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Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).
Aims
We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.
Method
Based on individual genotypes from case–control cohorts of BPD and MDD shared through the Psychiatric Genomics Consortium, we compile case–case–control cohorts, applying a careful quality control procedure. In a resulting cohort of 51 149 individuals (15 532 BPD patients, 12 920 MDD patients and 22 697 controls), we perform a variety of GWAS and PRS analyses.
Results
Although our GWAS is not well powered to identify genome-wide significant loci, we find significant chip heritability and demonstrate the ability of the resulting PRS to distinguish BPD from MDD, including BPD cases with depressive onset (BPD-D). We replicate our PRS findings in an independent Danish cohort (iPSYCH 2015, N = 25 966). We observe strong genetic correlation between our case–case GWAS and that of case–control BPD.
Conclusions
We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BPD and, importantly, BPD-D from MDD.
Loneliness has become a major public health issue of the recent decades due to its severe impact on health and mortality. Little is known about the relation between loneliness and social anxiety. This study aimed (1) to explore levels of loneliness and social anxiety in the general population, and (2) to assess whether and how loneliness affects symptoms of social anxiety and vice versa over a period of five years.
Methods
The study combined data from the baseline assessment and the five-year follow-up of the population-based Gutenberg Health Study. Data of N = 15 010 participants at baseline (Mage = 55.01, s.d.age = 11.10) were analyzed. Multiple regression analyses with loneliness and symptoms of social anxiety at follow-up including sociodemographic, physical illnesses, and mental health indicators at baseline were used to test relevant covariates. Effects of loneliness on symptoms of social anxiety over five years and vice versa were analyzed by autoregressive cross-lagged structural equation models.
Results
At baseline, 1076 participants (7.41%) showed symptoms of social anxiety and 1537 (10.48%) participants reported feelings of loneliness. Controlling for relevant covariates, symptoms of social anxiety had a small significant effect on loneliness five years later (standardized estimate of 0.164, p < 0.001). Vice versa, there was no significant effect of loneliness on symptoms of social anxiety taking relevant covariates into account.
Conclusions
Findings provided evidence that symptoms of social anxiety are predictive for loneliness. Thus, prevention and intervention efforts for loneliness need to address symptoms of social anxiety.
The Personalized Advantage Index (PAI) shows promise as a method for identifying the most effective treatment for individual patients. Previous studies have demonstrated its utility in retrospective evaluations across various settings. In this study, we explored the effect of different methodological choices in predictive modelling underlying the PAI.
Methods
Our approach involved a two-step procedure. First, we conducted a review of prior studies utilizing the PAI, evaluating each study using the Prediction model study Risk Of Bias Assessment Tool (PROBAST). We specifically assessed whether the studies adhered to two standards of predictive modeling: refraining from using leave-one-out cross-validation (LOO CV) and preventing data leakage. Second, we examined the impact of deviating from these methodological standards in real data. We employed both a traditional approach violating these standards and an advanced approach implementing them in two large-scale datasets, PANIC-net (n = 261) and Protect-AD (n = 614).
Results
The PROBAST-rating revealed a substantial risk of bias across studies, primarily due to inappropriate methodological choices. Most studies did not adhere to the examined prediction modeling standards, employing LOO CV and allowing data leakage. The comparison between the traditional and advanced approach revealed that ignoring these standards could systematically overestimate the utility of the PAI.
Conclusion
Our study cautions that violating standards in predictive modeling may strongly influence the evaluation of the PAI's utility, possibly leading to false positive results. To support an unbiased evaluation, crucial for potential clinical application, we provide a low-bias, openly accessible, and meticulously annotated script implementing the PAI.
Fast and efficient identification is critical for reducing the likelihood of weed establishment and for appropriately managing established weeds. Traditional identification tools require either knowledge of technical morphological terminology or time-consuming image matching by the user. In recent years, deep learning computer vision models have become mature enough to enable automatic identification. The major remaining bottlenecks are the availability of a sufficient number of high-quality, reliably identified training images and the user-friendly, mobile operationalization of the technology. Here, we present the first weed identification and reporting app and website for all of Australia. It includes an image classification model covering more than 400 species of weeds and some Australian native relatives, with a focus on emerging biosecurity threats and spreading weeds that can still be eradicated or contained. It links the user to additional information provided by state and territory governments, flags species that are locally reportable or notifiable, and allows the creation of observation records in a central database. State and local weed officers can create notification profiles to be alerted of relevant weed observations in their area. We discuss the background of the WeedScan project, the approach taken in design and software development, the photo library used for training the WeedScan image classifier, the model itself and its accuracy, and technical challenges and how these were overcome.
In a sample of 61 free-living, postparasitic male Euchordodes nigromaculatus collected from a mountain stream in New Zealand, we found that only large males are found in areas of high current velocity. Thirty-five of the 61 males still contained gametes; these worms were found in wider, deeper, and slower-flowing parts of the stream relative to worms that had released their gametes. These results suggest that the physical characteristics of the immediate microhabitat of male worms can determine their probability of mating.
In the field of experimental fluid dynamics, the direct measurement of vorticity remains a challenge, even though it plays a crucial role in understanding turbulent flows. The present study explores the influence of the rotation of nanoparticles on their luminescence anisotropy as a potential novel measurement method. This relation opens a new field of flow diagnostics, based on the measurement of polarized intensity components. Potentially, the method allows for the direct measurement of the vorticity. For this, the canonical flow in this study is a turbulent round jet at ${{Re}} = {12\,000}$ and 14 400. It is confirmed that the flow regime has an influence on the luminescence anisotropy. Using a model of such deterministic rotations according to another work by the authors (Schmidt & Rösgen, Phys. Rev. Res., vol. 5, no. 3, 2023, 033006), the magnitude of the vorticity components is computed, since the presented set-up is limited to sensing the magnitude of these quantities. The computed components indicate the self-similarity of the vorticity magnitude. A large-eddy simulation is conducted for comparison with the experiments, demonstrating good agreement.
For more than 2 years, coronavirus disease (COVID-19) has forced worldwide health care systems to adapt their daily practice. These adaptations add to the already stressful demands of providing timely medical care in an overcrowded health care system. Specifically, the COVID-19 pandemic added stress to an already overwhelmed emergency and critical care health care workers (HCWs) on the front lines during the first wave of the pandemic.
This study assessed comparative subjective and objective stress among frontline HCWs using a visual analog scale and biometric data, specifically heart rate variability (HRV).
Methods:
This is a prospective, observational study using surveys and heart rate monitoring among HCWs who work in 3 frontline health care units (emergency department, mobile intensive care unit, and intensive care unit) in the University Hospital of Clermont-Ferrand, France. Two sessions were performed: 1 during the first wave of the pandemic (April 10 to May 10, 2020) and 1 after the first wave of the pandemic (June 10 to July 15, 2020).
The primary outcome is the difference in stress levels between the 2 time points. Secondary objectives were the impact of overcrowding, sociodemographics, and other variables on stress levels. We also assessed the correlation between subjective and objective stress levels.
Results:
Among 199 HCWs, 98 participated in biometric monitoring, 84 had biometric and survey data, and 12 with only biometric data. Subjective stress was higher during the second time point compared to the first (4.39 ± 2.11 vs 3.16 ± 2.34, P = 0.23). There were higher objective stress levels with a decrease in HRV between the first and the second time points. Furthermore, we found higher patient volumes as a source of stress during the second time point. We did not find any significant correlation between subjective and objective stress levels.
Conclusion:
HCWs had higher stress levels between the 2 waves of the pandemic. Overcrowding in the emergency department is associated with higher stress levels. We did not find any correlation between subjective and objective stress among intensive care and emergency HCWs during the first wave of the pandemic.
Due to an expanding number of mechatronic functionalities in modern technical products, the proportion of software and electronic components is also increasing. As a result, the products are developed by different engineering domains in complex development processes. To handle the growing complexity, Systems Engineering (SE) is increasingly important for development organizations of enterprises. System Engineering (SE) is understood as an approach to network the individual engineering domains and shall lead to a collaborative development of complex systems. Model-Based System Engineering (MBSE) expands SE by using common models and software tools to describe und visualize the systems. However, MBSE is not widely established in enterprises today. On the one hand, the introduction requires a distinct and consistent system understanding and collaborative way of working. On the other hand, the application of the existing tools requires extensive tool competencies due to many possible functions and features. Therefore, this paper presents a concept and a software based tool for a lean implementation of SE/MBSE to support the collaborative development of complex technical systems in small and medium-sized enterprieses.
Artificial intelligence (AI) is seen as one of the major disrupting forces in the future healthcare system. However, assessment of the value of these new technologies is still unclear and no agreed international HTA-based guideline exists. Therefore, a Model for ASsessing the value of AI (MAS-AI) in medical imaging was developed by a multidisciplinary group of experts and patient representatives.
Methods
The MAS-AI guideline is based on four steps. First a literature review of existing guides, evaluations, and assessments of the value of AI in the field of medical imaging (5,890 studies were assessed with 86 studies included in the scoping review). Next, interviews with leading researchers in AI in Denmark. The third step was two workshops where decision-makers, patient organizations and researchers discussed crucial topics when evaluating AI. Between workshops, the multidisciplinary team revised the model according to comments from workshop-participants. Last step is a validation workshop in Canada.
Results
The MAS-AI guideline has three parts. There are two steps covering nine domains and then advises for the evaluation process. Step 1 contains a description of patients, how the AI-model was developed, and initial ethical and legal considerations. Finishing the four domains in Step 1 is a prerequisite for moving to step 2. In step 2, a multidisciplinary assessment of outcomes of the AI-application is done for the five remaining domains: safety, clinical aspects, economics, organizational aspects and patient aspects. The last part, is five advices to facilitate a good evaluation process.
Conclusions
We have developed an HTA based framework to support the prospective phase while introducing novel AI technologies into healthcare in medical imaging. MAS-AI can assist HTA organizations (and companies) in selecting the relevant domains and outcome measures in the assessment of AI applications. It is important to ensure uniform and valid decisions regarding the adoption of AI technology with a structured process and tool. MAS-AI can help support these decisions and provide greater transparency for all parties involved.
Palliative sedation (PS) is an intrusive measure to relieve patients at the end of their life from otherwise untreatable symptoms. Intensive discussion of the advantages and limitations of palliative care with the patients and their relatives should precede the initiation of PS since PS is terminated by the patient’s death in most cases. Drugs for PS are usually administered intravenously. Midazolam is widely used, either alone or in combination with other substances. PS can be conducted in both inpatient and outpatient settings; however, a quality analysis comparing both modalities was missing so far.
Patients and methods
This prospective observational study collected data from patients undergoing PS inpatient at the palliative care unit (PCU, n = 26) or outpatient at a hospice (n = 2) or at home (specialized outpatient palliative care [SAPV], n = 31) between July 2017 and June 2018. Demographical data, indications for PS, and drug protocols were analyzed. The depth of sedation according to the Richmond Agitation Sedation Scale (RASS) and the degree of satisfaction of staff members and patient’s relatives were included as parameters for quality assessment.
Results
Patients undergoing PS at the PCU were slightly younger compared to outpatients (hospice and SAPV combined). Most patients suffered from malignant diseases, and midazolam was the backbone of sedation for inpatients and outpatients. The median depth of sedation was between +1 and −3 according to the RASS with a trend to deeper sedation prior to death. The median degree of satisfaction was “good,” scored by staff members and by patient’s relatives. Significant differences between inpatients and outpatients were not seen in protocols, depth of sedation, and degree of satisfaction.
Conclusion
The data support the thesis that PS is possible for inpatients and outpatients with comparable results. For choosing the best place for PS, other aspects such as patient’s and relative’s wishes, stress, and medical reasons should be considered.
Artificial intelligence (AI) is seen as a major disrupting force in the future healthcare system. However, the assessment of the value of AI technologies is still unclear. Therefore, a multidisciplinary group of experts and patients developed a Model for ASsessing the value of AI (MAS-AI) in medical imaging. Medical imaging is chosen due to the maturity of AI in this area, ensuring a robust evidence-based model.
Methods
MAS-AI was developed in three phases. First, a literature review of existing guides, evaluations, and assessments of the value of AI in the field of medical imaging. Next, we interviewed leading researchers in AI in Denmark. The third phase consisted of two workshops where decision makers, patient organizations, and researchers discussed crucial topics for evaluating AI. The multidisciplinary team revised the model between workshops according to comments.
Results
The MAS-AI guideline consists of two steps covering nine domains and five process factors supporting the assessment. Step 1 contains a description of patients, how the AI model was developed, and initial ethical and legal considerations. In step 2, a multidisciplinary assessment of outcomes of the AI application is done for the five remaining domains: safety, clinical aspects, economics, organizational aspects, and patient aspects.
Conclusions
We have developed an health technology assessment-based framework to support the introduction of AI technologies into healthcare in medical imaging. It is essential to ensure informed and valid decisions regarding the adoption of AI with a structured process and tool. MAS-AI can help support decision making and provide greater transparency for all parties.
Developmental plasticity, where traits change state in response to environmental cues, is well studied in modern populations. It is also suspected to play a role in macroevolutionary dynamics, but due to a lack of long-term records, the frequency of plasticity-led evolution in deep time remains unknown. Populations are dynamic entities, yet their representation in the fossil record is a static snapshot of often isolated individuals. Here, we apply for the first time contemporary integral projection models (IPMs) to fossil data to link individual development with expected population variation. IPMs describe the effects of individual growth in discrete steps on long-term population dynamics. We parameterize the models using modern and fossil data of the planktonic foraminifer Trilobatus sacculifer. Foraminifera grow by adding chambers in discrete stages and die at reproduction, making them excellent case studies for IPMs. Our results predict that somatic growth rates have almost twice as much influence on population dynamics than survival and more than eight times more influence than reproduction, suggesting that selection would primarily target somatic growth as the major determinant of fitness. As numerous paleobiological systems record growth rate increments in single genetic individuals and imaging technologies are increasingly available, our results open up the possibility of evidence-based inference of developmental plasticity spanning macroevolutionary dynamics. Given the centrality of ecology in paleobiological thinking, our model is one approach to help bridge eco-evolutionary scales while directing attention toward the most relevant life-history traits to measure.
Cognitive Bias Modification for paranoia (CBM-pa) is a novel, theory-driven psychological intervention targeting the biased interpretation of emotional ambiguity associated with paranoia. Study objectives were (i) test the intervention's feasibility, (ii) provide effect size estimates, (iii) assess dose–response and (iv) select primary outcomes for future trials.
Methods
In a double-blind randomised controlled trial, sixty-three outpatients with clinically significant paranoia were randomised to either CBM-pa or an active control (text reading) between April 2016 and September 2017. Patients received one 40 min session per week for 6 weeks. Assessments were given at baseline, after each interim session, post-treatment, and at 1- and 3-months post-treatment.
Results
A total of 122 patients were screened and 63 were randomised. The recruitment rate was 51.2%, with few dropouts (four out of 63) and follow-up rates were 90.5% (1-month) and 93.7% (3-months). Each session took 30–40 min to complete. There was no statistical evidence of harmful effects of the intervention. Preliminary data were consistent with efficacy of CBM-pa over text-reading control: patients randomised to the intervention, compared to control patients, reported reduced interpretation bias (d = −0.48 to −0.76), improved symptoms of paranoia (d = −0.19 to −0.38), and lower depressed and anxious mood (d = −0.03 to −0.29). The intervention effect was evident after the third session.
Conclusions
CBM-pa is feasible for patients with paranoia. A fully powered randomised control trial is warranted.
We propose that any theory of visual awareness must explain the gradient of different awareness measures over experimental conditions, especially when those measures form double dissociations among each other. Theories meeting this requirement must be specific to the measured facets of awareness, such as motion, contrast, or color. Integrated information theory (IIT) lacks such specificity because it is an underconstrained theory with unspecific predictions.
Case-only longitudinal studies are common in psychiatry. Further, it is assumed that psychiatric ratings and questionnaire results of healthy controls stay stable over foreseeable time ranges. For cognitive tests, improvements over time are expected, but data for more than two administrations are scarce.
Aims
We comprehensively investigated the longitudinal course for trends over time in cognitive and symptom measurements for severe mental disorders. Assessments included the Trail Making Tests, verbal Digit Span tests, Global Assessment of Functioning, Inventory of Depressive Symptomatology, the Positive and Negative Syndrome Scale, and the Young Mania Rating Scale, among others.
Method
Using the data of control individuals (n = 326) from the PsyCourse study who had up to four assessments over 18 months, we modelled the course using linear mixed models or logistic regression. The slopes or odds ratios were estimated and adjusted for age and gender. We also assessed the robustness of these results using a longitudinal non-parametric test in a sensitivity analysis.
Results
Small effects were detected for most cognitive tests, indicating a performance improvement over time (P < 0.05). However, for most of the symptom rating scales and questionnaires, no effects were detected, in line with our initial hypothesis.
Conclusions
The slightly but consistently improved performance in the cognitive tests speaks of a test-unspecific positive trend, while psychiatric ratings and questionnaire results remain stable over the observed period. These detectable improvements need to be considered when interpreting longitudinal courses. We therefore recommend recruiting control participants if cognitive tests are administered.
In the present paper, as part of an interdisciplinary research project (Priority Programme SPP2045), we propose a possible way to design an open access archive for particle-discrete tomographic datasets: the PARROT database (https://parrot.tu-freiberg.de). This archive is the result of a pilot study in the field of particle technology and three use cases are presented for illustrative purposes. Instead of providing a detailed instruction manual, we focus on the methodologies of such an archive. The presented use cases stem from our working group and are intended to demonstrate the advantage of using such an archive with concise and consistent data for potential and ongoing studies. Data and metadata merely serve as examples and need to be adapted for disciplines not concerned here. Since all datasets within the PARROT database and its source code are freely accessible, this study represents a starting point for similar projects.
In this volume, T.C. Schmidt offers a new perspective on the formation of the New Testament by examining it simply as a Greco-Roman 'testament', a legal document of great authority in the ancient world. His work considers previously unexamined parallels between Greco-Roman juristic standards and the authorization of Christianity's holy texts. Recapitulating how Greco-Roman testaments were created and certified, he argues that the book of Revelation possessed many testamentary characteristics that were crucial for lending validity to the New Testament. Even so, Schmidt shows how Revelation fell out of favor amongst most Eastern Christian communities for over a thousand years until commentators rehabilitated its status and reintegrated it into the New Testament. Schmidt uncovers why so many Eastern churches neglected Revelation during this period, and then draws from Greco-Roman legal practice to describe how Eastern commentators successfully argued for Revelation's inclusion in the New Testaments of their Churches.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, with its impact on our way of life, is affecting our experiences and mental health. Notably, individuals with mental disorders have been reported to have a higher risk of contracting SARS-CoV-2. Personality traits could represent an important determinant of preventative health behaviour and, therefore, the risk of contracting the virus.
Aims
We examined overlapping genetic underpinnings between major psychiatric disorders, personality traits and susceptibility to SARS-CoV-2 infection.
Method
Linkage disequilibrium score regression was used to explore the genetic correlations of coronavirus disease 2019 (COVID-19) susceptibility with psychiatric disorders and personality traits based on data from the largest available respective genome-wide association studies (GWAS). In two cohorts (the PsyCourse (n = 1346) and the HeiDE (n = 3266) study), polygenic risk scores were used to analyse if a genetic association between, psychiatric disorders, personality traits and COVID-19 susceptibility exists in individual-level data.
Results
We observed no significant genetic correlations of COVID-19 susceptibility with psychiatric disorders. For personality traits, there was a significant genetic correlation for COVID-19 susceptibility with extraversion (P = 1.47 × 10−5; genetic correlation 0.284). Yet, this was not reflected in individual-level data from the PsyCourse and HeiDE studies.
Conclusions
We identified no significant correlation between genetic risk factors for severe psychiatric disorders and genetic risk for COVID-19 susceptibility. Among the personality traits, extraversion showed evidence for a positive genetic association with COVID-19 susceptibility, in one but not in another setting. Overall, these findings highlight a complex contribution of genetic and non-genetic components in the interaction between COVID-19 susceptibility and personality traits or mental disorders.