Introduction: Carotid geometry and wall shear stress (WSS) have been proposed as independent risk factors for the progression of carotid atherosclerosis, but this has not yet been demonstrated in larger longitudinal studies. Therefore, we investigated the impact of these biomarkers on carotid wall thickness in patients with high cardiovascular risk.Methods: Ninety-seven consecutive patients with hypertension, at least one additional cardiovascular risk factor and internal carotid artery (ICA) plaques (wall thickness ≥ 1.5 mm and degree of stenosis ≤ 50%) were prospectively included. They underwent high-resolution 3D multi-contrast and 4D flow MRI at 3 Tesla both at baseline and follow-up. Geometry (ICA/common carotid artery (CCA)-diameter ratio, bifurcation angle, tortuosity and wall thickness) and hemodynamics [WSS, oscillatory shear index (OSI)] of both carotid bifurcations were measured at baseline. Their predictive value for changes of wall thickness 12 months later was calculated using linear regression analysis for the entire study cohort (group 1, 97 patients) and after excluding patients with ICA stenosis ≥10% to rule out relevant inward remodeling (group 2, 61 patients).Results: In group 1, only tortuosity at baseline was independently associated with carotid wall thickness at follow-up (regression coefficient = −0.52, p < 0.001). However, after excluding patients with ICA stenosis ≥10% in group 2, both ICA/CCA-ratio (0.49, p < 0.001), bifurcation angle (0.04, p = 0.001), tortuosity (−0.30, p = 0.040), and WSS (−0.03, p = 0.010) at baseline were independently associated with changes of carotid wall thickness at follow-up.Conclusions: A large ICA bulb and bifurcation angle and low WSS seem to be independent risk factors for the progression of carotid atherosclerosis in the absence of ICA stenosis. By contrast, a high carotid tortuosity seems to be protective both in patients without and with ICA stenosis. These biomarkers may be helpful for the identification of patients who are at particular risk of wall thickness progression and who may benefit from intensified monitoring and treatment.

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The extent to which the degeneration of the substantia nigra (SN) and putamen each contribute to motor impairment in Parkinson’s disease (PD) is unclear, as they are usually investigated using different imaging modalities. To examine the pathophysiological significance of the SN and putamen in both motor impairment and the levodopa response in PD using diffusion microstructure imaging (DMI). In this monocentric retrospective cross-sectional study, DMI parameters from 108 patients with PD and 35 healthy controls (HC) were analyzed using a voxel- and region-based approach. Linear models were applied to investigate the association between individual DMI parameters and Movement Disorder Society Unified Parkinson’s Disease Rating Scale-Part 3 performance in ON- and OFF-states, as well as the levodopa response, controlling for age and sex. Voxel- and region-based group comparisons of DMI parameters between PD and HC revealed significant differences in the SN and putamen. In PD, a poorer MDS-UPDRS-III performance in the ON-state was associated with increased free fluid in the SN (b-weight = 65.79, p = 0.004) and putamen (b-weight = 86.00, p = 0.006), and contrariwise with the demise of cells in both structures. The levodopa response was inversely associated with free fluid both in the SN (b-weight = −83.61, p = 0.009) and putamen (b-weight = −176.56, p < 0.001). Interestingly, when the two structures were assessed together, the integrity of the putamen, but not the SN, served as a predictor for the levodopa response (b-weight = −158.03, p < 0.001). Structural alterations in the SN and putamen can be measured by diffusion microstructure imaging in PD. They are associated with poorer motor performance in the ON-state, as well as a reduced response to levodopa. While both nigral and putaminal integrity are required for good performance in the ON-state, it is putaminal integrity alone that determines the levodopa response. Therefore, the structural integrity of the putamen is crucial for the improvement of motor symptoms to dopaminergic medication, and might therefore serve as a promising biomarker for motor staging.

Deep generative models, such as variational autoencoders (VAEs) or deep Boltzmann machines (DBMs), can generate an arbitrary number of synthetic observations after being trained on an initial set of samples. This has mainly been investigated for imaging data but could also be useful for single-cell transcriptomics (scRNA-seq). A small pilot study could be used for planning a full-scale experiment by investigating planned analysis strategies on synthetic data with different sample sizes. It is unclear whether synthetic observations generated based on a small scRNA-seq dataset reflect the properties relevant for subsequent data analysis steps. We specifically investigated two deep generative modeling approaches, VAEs and DBMs. First, we considered single-cell variational inference (scVI) in two variants, generating samples from the posterior distribution, the standard approach, or the prior distribution. Second, we propose single-cell deep Boltzmann machines (scDBMs). When considering the similarity of clustering results on synthetic data to ground-truth clustering, we find that the $$scVI_{posterior}$$ s c V I posterior variant resulted in high variability, most likely due to amplifying artifacts of small datasets. All approaches showed mixed results for cell types with different abundance by overrepresenting highly abundant cell types and missing less abundant cell types. With increasing pilot dataset sizes, the proportions of the cells in each cluster became more similar to that of ground-truth data. We also showed that all approaches learn the univariate distribution of most genes, but problems occurred with bimodality. Across all analyses, in comparing 10$$\times$$ × Genomics and Smart-seq2 technologies, we could show that for 10$$\times$$ × datasets, which have higher sparsity, it is more challenging to make inference from small to larger datasets. Overall, the results show that generative deep learning approaches might be valuable for supporting the design of scRNA-seq experiments.

Purpose Psychosocial distress is common among cancer patients in general, but those undergoing radiotherapy may face specific challenges. Therefore, we investigated the prevalence and risk factors for distress in a large national cohort. Methods We performed a secondary analysis of a multicenter prospective cross-sectional study which surveyed cancer patients at the end of a course of radiotherapy using a patient-reported questionnaire. Distress was measured with the distress thermometer (DT), using a cut-off of ≥ 5 points for clinically significant distress. Univariate analyses and multivariate multiple regression were used to assess associations of distress with patient characteristics. A two-sided p-value < 0.05 was considered statistically significant. Results Out of 2341 potentially eligible patients, 1075 participated in the study, of which 1042 completed the DT. The median age was 65 years and 49% (511/1042) of patients were female. The mean DT score was 5.2 (SD = 2.6). Clinically significant distress was reported by 63% (766/1042) of patients. Of the patient characteristics that were significantly associated with distress in the univariate analysis, a lower level of education, a higher degree of income loss, lower global quality of life, and a longer duration of radiotherapy in days remained significantly associated with higher distress in the multivariate analysis. Yet effect sizes of these associations were small. Conclusion Nearly two in three cancer patients undergoing radiotherapy reported clinically significant distress in a large multicenter cohort. While screening and interventions to reduce distress should be maintained and promoted, the identified risk factors may help to raise awareness in clinical practice. Trial Registry identifier DRKS: German Clinical Trial Registry identifier: DRKS00028784.