Search

Abstract Pulmonary fibrosis is characterized by excessive deposition of extracellular matrix (ECM), stiffening of the lung tissue, and impaired gas exchange. Our current understanding of fibrogenesis generally focuses on the individual roles of mechanical and biochemical stimuli in driving disease progression. However, many mechano-chemical pathways are interrelated, so dissecting the interactive effects of mechanical and biochemical signals is an important knowledge gap. To address this gap, we investigated lung fibroblast behavior on static and cyclically strained photopolymerizable hydrogels consisting of different ratios of methacrylated gelatin, methacrylated hyaluronan, and non-methacrylated gelatin to create substrates with tunable stiffness and chemistry, representative of both healthy and fibrotic lung ECM properties. We observed that higher stiffness gels amplified the impact of strain, resulting in distinct differences in expression of MMP1, CTGF, Rho/ROCK, and ECM deposition genes. Substrates with hyaluronan demonstrated a capacity to modulate strain-induced fibrogenic responses, suggesting a buffering effect of hyaluronan on fibrotic disease progression. Overall, our results highlight mechanotransductive changes in gene expression in response to substrate composition, stiffness, and cyclic mechanical strain. Through the controlled study of mechanical and biochemical cues, our findings contribute to a deeper understanding of the pathogenesis of pulmonary fibrosis.

Abstract Background Health-related social problems are common in primary care. Different care models integrating medical and non-medical services in primary care have been tested and established nationally and internationally, such as social prescribing, social work in primary care, health kiosks and integrated primary care centres. The aim of our study was to explore the perspective of general practitioners (GPs) working in Germany on these four care models regarding their meaningfulness and if they would like to use them. Secondary objective was to explore factors influencing this assessment. Methods We conducted a survey of a representative sample of GPs working in Germany. The questionnaire included questions on the assessment of the care models’ meaningfulness and whether the GPs would like to use them. The analysis was carried out descriptively and using linear regression. Results One thousand four hundred thirty-nine GPs took part in the survey. Social prescribing and social work in primary care were rated as the most meaningful concepts. Over 65% of the GPs believed that using at least one of the care models would be beneficial. One in four GPs would even welcome the idea of integrating their practice into an integrated primary care center. Older age and male gender were associated with a more negative assessment of the care models. Conclusions German GPs consider integrating medical and non-medical services in primary care to be meaningful, yet they are somewhat skeptical about its practical implementation in daily practice. However, younger GPs in Germany are significantly more receptive to these models. Trial registration German Register of Clinical Studies (DRKS-ID: DRKS00032585; Registration Date: September 1, 2023).

Abstract Anxiety disorders are the most prevalent mental health conditions. Besides psycho-pharmacotherapy, cognitive behavioral therapy with an exposure-based approach is considered the gold standard. However, not all patients benefit from this approach. Here, we aimed to translate laboratory findings on enhanced fear extinction with repetitive transcranial magnetic stimulation (TMS) to the clinic. In this double-blind, randomized, placebo-controlled clinical trial, 76 participants with acrophobia received an activating intermittent theta burst stimulation (iTBS) targeting the left posterior prefrontal cortex immediately before two virtual reality exposure therapy sessions. Phobic symptoms were assessed at baseline, post-intervention, and a 6-month follow-up. Results revealed a significant reduction in phobic symptoms from baseline to post-assessment and follow-up and confirmed the efficacy of virtual reality exposure therapy as a treatment for specific phobias. Interestingly, no additional effect was observed for active iTBS compared to sham iTBS. Our post-hoc analyses argue for an individualized TMS application. Further research is needed to determine optimal TMS parameters and validate these results in clinical trials, accounting for methodological and inter- and intra-individual variability, as well as alternative therapeutic processes.

Abstract Social prescribing has become a global phenomenon. A Delphi study was recently conducted with 48 social prescribing experts from 26 countries to establish global agreement on the definition of social prescribing. We reflect on the use and utility of the outputs of this work, and where we go from here.

Abstract Recent results have evidenced that spontaneous brain activity signals are organized in bursts with scale free features and long-range spatio-temporal correlations. These observations have stimulated a theoretical interpretation of results inspired in critical phenomena. In particular, relying on maximum entropy arguments, certain aspects of time-averaged experimental neuronal data have been recently described using Ising-like models, allowing the study of neuronal networks under an analogous thermodynamical framework. This method has been so far applied to a variety of experimental datasets, but never to a biologically inspired neuronal network with short and long-term plasticity. Here, we apply for the first time the Maximum Entropy method to an Integrate-and-fire (IF) model that can be tuned at criticality, offering a controlled setting for a systematic study of criticality and finite-size effects in spontaneous neuronal activity, as opposed to experiments. We consider generalized Ising Hamiltonians whose local magnetic fields and interaction parameters are assigned according to the average activity of single neurons and correlation functions between neurons of the IF networks in the critical state. We show that these Hamiltonians exhibit a spin glass phase for low temperatures, having mostly negative intrinsic fields and a bimodal distribution of interaction constants that tends to become unimodal for larger networks. Results evidence that the magnetization and the response functions exhibit the expected singular behavior near the critical point. Furthermore, we also found that networks with higher percentage of inhibitory neurons lead to Ising-like systems with reduced thermal fluctuations. Finally, considering only neuronal pairs associated with the largest correlation functions allows the study of larger system sizes.

ObjectivesTo investigate the diagnostic performance of fetal cardiovascular magnetic resonance imaging (MRI) using Doppler ultrasound (DUS) gating for the evaluation of the standardized five axial views in comparison with fetal echocardiography.MethodsIn this prospective study 29 pregnant women (median: 34.4 weeks of gestation) underwent fetal cardiovascular MRI using DUS gating at 3 Tesla. The standardized five axial views in prenatal screening (fetal abdomen, four-chamber view, left ventricular outflow tract, right ventricular outflow tract, and three-vessel view) were independently assessed and analysed by both fetal MRI and fetal echocardiography on the same day. Image analysis included qualitative assessment and quantitative measurements of cardiovascular structures. MR image quality was assessed using a 4-point scale (from 1 = low to 4 = excellent). Postnatal echocardiography was performed for validation.Results17/28 fetuses (60.7%) had pathological findings [16 congenital heart defect (CHD), one diaphragmatic hernia] in prenatal echocardiography. One fetus was excluded due to severe motion. Overall sensitivity and specificity in detecting fetal cardiac abnormalities was 88% and 100%, respectively, for fetal MRI and 100% and 100% for fetal echocardiography. MR image quality for evaluation of cardiac structures was high with a mean score of 2.8 (±0.8) (score 4: 15.9%, score 3: 53.8%, score 2: 19.3%, score 1: 11%). Quantitative measurements did not differ between fetal cardiovascular MRI and fetal echocardiography (all p > 0.05).ConclusionDiagnostic performance of fetal cardiovascular MRI using DUS gating was comparable to fetal echocardiography. Fetal cardiovascular MRI using DUS gating might be a valuable diagnostic adjunct for the prenatal evaluation of CHD.

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.

Abstract Although highly effective on average, exposure-based treatments do not work equally well for all patients with anxiety disorders. The identification of pre-treatment response-predicting patient characteristics may enable patient stratification. Preliminary research highlights the relevance of inhibitory fronto-limbic networks as such. We aimed to identify pre-treatment neural signatures differing between exposure treatment responders and non-responders in spider phobia and to validate results through rigorous replication. Data of a bi-centric intervention study comprised clinical phenotyping and pre-treatment resting-state functional connectivity (rsFC) data of n = 79 patients with spider phobia (discovery sample) and n = 69 patients (replication sample). RsFC data analyses were accomplished using the Matlab-based CONN-toolbox with harmonized analyses protocols at both sites. Treatment response was defined by a reduction of >30% symptom severity from pre- to post-treatment (Spider Phobia Questionnaire Score, primary outcome). Secondary outcome was defined by a reduction of >50% in a Behavioral Avoidance Test (BAT). Mean within-session fear reduction functioned as a process measure for exposure. Compared to non-responders and pre-treatment, results in the discovery sample seemed to indicate that responders exhibited stronger negative connectivity between frontal and limbic structures and were characterized by heightened connectivity between the amygdala and ventral visual pathway regions. Patients exhibiting high within-session fear reduction showed stronger excitatory connectivity within the prefrontal cortex than patients with low within-session fear reduction. Whereas these results could be replicated by another team using the same data (cross-team replication), cross-site replication of the discovery sample findings in the independent replication sample was unsuccessful. Results seem to support negative fronto-limbic connectivity as promising ingredient to enhance response rates in specific phobia but lack sufficient replication. Further research is needed to obtain a valid basis for clinical decision-making and the development of individually tailored treatment options. Notably, future studies should regularly include replication approaches in their protocols.

Abstract We analyze time-averaged experimental data from in vitro activities of neuronal networks. Through a Pairwise Maximum-Entropy method, we identify through an inverse binary Ising-like model the local fields and interaction couplings which best reproduce the average activities of each neuron as well as the statistical correlations between the activities of each pair of neurons in the system. The specific information about the type of neurons is mainly stored in the local fields, while a symmetric distribution of interaction constants seems generic. Our findings demonstrate that, despite not being directly incorporated into the inference approach, the experimentally observed correlations among groups of three neurons are accurately captured by the derived Ising-like model. Within the context of the thermodynamic analogy inherent to the Ising-like models developed in this study, our findings additionally indicate that these models demonstrate characteristics of second-order phase transitions between ferromagnetic and paramagnetic states at temperatures above, but close to, unity. Considering that the operating temperature utilized in the Maximum-Entropy method is $$T_{o}=1$$ T o = 1 , this observation further expands the thermodynamic conceptual parallelism postulated in this work for the manifestation of criticality in neuronal network behavior.

The exact delay-zero calibration in an attosecond pump-probe experiment is important for the correct interpretation of experimental data. In attosecond transient absorption spectroscopy the determination of the delay-zero exclusively from the experimental results is not straightforward and may introduce significant errors. Here, we report the observation of quarter-laser-cycle (4 ω ) oscillations in a transient absorption experiment in helium using an attosecond pulse train overlapped with a precisely synchronized, moderately strong infrared pulse. We demonstrate how to extract and calibrate the delay-zero with the help of the highly nonlinear 4 ω signal. A comparison with the solution of the time-dependent Schrödinger equation is used to confirm the accuracy and validity of the approach. Moreover, we study the mechanisms behind the quarter-laser-cycle and the better-known half-laser-cycle oscillations as a function of experimental parameters. This investigation yields an indication of the robustness of our delay-zero calibration approach.

Background RNA sequencing (RNA-seq) is a widely used technique in many scientific studies. Given the plethora of models and software packages that have been developed for processing and analyzing RNA-seq datasets, choosing the most appropriate ones is a time-consuming process that requires an in-depth understanding of the data, as well as of the principles and parameters of each tool. In addition, packages designed for individual tasks are developed in different programming languages and have dependencies of various degrees of complexity, which renders their installation and execution challenging for users with limited computational expertise. Workflow languages and execution engines with support for virtualization and encapsulation options such as containers and Conda environments facilitate these tasks considerably. The resulting computational workflows can then be reliably shared with the scientific community, enhancing reusability and the reproducibility of results as individual analysis steps are becoming more transparent and portable. Methods Here we present ZARP, a general purpose RNA-seq analysis workflow that builds on state-of-the-art software in the field to facilitate the analysis of RNA-seq datasets. ZARP is developed in the Snakemake workflow language and can run locally or in a cluster environment, generating extensive reports not only of the data but also of the options utilized. It is built using modern technologies with the ultimate goal to reduce the hands-on time for bioinformaticians and non-expert users and serve as a template for future workflow development. To this end, we also provide ZARP-cli, a dedicated command-line interface that may make running ZARP on an RNA-seq library of interest as easy as executing a single two-word command. Conclusions ZARP is a powerful RNA-seq analysis workflow that is easy to use even for beginners, built using best software development practices, available under a permissive Open Source license and open to contributions by the scientific community.

Abstract Solving multiphysics-based inverse problems for geological carbon storage monitoring can be challenging when multimodal time-lapse data are expensive to collect and costly to simulate numerically. We overcome these challenges by combining computationally cheap learned surrogates with learned constraints. Not only does this combination lead to vastly improved inversions for the important fluid-flow property, permeability, it also provides a natural platform for inverting multimodal data including well measurements and active-source time-lapse seismic data. By adding a learned constraint, we arrive at a computationally feasible inversion approach that remains accurate. This is accomplished by including a trained deep neural network, known as a normalizing flow, which forces the model iterates to remain in-distribution, thereby safeguarding the accuracy of trained Fourier neural operators that act as surrogates for the computationally expensive multiphase flow simulations involving partial differential equation solves. By means of carefully selected experiments, centered around the problem of geological carbon storage, we demonstrate the efficacy of the proposed constrained optimization method on two different data modalities, namely time-lapse well and time-lapse seismic data. While permeability inversions from both these two modalities have their pluses and minuses, their joint inversion benefits from either, yielding valuable superior permeability inversions and CO2 plume predictions near, and far away, from the monitoring wells.

Abstract Background Breast cancer and cervical cancer are among the most common cancers in women in Germany. Early detection examinations such as mammography and the cervical smear test (Pap-test) have been shown to contribute to the reduction in the mortality and/or incidence of these cancers and can be utilised free of charge by women in certain age groups as part of national screening programmes. Analyses show that the use of health services varies regionally, especially when comparing the federal states of the former German Democratic Republic (GDR, Eastern Germany) and the Federal Republic of Germany (FRG, Western Germany). This study investigated to what extent the utilisation of mammography examinations and Pap-tests by women differs in federal states of former GDR and FRG. Methods For this purpose, we analysed data from the nationwide health survey GEDA14/15 conducted by the Robert Koch Institute (RKI) in 2014 and 2015. We calculated weighted proportions and compared attendance between eastern and western German states by a Chi-Square-test. Additionally, we conducted regression analysis to adjust for socio-economic status, living environment and place of birth. Results 2,772 female participants aged 20–34 years were analysed for Pap-test attendance in the last two years and 4,323 female participants aged 50–69 years old were analysed for mammography screening attendance in the last two years. 50–69-year-old women in eastern German states were with 78.3% (95%-CI 75.3%, 81.2%) more likely to attend mammography screening than in western Germany with 73.4% (95%-CI 71.8%, 74.9%). Pap-test uptake was statistically significantly higher in the East of Germany with 83.3% (95%-CI 79.6%, 87.1%) compared to 77.5% (95%-CI 75.8%, 79.3%) in the West of Germany. This relationship was robust to adjusting for socio-economic status, living environment and place of birth. Conclusions Cultural influences and socialization in the GDR might explain the higher utilisation of these cancer screening examinations at least to some extent. This could have many reasons, for example a higher health awareness through education or a possible greater trust in medical structures and the associated higher compliance of women. These hypotheses should be further explored to increase the uptake of screening examinations by women in Germany.

BackgroundIndividuals with anxiety disorders (ADs) often display hypervigilance to threat information, although this response may be less pronounced following psychotherapy. This study aims to investigate the unconscious recognition performance of facial expressions in patients with panic disorder (PD) post-treatment, shedding light on alterations in their emotional processing biases.MethodsPatients with PD (n=34) after (exposure-based) cognitive behavior therapy and healthy controls (n=43) performed a subliminal affective recognition task. Emotional facial expressions (fearful, happy, or mirrored) were displayed for 33 ms and backwardly masked by a neutral face. Participants completed a forced choice task to discriminate the briefly presented facial stimulus and an uncovered condition where only the neutral mask was shown. We conducted a secondary analysis to compare groups based on their four possible response types under the four stimulus conditions and examined the correlation of the false alarm rate for fear responses to non-fearful (happy, mirrored, and uncovered) stimuli with clinical anxiety symptoms.ResultsThe patient group showed a unique selection pattern in response to happy expressions, with significantly more correct “happy” responses compared to controls. Additionally, lower severity of anxiety symptoms after psychotherapy was associated with a decreased false fear response rate with non-threat presentations.ConclusionThese data suggest that patients with PD exhibited a “happy-face recognition advantage” after psychotherapy. Less symptoms after treatment were related to a reduced fear bias. Thus, a differential facial emotion detection task could be a suitable tool to monitor response patterns and biases in individuals with ADs in the context of psychotherapy.

(1) Background: Radial RARE-EPI MRI facilitates simultaneous T2 and T2* mapping (2in1-RARE-EPI). With modest undersampling (R = 2), the speed gain of 2in1-RARE-EPI relative to Multi-Spin-Echo and Multi-Gradient-Recalled-Echo references is limited. Further reduction in scan time is crucial for clinical studies investigating T2 and T2* as imaging biomarkers. We demonstrate the feasibility of further acceleration, utilizing compressed sensing (CS) reconstruction of highly undersampled 2in1-RARE-EPI. (2) Methods: Two-fold radially-undersampled 2in1-RARE-EPI data from phantoms, healthy volunteers (n = 3), and multiple sclerosis patients (n = 4) were used as references, and undersampled (Rextra = 1–12, effective undersampling Reff = 2–24). For each echo time, images were reconstructed using CS-reconstruction. For T2 (RARE module) and T2* mapping (EPI module), a linear least-square fit was applied to the images. T2 and T2* from CS-reconstruction of undersampled data were benchmarked against values from CS-reconstruction of the reference data. (3) Results: We demonstrate accelerated simultaneous T2 and T2* mapping using undersampled 2in1-RARE-EPI with CS-reconstruction is feasible. For Rextra = 6 (TA = 01:39 min), the overall MAPE was ≤8% (T2*) and ≤4% (T2); for Rextra = 12 (TA = 01:06 min), the overall MAPE was 2*) and 2). (4) Conclusion: Substantial reductions in scan time are achievable for simultaneous T2 and T2* mapping of the brain using highly undersampled 2in1-RARE-EPI with CS-reconstruction.

The distribution of currents on critical percolation clusters is the fundamental quantity describing the transport properties of weakly connected systems. Nevertheless, its finite-size extrapolation is still one of the outstanding open questions concerning disordered media. By hierarchically decomposing the 3-connected components of the backbone, we disclose that the current distribution is determined from two distributions, namely, the one corresponding to the number of bonds in each level and another one corresponding to the factors by which the current is reduced, when going from one level to the next. The first distribution follows a finite-size scaling, while the second is a power law with an exponent consistent with 3/4 in two dimensions. The standard hierarchical model for the backbone is too simple to reproduce this complex scenario. Our new decomposition method of the backbone also allows to calculate much smaller currents than before, attaining a precision of 10−35 and systems of size L = 81922. Moreover, our method is not restricted to electric currents on critical percolation clusters but could also be applied to other transport problems on sparse graphs including fluid flow and car traffic.