The impact of telephone crisis services on suicidal users: A systematic review of the past 45 years

Purpose – Telephone crisis services are increasingly subject to a requirement to “prove their worth” as a suicide prevention strategy. The purpose of this paper is to: first, provide a detailed overview of the evidence on the impact of telephone crisis services on suicidal users; second, determine the limitations of the outcome measures used in this evidence; and third, suggest directions for future research. Design/methodology/approach – MEDLINE via Pubmed (from 1966), PsycINFO APA (from 1967) and ProQuest Dissertation and Theses (all to 4 June 2015) were searched. Papers were systematically extracted by title then abstract according to predefined inclusion and exclusion criteria. Findings – In total, 18 articles met inclusion criteria representing a range of outcome measures: changes during calls, reutilization of service, compliance with advice, caller satisfaction and counsellor satisfaction. The majority of studies showed beneficial impact on an immediate and intermediate degree of suicidal urgency, depressive mental states as well as positive feedback from users and counsellors. Research limitations/implications – A major limitation pertains to differences in the use of the term “suicidal”. Other limitations include the lack of long-term follow-up and of controlled research designs. Future research should include a focus on long-term follow-up designs, involving strict data protection. Furthermore, more qualitative research is needed in order to capture the essential nature of the intervention. Originality/value – This paper attempts to broaden the study and the concept of “effectiveness” as hitherto used in the literature about telephone crisis services and offers suggestions for future research. </jats:sec

The ethical context of conducting interventions against depression at the population-level rather than the individual level

Depression is often seen as a growing public health issue, and this is reflected in a growing number of interventions. This paper discusses the ethical implications of interventions conducted at the population, as opposed to the individual, level. We start by outlining the main ethical considerations of such approaches. Particularly we argue that given the stigma associated with diagnoses and labels of mental illness, including depression, allocation or adoption of such labels greatly effects the lives of sufferers. While long-term social and legal efforts may be made to reduce stigma and discrimination, the potential consequences of such labels must be recognised and greater sensitivity to this must illuminate discussions and practice of diagnosis, intervention and treatment. With this in mind, we proceed to consider several specific large-scale interventions to address depression. These include: webbased interventions; over the counter sales of anti-depressants; school-based interventions and interventions in the young and environmental interventions. We conclude that many types of large-scale intervention are not ethically unproblematic and that there is a significant difference between treating people who seek help, and searching for people whom we think need help. Consequently, in the current climate of prevailing attitudes towards mental health generally, and depression in particular, the use of population-level interventions raises serious ethical considerations that cannot be ignored

Multi-elemental speciation analysis of barley genotypes diering in tolerance to cadmium toxicity using SEC-ICP-MS and ESI-TOF-MS

Plants respond to Cd exposure by synthesizing heavy-metal-binding oligopeptides, called phytochelatins (PCs). These peptides reduce the activity of Cd2+ ions in the plant tissues by forming Cd chelates. The main objective of the present work was to develop an analytical technique, which allowed identication of the most prominent Cd species in plant tissue by SEC-ICP-MS and ESI-TOF-MS. An integrated part of the method development was to test the hypothesis that dierential Cd tolerance between two barley genotypes was linked to dierences in Cd speciation. Only one fraction of Cd species, ranging from 7001800 Da, was detected in the shoots of both genotypes. In the roots, two additional fractions ranging from 29004600 and 670015 000 Da were found. The Cd-rich SEC fractions were heart-cut, de-salted and demetallized using reversed-phase chromatography (RPC), followed by ESI-MS-TOF to identify the ligands. Three dierent families of PCs, viz. (gGlu-Cys)n-Gly (PCn), (gGlu-Cys)n-Ser (iso-PCn) and Cys-(gGlu-Cys)n-Gly (des-gGlu-PCn), the last lacking the N-terminal amino acid, were identied. The PCs induced by Cd toxicity also bound several essential trace elements in plants, including Zn, Cu, and Ni, whereas no Mn species were detected. Zn, Cu and Ni-species were distributed between the 7001800 Da and 670015 000 Da fractions, whereas only Cd species were found in the 29004600 Da fraction dominated by PC3 ligands. Although the total tissue concentration of Cd was similar for the two species, the tolerant barley genotype synthesized signicantly more CdPC3 species with a high Cd specicity than the intolerant genotype, clearly indicating a correlation between Cd tolerance and the CdPC speciation

Characterization of Microfibrillar-associated Protein 4 (MFAP4) as a Tropoelastin- and Fibrillin-binding Protein Involved in Elastic Fiber Formation

MFAP4 (microfibrillar-associated protein 4) is an extracellular glycoprotein found in elastic fibers without a clearly defined role in elastic fiber assembly. In the present study, we characterized molecular interactions between MFAP4 and elastic fiber components. We established that MFAP4 primarily assembles into trimeric and hexameric structures of homodimers. Binding analysis revealed that MFAP4 specifically binds tropoelastin and fibrillin-1 and -2, as well as the elastin cross-linking amino acid desmosine, and that it co-localizes with fibrillin-1-positive fibers in vivo. Site-directed mutagenesis disclosed residues Phe(241) and Ser(203) in MFAP4 as being crucial for type I collagen, elastin, and tropoelastin binding. Furthermore, we found that MFAP4 actively promotes tropoelastin self-assembly. In conclusion, our data identify MFAP4 as a new ligand of microfibrils and tropoelastin involved in proper elastic fiber organization

Statistical properties of hybrid estimators proposed for GEDI – NASA’s Global Ecosystem Dynamics Investigation

NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission will collect waveform lidar data at a dense sample of ∼25 m footprints along ground tracks paralleling the orbit of the International Space Station (ISS). GEDI’s primary science deliverable will be a 1 km grid of estimated mean aboveground biomass density (Mg ha ^−1 ), covering the latitudes overflown by ISS (51.6 °S to 51.6 °N). One option for using the sample of waveforms contained within an individual grid cell to produce an estimate for that cell is hybrid inference, which explicitly incorporates both sampling design and model parameter covariance into estimates of variance around the population mean. We explored statistical properties of hybrid estimators applied in the context of GEDI, using simulations calibrated with lidar and field data from six diverse sites across the United States. We found hybrid estimators of mean biomass to be unbiased and the corresponding estimators of variance appeared to be asymptotically unbiased, with under-estimation of variance by approximately 20% when data from only two clusters (footprint tracks) were available. In our study areas, sampling error contributed more to overall estimates of variance than variability due to the model, and it was the design-based component of the variance that was the source of the variance estimator bias at small sample sizes. These results highlight the importance of maximizing GEDI’s sample size in making precise biomass estimates. Given a set of assumptions discussed here, hybrid inference provides a viable framework for estimating biomass at the scale of a 1 km grid cell while formally accounting for both variability due to the model and sampling error