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"Berger, T"
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Depression, anxiety, fatigue, and quality of life in a large sample of patients suffering from head and neck cancer in comparison with the general population
Background
Treatment of head and neck cancer (HNC) often leads to visible and severe functional impairments. In addition, patients often suffer from a variety of psychosocial problems, significantly associated with a decreased quality of life. We aimed to compare depression, anxiety, fatigue and quality of life (QoL) between HNC patients and a large sample of the general population in Germany and to examine the impact of sociodemographic, behavioral and clinical factors on these symptoms.
Methods
We assessed data of HNC patients during the aftercare consultation at the Leipzig University Medical Center with a patient reported outcome (PRO) tool named “OncoFunction”. Depression, anxiety, fatigue and QoL were assessed using validated outcome measures including the PHQ-9, the GAD-2, and the EORTC QLQ-C30 questionnaire.
Results
A total of 817 HNC patients were included in our study and compared to a sample of 5018 individuals of the general German population. HNC patients showed significantly higher levels of impairment in all dimensions assessed. Examination of association between depression, anxiety, fatigue and QoL and clinical as well as sociodemographic variables showed significant relationships between occupational status, ECOG-state, body mass index and time since diagnosis.
Conclusions
HNC patients suffer significantly from psychological distress. The used questionnaires are suitable for the use in daily routine practice and can be helpful to increase the detection of depression, anxiety and fatigue and therefore can improve HNC aftercare.
Journal Article
Effects of a transdiagnostic unguided Internet intervention (‘velibra’) for anxiety disorders in primary care: results of a randomized controlled trial
Internet-based cognitive-behavioural treatment (ICBT) for anxiety disorders has shown some promise, but no study has yet examined unguided ICBT in primary care. This randomized controlled trial (RCT) investigated whether a transdiagnostic, unguided ICBT programme for anxiety disorders is effective in primary care settings, after a face-to-face consultation with a physician (MD). We hypothesized that care as usual (CAU) plus unguided ICBT would be superior to CAU in reducing anxiety and related symptoms among patients with social anxiety disorder (SAD), panic disorder with or without agoraphobia (PDA) and/or generalized anxiety disorder (GAD).
Adults (n = 139) with at least one of these anxiety disorders, as reported by their MD and confirmed by a structured diagnostic interview, were randomized. Unguided ICBT was provided by a novel transdiagnostic ICBT programme ('velibra'). Primary outcomes were generic measures, such as anxiety and depression symptom severity, and diagnostic status at post-treatment (9 weeks). Secondary outcomes included anxiety disorder-specific measures, quality of life, treatment adherence, satisfaction, and general psychiatric symptomatology at follow-up (6 months after randomization).
CAU plus unguided ICBT was more effective than CAU at post-treatment, with small to medium between-group effect sizes on primary (Cohen's d = 0.41-0.47) and secondary (Cohen's d = 0.16-0.61) outcomes. Treatment gains were maintained at follow-up. In the treatment group, 28.2% of those with a SAD diagnosis, 38.3% with a PDA diagnosis, and 44.8% with a GAD diagnosis at pretreatment no longer fulfilled diagnostic criteria at post-treatment.
The unguided ICBT intervention examined is effective for anxiety disorders when delivered in primary care.
Journal Article
The Thermosphere Is a Drag: The 2022 Starlink Incident and the Threat of Geomagnetic Storms to Low Earth Orbit Space Operations
On 03 February 2022, SpaceX launched 49 Starlink satellites, 38 of which re‐entered the atmosphere on or about 07 February 2022 due to unexpectedly high atmospheric drag. We use empirical model (NRLMSIS, JB08, and HASDM) outputs as well as solar extreme ultraviolet occultation and high‐fidelity accelerometer data to show that thermospheric density was at least 20%–30% higher at 210 km relative to the 9 days prior to the launch due to consecutive geomagnetic storms related to solar eruptions from NOAA AR12936 on 29 January 2022. We model the orbital altitude and in‐track position of a Starlink‐like satellite in a low‐drag configuration at 200 km during minor (G1) and extreme (G5) geomagnetic storms to show that an extreme storm would have at least a factor of two higher impact, with cumulative in‐track errors on the order of 10,000 km after a 5‐day duration extreme storm. Comparison of the JB08 and NRL MSIS models relative to the HASDM model during modeled historical minor and extreme geomagnetic storms shows that in‐track errors on the order of 100 km per day at 250 km, decreasing to cumulative errors on the order of 1 km per day at 550 km during geomagnetic storms. We conclude that full‐physics, data assimilative, coupled models of the magnetosphere and upper atmosphere, as well as new operational satellite missions providing “nowcasting” data to launch controllers, space traffic coordinators, and satellite operators, are needed to prevent similar—or worse—orbital system impacts during future geomagnetic storms.
Journal Article
Predictors of treatment dropout in self-guided web-based interventions for depression: an ‘individual patient data’ meta-analysis
It is well known that web-based interventions can be effective treatments for depression. However, dropout rates in web-based interventions are typically high, especially in self-guided web-based interventions. Rigorous empirical evidence regarding factors influencing dropout in self-guided web-based interventions is lacking due to small study sample sizes. In this paper we examined predictors of dropout in an individual patient data meta-analysis to gain a better understanding of who may benefit from these interventions.
A comprehensive literature search for all randomized controlled trials (RCTs) of psychotherapy for adults with depression from 2006 to January 2013 was conducted. Next, we approached authors to collect the primary data of the selected studies. Predictors of dropout, such as socio-demographic, clinical, and intervention characteristics were examined.
Data from 2705 participants across ten RCTs of self-guided web-based interventions for depression were analysed. The multivariate analysis indicated that male gender [relative risk (RR) 1.08], lower educational level (primary education, RR 1.26) and co-morbid anxiety symptoms (RR 1.18) significantly increased the risk of dropping out, while for every additional 4 years of age, the risk of dropping out significantly decreased (RR 0.94).
Dropout can be predicted by several variables and is not randomly distributed. This knowledge may inform tailoring of online self-help interventions to prevent dropout in identified groups at risk.
Journal Article
Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires
The adaptive immune system recognizes antigens
an immense array of antigen-binding antibodies and T-cell receptors, the immune repertoire. The interrogation of immune repertoires is of high relevance for understanding the adaptive immune response in disease and infection (e.g., autoimmunity, cancer, HIV). Adaptive immune receptor repertoire sequencing (AIRR-seq) has driven the quantitative and molecular-level profiling of immune repertoires, thereby revealing the high-dimensional complexity of the immune receptor sequence landscape. Several methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity and to understand the dynamics of adaptive immunity. Here, we review the current research on (i) diversity, (ii) clustering and network, (iii) phylogenetic, and (iv) machine learning methods applied to dissect, quantify, and compare the architecture, evolution, and specificity of immune repertoires. We summarize outstanding questions in computational immunology and propose future directions for systems immunology toward coupling AIRR-seq with the computational discovery of immunotherapeutics, vaccines, and immunodiagnostics.
Journal Article
Inhibition of Fatty Acid Binding Proteins Elevates Brain Anandamide Levels and Produces Analgesia
The endocannabinoid anandamide (AEA) is an antinociceptive lipid that is inactivated through cellular uptake and subsequent catabolism by fatty acid amide hydrolase (FAAH). Fatty acid binding proteins (FABPs) are intracellular carriers that deliver AEA and related N-acylethanolamines (NAEs) to FAAH for hydrolysis. The mammalian brain expresses three FABP subtypes: FABP3, FABP5, and FABP7. Recent work from our group has revealed that pharmacological inhibition of FABPs reduces inflammatory pain in mice. The goal of the current work was to explore the effects of FABP inhibition upon nociception in diverse models of pain. We developed inhibitors with differential affinities for FABPs to elucidate the subtype(s) that contributes to the antinociceptive effects of FABP inhibitors. Inhibition of FABPs reduced nociception associated with inflammatory, visceral, and neuropathic pain. The antinociceptive effects of FABP inhibitors mirrored their affinities for FABP5, while binding to FABP3 and FABP7 was not a predictor of in vivo efficacy. The antinociceptive effects of FABP inhibitors were mediated by cannabinoid receptor 1 (CB1) and peroxisome proliferator-activated receptor alpha (PPARα) and FABP inhibition elevated brain levels of AEA, providing the first direct evidence that FABPs regulate brain endocannabinoid tone. These results highlight FABPs as novel targets for the development of analgesic and anti-inflammatory therapeutics.
Journal Article
Individual variation in local interaction rules can explain emergent patterns of spatial organization in wild baboons
Researchers have long noted that individuals occupy consistent spatial positions within animal groups. However, an individual's position depends not only on its own behaviour, but also on the behaviour of others. Theoretical models of collective motion suggest that global patterns of spatial assortment can arise from individual variation in local interaction rules. However, this prediction remains untested. Using high-resolution GPS tracking of members of a wild baboon troop, we identify consistent inter-individual differences in within-group spatial positioning. We then apply an algorithm that identifies what number of conspecific group members best predicts the future location of each individual (we call this the individual's neighbourhood size) while the troop is moving. We find clear variation in the most predictive neighbourhood size, and this variation relates to individuals' propensity to be found near the centre of their group. Using simulations, we show that having different neighbourhood sizes is a simple candidate mechanism capable of linking variation in local individual interaction rules—in this case how many conspecifics an individual interacts with—to global patterns of spatial organization, consistent with the patterns we observe in wild primates and a range of other organisms.
Journal Article
Collaboration patterns in the German political science co-authorship network
Research on social processes in the production of scientific output suggests that the collective research agenda of a discipline is influenced by its structural features, such as \"invisible colleges\" or \"groups of collaborators\" as well as academic \"stars\" that are embedded in, or connect, these research groups. Based on an encompassing dataset that takes into account multiple publication types including journals and chapters in edited volumes, we analyze the complete co-authorship network of all 1,339 researchers in German political science. Through the use of consensus graph clustering techniques and descriptive centrality measures, we identify the ten largest research clusters, their research topics, and the most central researchers who act as bridges and connect these clusters. We also aggregate the findings at the level of research organizations and consider the inter-university co-authorship network. The findings indicate that German political science is structured by multiple overlapping research clusters with a dominance of the subfields of international relations, comparative politics and political sociology. A small set of well-connected universities takes leading roles in these informal research groups.
Journal Article