Collaborative Assessment and Management of Suicidality (CAMS) compared to enhanced treatment as usual (E-TAU) for suicidal patients in an inpatient setting: study protocol for a randomized controlled trial

Santel M, Beblo T, Neuner F, et al. Collaborative Assessment and Management of Suicidality (CAMS) compared to enhanced treatment as usual (E-TAU) for suicidal patients in an inpatient setting: study protocol for a randomized controlled trial. BMC Psychiatry. 2020;20(1): 183.Background The Collaborative Assessment and Management of Suicidality (CAMS) is a therapeutic framework that has been shown to reduce suicidal ideation and overall symptom distress. CAMS has not been previously evaluated in a standard acute inpatient mental health care setting with only short treatment times for suicidal patients. In this randomized controlled trial (RCT) we are investigating whether CAMS is more effective than Enhanced-Treatment as Usual (E-TAU) in reducing suicidal thoughts as primary outcome variable. We are also investigating depressive symptoms, general symptom relief, and the quality of the therapeutic alliance as secondary outcomes. Methods/Design This RCT is designed as a single-center, two-armed, parallel group observer-blinded clinical effectiveness investigation. We are recruiting and randomizing 60 participants with different diagnoses, who are admitted as inpatients because of acute suicidal thoughts or behaviors into the Clinic for Psychiatry and Psychotherapy, Ev. Hospital Bethel in Bielefeld, Germany. The duration of treatment will vary depending on patients’ needs and clinical assessments ranging between 10 and 40 days. Patients are assessed four times, at admission, discharge, 1 month, and 5 months post-discharge. The primary outcome measure is the Beck Scale for Suicide Ideation. Other outcome measures are administered as assessment timepoints including severity of psychiatric symptoms, depression, reasons for living, and therapeutic relationship. Discussion This effectiveness study is being conducted on an acute ward in a psychiatric clinic where patients have multiple problems and diagnoses. Treatment is somewhat limited, and therapists have a large caseloads. The results of this study can thus be generalizable to a typical inpatient psychiatric hospital settings

Impact of therapist change after initial contact and traumatic burden on dropout in a naturalistic sample of inpatients with borderline pathology receiving dialectical behavior therapy

Steuwe C, Berg M, Driessen M, Beblo T. Impact of therapist change after initial contact and traumatic burden on dropout in a naturalistic sample of inpatients with borderline pathology receiving dialectical behavior therapy. Borderline Personality Disorder and Emotion Dysregulation. 2017;4(14): 14.Background This study focused on the predictors of therapy dropout in a naturalistic sample of patients with borderline pathology receiving dialectical behavior therapy (DBT) in an inpatient setting. We assumed that the change of the therapist between DBT-briefing and start of DBT-treatment as well as comorbid posttraumatic stress disorder (PTSD) and childhood trauma history were associated with elevated dropout. Methods Eighty-nine participants with borderline pathology (≥ 3 borderline personality disorder criteria) receiving an inpatient DBT program completed a quality assurance questionnaire set assessing demographic information and pretreatment psychopathology during the days of their inpatient stay. Beyond that, changes of therapists were documented. The predictor analyses were investigated with generalized estimating equations. Results The dropout rate was 24.7%. A change of therapist between DBT-briefing and treatment as well as high childhood emotional abuse was associated with premature termination of treatment. Higher values of physical neglect during childhood were associated with a protective effect on treatment dropout. Surprisingly, this was also true for comorbid PTSD. Conclusions This study supports the importance of therapy process variables as predictors of therapy dropout in borderline pathology. A change of therapist between DBT-briefing and treatment was associated with an increased vulnerability for dropping out of treatment and should therefore be avoided if possible. Against our hypotheses, a comorbid PTSD was even protective with regard to DBT dropout. Therefore, this severely suffering patient group should not be rejected from treatment assuming them to be too unstable for psychotherapy. However, results need to be replicated. ClinicalTrials.gov Identifier: NCT03018639, retrospectively registered on January 9, 2017

Unifying candidate gene and GWAS Approaches in Asthma.

The first genome wide association study (GWAS) for childhood asthma identified a novel major susceptibility locus on chromosome 17q21 harboring the ORMDL3 gene, but the role of previous asthma candidate genes was not specifically analyzed in this GWAS. We systematically identified 89 SNPs in 14 candidate genes previously associated with asthma in >3 independent study populations. We re-genotyped 39 SNPs in these genes not covered by GWAS performed in 703 asthmatics and 658 reference children. Genotyping data were compared to imputation data derived from Illumina HumanHap300 chip genotyping. Results were combined to analyze 566 SNPs covering all 14 candidate gene loci. Genotyped polymorphisms in ADAM33, GSTP1 and VDR showed effects with p-values <0.0035 (corrected for multiple testing). Combining genotyping and imputation, polymorphisms in DPP10, EDN1, IL12B, IL13, IL4, IL4R and TNF showed associations at a significance level between p = 0.05 and p = 0.0035. These data indicate that (a) GWAS coverage is insufficient for many asthma candidate genes, (b) imputation based on these data is reliable but incomplete, and (c) SNPs in three previously identified asthma candidate genes replicate in our GWAS population with significance after correction for multiple testing in 14 genes

Effectiveness and feasibility of Narrative Exposure Therapy (NET) in patients with borderline personality disorder and posttraumatic stress disorder – a pilot study

Steuwe C, Rullkötter N, Ertl V, et al. Effectiveness and feasibility of Narrative Exposure Therapy (NET) in patients with borderline personality disorder and posttraumatic stress disorder – a pilot study. BMC Psychiatry. 2016;16(1): 254.Background This pilot study focused on the feasibility and potential effectiveness of a protocol based on Narrative Exposure Therapy (NET) that was integrated into a standard inpatient program to treat patients with comorbid Borderline Personality Disorder (BPD) and Posttraumatic Stress Disorder (PTSD). Methods Eleven patients (1 male, 10 female) without previous stabilization periods or the absence of intentional self-injury received NET during a ten-week inpatient program. Patients were assessed again at post-treatment and a 12-month follow-up. Results Drop-out rates during treatment were low, with 90.9 % completing NET. Furthermore, acceptance of NET was high, with only one patient rejecting treatment. The program was safe because it did not lead to aggravations in symptom severity at either the post-treatment or 12-month follow-up. Additionally, the rate of self-harming behaviors throughout the treatment phase was low (18.2 %). In fact, treatment was associated with positive effects on PTSD and BPD symptom severity as well as secondary outcome measures, including depression, dissociation and quality of life. Conclusions The present study found that NET is feasible and safe in an inpatient setting for treating highly burdened patients with BPD and PTSD. There is also evidence for the potential effectiveness of NET in this highly burdened population

Twice‐weekly ixazomib in combination with lenalidomide‐dexamethasone in patients with newly diagnosed multiple myeloma

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144661/1/bjh15394.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144661/2/bjh15394_am.pd

Künstliche Intelligenz in Mittelstädten - Mittendrin oder außen vor?

Künstliche Intelligenz in Mittelstädten – mittendrin oder außen vor? 1/2020 – 03/2021 Mittelstädte stehen nur selten im Fokus beim Einsatz von Künstlicher Intelligenz. Dies ist das zentrale Ergebnis der Studie aus dem Jahr 2020. Das Ziel bestand darin, Hemmnisse der digitalen Transformation in Politik und Verwaltung zu identifizieren, technologische Varianten zu untersuchen und gute Beispiele aus Mittelstädten vorzustellen. Autor*innen des Fachgebiets Stadtsoziologie der TU Kaiserslautern, des Deutschen Forschungszentrums für Künstliche Intelligenz (DFKI) und des Fraunhofer-Institut für Experimentelles Software Engineering (IESE) stellen in der Studie Interviews mit Vertreter*innen aus Kommunen und Anbieter*innn von KI-Verwaltungsdienstleistungen. Die Studie wurde von der Entwicklungsagentur Rheinland-Pfalz gefördert und hat folgende Fragen beantwortet: • Welche Kompetenzen und Stellen sind in den Rathäusern von Mittelstädten verfügbar, um die digitale Transformation zu gestalten? Wie sind die Prozesse gestaltet? Wie sehen Personalstrategien für die Digitalisierung der Verwaltung aus? • Gibt es nationale und internationale kreative Beispiele von KI-Nutzungsmöglichkeiten und Vorgehensweisen? • Gibt es Unterschiede in der Nachfrage nach bestimmten Diensten, Beratungen, Kompetenzen, Infrastrukturen zwischen Großstädten und Mittelstädten, insbesondere kleinen Mittelstädten? • Kann das Land eine relevante Rolle zur Unterstützung von Städten und Gemeinden einnehmen (zum Beispiel, wenn die vom Bund angekündigte Unterstützung zur Umsetzung des Onlinezugangsgesetzes ausbleibt)? Mittelstädte sind neben den Kleinstädten die typischen Städte des bundesdeutschen Siedlungssystems. Es gibt dabei lediglich eine quantitative Bestimmung von Mittelstädten, die eine Spanne von 20.000 bis 100.000 Einwohner umfasst. Ein Drittel der deutschen Bevölkerung lebt in Mittelstädten, in Rheinland-Pfalz sind es rund 720.000 Personen

Using [Ne V]/[Ne III] to Understand the Nature of Extreme-Ionization Galaxies

Spectroscopic studies of extreme-ionization galaxies (EIGs) are critical to our understanding of exotic systems throughout cosmic time. These EIGs exhibit spectral features requiring >54.42 eV photons: the energy needed to fully ionize helium into He2+ and emit He II recombination lines. They are likely key contributors to reionization, and they can also probe exotic stellar populations or accretion onto massive black holes. To facilitate the use of EIGs as probes of high ionization, we focus on ratios constructed from strong rest-frame UV/optical emission lines, specifically [O III] 5008, H-beta, [Ne III] 3870, [O II] 3727,3729, and [Ne V] 3427. These lines probe the relative intensity at energies of 35.12, 13.62, 40.96, 13.62 eV, and 97.12, respectively, covering a wider range of ionization than traced by other common rest-frame UV/optical techniques. We use ratios of these lines ([Ne V]/[Ne III] = Ne53 and [Ne III]/[O II]), which are closely separated in wavelength, and mitigates effects of dust attenuation and uncertainties in flux calibration. We make predictions from photoionization models constructed from Cloudy that use a broad range of stellar populations and black hole accretion models to explore the sensitivity of these line ratios to changes in the ionizing spectrum. We compare our models to observations from the Hubble Space Telescope and James Webb Space Telescope of galaxies with strong high-ionization emission lines at z ~ 0, z ~ 2, and z ~ 7. We show that the Ne53 ratio can separate galaxies with ionization from 'normal' stellar populations from those with AGN and even 'exotic' Population III models. We introduce new selection methods to identify galaxies with photoionization driven by Population III stars or intermediate-mass black hole accretion disks that could be identified in upcoming high-redshift spectroscopic surveys.Comment: 16 pages, 5 figures, 1 table. Accepted in Ap

Resting-state EEG reveals four subphenotypes of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is a devastating disease characterized primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. Amyotrophic lateral sclerosis is both clinically and biologically heterogeneous. Subgrouping is currently undertaken using clinical parameters, such as site of symptom onset (bulbar or spinal), burden of disease (based on the modified El Escorial Research Criteria) and genomics in those with familial disease. However, with the exception of genomics, these subcategories do not take into account underlying disease pathobiology, and are not fully predictive of disease course or prognosis. Recently, we have shown that resting-state EEG can reliably and quantitatively capture abnormal patterns of motor and cognitive network disruption in amyotrophic lateral sclerosis. These network disruptions have been identified across multiple frequency bands, and using measures of neural activity (spectral power) and connectivity (comodulation of activity by amplitude envelope correlation and synchrony by imaginary coherence) on source-localized brain oscillations from high-density EEG. Using data-driven methods (similarity network fusion and spectral clustering), we have now undertaken a clustering analysis to identify disease subphenotypes and to determine whether different patterns of disruption are predictive of disease outcome. We show that amyotrophic lateral sclerosis patients (n = 95) can be subgrouped into four phenotypes with distinct neurophysiological profiles. These clusters are characterized by varying degrees of disruption in the somatomotor (α-band synchrony), frontotemporal (β-band neural activity and γl-band synchrony) and frontoparietal (γl-band comodulation) networks, which reliably correlate with distinct clinical profiles and different disease trajectories. Using an in-depth stability analysis, we show that these clusters are statistically reproducible and robust, remain stable after reassessment using a follow-up EEG session, and continue to predict the clinical trajectory and disease outcome. Our data demonstrate that novel phenotyping using neuroelectric signal analysis can distinguish disease subtypes based exclusively on different patterns of network disturbances. These patterns may reflect underlying disease neurobiology. The identification of amyotrophic lateral sclerosis subtypes based on profiles of differential impairment in neuronal networks has clear potential in future stratification for clinical trials. Advanced network profiling in amyotrophic lateral sclerosis can also underpin new therapeutic strategies that are based on principles of neurobiology and designed to modulate network disruption

Variation in the Male Pheromones and Mating Success of Wild Caught Drosophila melanogaster

Drosophila melanogaster males express two primary cuticular hydrocarbons (male-predominant hydrocarbons). These act as sex pheromones by influencing female receptivity to mating. The relative quantities of these hydrocarbons vary widely among natural populations and can contribute to variation in mating success. We tested four isofemale lines collected from a wild population to assess the effect of intrapopulation variation in male-predominant hydrocarbons on mating success. The receptivity of laboratory females to males of the four wild-caught lines varied significantly, but not consistently in the direction predicted by variation in male-predominant hydrocarbons. Receptivity of the wild-caught females to laboratory males also varied significantly, but females from lines with male-predominant hydrocarbon profiles closer to a more cosmopolitan one did not show a correspondingly strong mating bias toward a cosmopolitan male. Among wild-caught lines, the male-specific ejaculatory bulb lipid, cis-vaccenyl acetate, varied more than two-fold, but was not associated with variation in male mating success. We observed a strong inverse relationship between the receptivity of wild-caught females and the mating success of males from their own lines, when tested with laboratory flies of the opposite sex

Life course trajectories of alcohol consumption in the United Kingdom using longitudinal data from nine cohort studies.

Background Alcohol consumption patterns change across life and this is not fully captured in cross-sectional series data. Analysis of longitudinal data, with repeat alcohol measures, is necessary to reveal changes within the same individuals as they age. Such data are scarce and few studies are able to capture multiple decades of the life course. Therefore, we examined alcohol consumption trajectories, reporting both average weekly volume and frequency, using data from cohorts with repeated measures that cover different and overlapping periods of life. Methods Data were from nine UK-based prospective cohorts with at least three repeated alcohol consumption measures on individuals (combined sample size of 59,397 with 174,666 alcohol observations), with data spanning from adolescence to very old age (90 years plus). Information on volume and frequency of drinking were harmonised across the cohorts. Predicted volume of alcohol by age was estimated using random effect multilevel models fitted to each cohort. Quadratic and cubic polynomial terms were used to describe non-linear age trajectories. Changes in drinking frequency by age were calculated from observed data within each cohort and then smoothed using locally weighted scatterplot smoothing. Models were fitted for men and women separately. Results We found that, for men, mean consumption rose sharply during adolescence, peaked at around 25 years at 20 units per week, and then declined and plateaued during mid-life, before declining from around 60 years. A similar trajectory was seen for women, but with lower overall consumption (peak of around 7 to 8 units per week). Frequent drinking (daily or most days of the week) became more common during mid to older age, most notably among men, reaching above 50% of men. Conclusions This is the first attempt to synthesise longitudinal data on alcohol consumption from several overlapping cohorts to represent the entire life course and illustrates the importance of recognising that this behaviour is dynamic. The aetiological findings from epidemiological studies using just one exposure measure of alcohol, as is typically done, should be treated with caution. Having a better understanding of how drinking changes with age may help design intervention strategies