels are specific for each matrix and represent distinct immunological conditions. (aut.ref.

Terrestrial organic matter input drives sedimentary trace metal sequestration in a human-impacted boreal estuary

Coastal sediments play a fundamental role in processing anthropogenic trace metal inputs. Previous studies have shown that terrestrial organic matter (OM) is a significant vector for trace metal transport across the land-to-sea continuum, but little is known about the fate of land-derived metal-OM complexes in coastal sediments. Here, we use a comprehensive set of sediment pore water and solid-phase analyses to investigate how variations in terrestrial OM delivery since the 1950s have influenced trace metal accumulation and diagenesis in a human-impacted boreal estuary in the northern Baltic Sea. A key feature of our dataset is a strong correlation between terrestrial OM deposition and accumulation of metal-OM complexes in the sediments. Based on this strong coupling, we infer that the riverine input of terrestrial metal-OM complexes from the hinterland, followed by flocculation-induced settling in the estuary, effectively modulates sedimentary trace metal sequestration. While part of the trace metal pool associated with these complexes is efficiently recycled in the surface sediments during diagenesis, a substantial fraction is permanently buried as refractory metal-OM complexes or through incorporation into insoluble sulfides, thereby escaping further biological processing. These findings suggest that terrestrial OM input could play a more pivotal role in trace metal processing in coastal environments than hitherto acknowledged.</p

Carrying Ideas from Knowledge-Based Configuration to Software Product Lines

Software variability modelling (SVM) has become a central concern in software product lines -- especially configurable software product lines (CSPL) require rigorous SVM. Dynamic SPLs, service oriented SPLs, and autonomous or pervasive systems are examples where CSPLs are applied. Knowledge-based configuration (KBC) is an established way to address variability modelling aiming for the automatic product configuration of physical products. Our aim was to study what major ideas from KBC can be applied to SVM, particularly in the context of CSPLs. Our main contribution is the identification of major ideas from KBC that could be applied to SVM. First, we call for the separation of types and instances. Second, conceptual clarity of modelling concepts, e.g., having both taxonomical and compositional relations would be useful. Third, we argue for the importance of a conceptual basis that provides a foundation for multiple representations, e.g., graphical and textual. Applying the insights and experiences embedded in these ideas may help in the development of modelling support for software product lines, particularly in terms of conceptual clarity and as a basis for tool support with a high level of automation.Peer reviewe

Brain Basis of Psychopathy in Criminal Offenders and General Population.

Psychopathy is characterized by persistent antisocial behavior, impaired empathy, and egotistical traits. These traits vary also in normally functioning individuals. Here, we tested whether such antisocial personalities are associated with similar structural and neural alterations as those observed in criminal psychopathy. Subjects were 100 non-convicted well-functioning individuals, 19 violent male offenders, and 19 matched controls. Subjects underwent T1-weighted magnetic resonance imaging and viewed movie clips with varying violent content during functional magnetic resonance imaging. Psychopathic traits were evaluated with Levenson Self-Report Psychopathy Scale (controls) and Psychopathy Checklist-Revised (offenders). Psychopathic offenders had lower gray matter density (GMD) in orbitofrontal cortex and anterior insula. In the community sample, affective psychopathy traits were associated with lower GMD in the same areas. Viewing violence increased brain activity in periaqueductal grey matter, thalamus, somatosensory, premotor, and temporal cortices. Psychopathic offenders had increased responses to violence in thalamus and orbitofrontal, insular, and cingulate cortices. In the community sample, impulsivity-related psychopathy traits were positively associated with violence-elicited responses in similar areas. We conclude that brain characteristics underlying psychopathic spectrum in violent psychopathy are related to those observed in well-functioning individuals with asocial personality features

Genome-wide association analyses of symptom severity among clozapine-treated patients with schizophrenia spectrum disorders

Clozapine is the most effective antipsychotic for patients with treatment-resistant schizophrenia. However, response is highly variable and possible genetic underpinnings of this variability remain unknown. Here, we performed polygenic risk score (PRS) analyses to estimate the amount of variance in symptom severity among clozapine-treated patients explained by PRSs (R2) and examined the association between symptom severity and genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activity. Genome-wide association (GWA) analyses were performed to explore loci associated with symptom severity. A multicenter cohort of 804 patients (after quality control N = 684) with schizophrenia spectrum disorder treated with clozapine were cross-sectionally assessed using the Positive and Negative Syndrome Scale and/or the Clinical Global Impression-Severity (CGI-S) scale. GWA and PRS regression analyses were conducted. Genotype-predicted CYP1A2, CYP2D6, and CYP2C19 enzyme activities were calculated. Schizophrenia-PRS was most significantly and positively associated with low symptom severity (p = 1.03 × 10−3; R2 = 1.85). Cross-disorder-PRS was also positively associated with lower CGI-S score (p = 0.01; R2 = 0.81). Compared to the lowest tertile, patients in the highest schizophrenia-PRS tertile had 1.94 times (p = 6.84×10−4) increased probability of low symptom severity. Higher genotype-predicted CYP2C19 enzyme activity was independently associated with lower symptom severity (p = 8.44×10−3). While no locus surpassed the genome-wide significance threshold, rs1923778 within NFIB showed a suggestive association (p = 3.78×10−7) with symptom severity. We show that high schizophrenia-PRS and genotype-predicted CYP2C19 enzyme activity are independently associated with lower symptom severity among individuals treated with clozapine. Our findings open avenues for future pharmacogenomic projects investigating the potential of PRS and genotype-predicted CYP-activity in schizophrenia

All-cause mortality among people with serious mental illness (SMI), substance use disorders, and depressive disorders in southeast London: a cohort study

<p>Abstract</p> <p>Background</p> <p>Higher mortality has been found for people with serious mental illness (SMI, including schizophrenia, schizoaffective disorders, and bipolar affective disorder) at all age groups. Our aim was to characterize vulnerable groups for excess mortality among people with SMI, substance use disorders, depressive episode, and recurrent depressive disorder.</p> <p>Methods</p> <p>A case register was developed at the South London and Maudsley National Health Services Foundation Trust (NHS SLAM), accessing full electronic clinical records on over 150,000 mental health service users as a well-defined cohort since 2006. The Case Register Interactive Search (CRIS) system enabled searching and retrieval of anonymised information since 2008. Deaths were identified by regular national tracing returns after 2006. Standardized mortality ratios (SMRs) were calculated for the period 2007 to 2009 using SLAM records for this period and the expected number of deaths from age-specific mortality statistics for the England and Wales population in 2008. Data were stratified by gender, ethnicity, and specific mental disorders.</p> <p>Results</p> <p>A total of 31,719 cases, aged 15 years old or more, active between 2007-2009 and with mental disorders of interest prior to 2009 were detected in the SLAM case register. SMRs were 2.15 (95% CI: 1.95-2.36) for all SMI with genders combined, 1.89 (1.64-2.17) for women and 2.47 (2.17-2.80) for men. In addition, highest mortality risk was found for substance use disorders (SMR = 4.17; 95% CI: 3.75-4.64). Age- and gender-standardised mortality ratios by ethnic group revealed huge fluctuations, and SMRs for all disorders diminished in strength with age. The main limitation was the setting of secondary mental health care provider in SLAM.</p> <p>Conclusions</p> <p>Substantially higher mortality persists in people with serious mental illness, substance use disorders and depressive disorders. Furthermore, mortality risk differs substantially with age, diagnosis, gender and ethnicity. Further research into specific risk groups is required.</p

Microbial−mammalian cometabolites dominate the age-associated urinary metabolic phenotype in Taiwanese and American populations

Understanding the metabolic processes associated with aging is key to developing effective management and treatment strategies for age-related diseases. We investigated the metabolic profiles associated with age in a Taiwanese and an American population. 1H NMR spectral profiles were generated for urine specimens collected from the Taiwanese Social Environment and Biomarkers of Aging Study (SEBAS; n = 857; age 54–91 years) and the Mid-Life in the USA study (MIDUS II; n = 1148; age 35–86 years). Multivariate and univariate linear projection methods revealed some common age-related characteristics in urinary metabolite profiles in the American and Taiwanese populations, as well as some distinctive features. In both cases, two metabolites—4-cresyl sulfate (4CS) and phenylacetylglutamine (PAG)—were positively associated with age. In addition, creatine and β-hydroxy-β-methylbutyrate (HMB) were negatively correlated with age in both populations (p < 4 × 10–6). These age-associated gradients in creatine and HMB reflect decreasing muscle mass with age. The systematic increase in PAG and 4CS was confirmed using ultraperformance liquid chromatography–mass spectrometry (UPLC–MS). Both are products of concerted microbial–mammalian host cometabolism and indicate an age-related association with the balance of host–microbiome metabolism

Sequence variations of ABCB1, SLC6A2, SLC6A3, SLC6A4, CREB1, CRHR1 and NTRK2: association with major depression and antidepressant response in Mexican-Americans

We studied seven genes that reflect events relevant to antidepressant action at four sequential levels: (1) entry into the brain, (2) binding to monoaminergic transporters, and (3) distal effects at the transcription level, resulting in (4) changes in neurotrophin and neuropeptide receptors. Those genes are ATP-binding cassette subfamily B member 1 (ABCB1), the noradrenaline, dopamine, and serotonin transporters (SLC6A2, SLC6A3 and SLC6A4), cyclic AMP-responsive element binding protein 1 (CREB1), corticotropin-releasing hormone receptor 1 (CRHR1) and neurotrophic tyrosine kinase type 2 receptor (NTRK2). Sequence variability for those genes was obtained in exonic and flanking regions. A total of 56 280 000 bp across were sequenced in 536 unrelated Mexican Americans from Los Angeles (264 controls and 272 major depressive disorder (MDD)). We detected in those individuals 419 single nucleotide polymorphisms (SNPs); the nucleotide diversity was 0.00054±0.0001. Of those, a total of 204 novel SNPs were identified, corresponding to 49% of all previously reported SNPs in those genes: 72 were in untranslated regions, 19 were in coding sequences of which 7 were non-synonymous, 86 were intronic and 27 were in upstream/downstream regions. Several SNPs or haplotypes in ABCB1, SLC6A2, SLC6A3, SLC6A4, CREB1 and NTRK2 were associated with MDD, and in ABCB1, SLC6A2 and NTRK2 with antidepressant response. After controlling for age, gender and baseline 21-item Hamilton Depression Rating Scale (HAM-D21) score, as well as correcting for multiple testing, the relative reduction of HAM-D21 score remained significantly associated with two NTRK2-coding SNPs (rs2289657 and rs56142442) and the haplotype CAG at rs2289658 (splice site), rs2289657 and rs2289656. Further studies in larger independent samples will be needed to confirm these associations. Our data indicate that extensive assessment of sequence variability may contribute to increase understanding of disease susceptibility and drug response. Moreover, these results highlight the importance of direct re-sequencing of key candidate genes in ethnic minority groups in order to discover novel genetic variants that cannot be simply inferred from existing databases