Multi-omic analysis of human kidney tissue identified medulla-specific gene expression patterns.

The kidney medulla is a specialized region with important homeostatic functions. It has been implicated in genetic and developmental disorders along with ischemic and drug-induced injuries. Despite its role in kidney function and disease, the medulla's baseline gene expression and epigenomic signatures have not been well described in the adult human kidney.

Satisfaction with radiotherapy care among cancer patients treated in Germany-secondary analysis of a large multicenter study.

Purpose: Patient satisfaction with healthcare has been linked to clinical outcomes and regulatory agencies demand its regular assessment. Therefore, we aimed to investigate patient satisfaction with radiotherapy care and its determinants.

Methods: This is a secondary analysis of a multicenter prospective cross-sectional study.

DOT1L activity affects neural stem cell division mode and reduces differentiation and ASNS expression.

Cortical neurogenesis depends on the balance between self-renewal and differentiation of apical progenitors (APs). Here, we study the epigenetic control of AP's division mode by focusing on the enzymatic activity of the histone methyltransferase DOT1L. Combining lineage tracing with single-cell RNA sequencing of clonally related cells, we show at the cellular level that DOT1L inhibition increases neurogenesis driven by a shift of APs from asymmetric self-renewing to symmetric neurogenic consumptive divisions.

Psychosocial distress in cancer patients undergoing radiotherapy: a prospective national cohort of 1042 patients in Germany.

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.

Characterization of aortic aging using 3D multi-parametric MRI-long-term follow-up in a population study.

We comprehensively studied morphological and functional aortic aging in a population study using modern three-dimensional MR imaging to allow future comparison in patients with diseases of the aortic valve or aorta. We followed 80 of 126 subjects of a population study (20 to 80 years of age at baseline) using the identical methodology 6.0 ± 0.

Financial toxicity in cancer patients undergoing radiotherapy in a universal health care system - A prospective multicenter study of 1075 patients.

Purpose: To establish and confirm prevalence as well as risk factors of financial toxicity in a large national cohort of cancer patients undergoing radiotherapy in a universal health care system.

Methods: We conducted a prospective cross-sectional study offering a patient-reported questionnaire to all eligible cancer patients treated with radiotherapy in 11 centers in Germany during 60 consecutive days. The four-point subjective financial distress question of the EORTC QLQ-C30 was used as a surrogate for financial toxicity.

The performance of deep generative models for learning joint embeddings of single-cell multi-omics data.

Recent extensions of single-cell studies to multiple data modalities raise new questions regarding experimental design. For example, the challenge of sparsity in single-omics data might be partly resolved by compensating for missing information across modalities. In particular, deep learning approaches, such as deep generative models (DGMs), can potentially uncover complex patterns a joint embedding.

Disentangling nigral and putaminal contribution to motor impairment and levodopa response in Parkinson's disease.

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.

Interpretable generative deep learning: an illustration with single cell gene expression data.

Deep generative models can learn the underlying structure, such as pathways or gene programs, from omics data. We provide an introduction as well as an overview of such techniques, specifically illustrating their use with single-cell gene expression data. For example, the low dimensional latent representations offered by various approaches, such as variational auto-encoders, are useful to get a better understanding of the relations between observed gene expressions and experimental factors or phenotypes.

Carotid Geometry and Wall Shear Stress Independently Predict Increased Wall Thickness-A Longitudinal 3D MRI Study in High-Risk Patients.

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. Ninety-seven consecutive patients with hypertension, at least one additional cardiovascular risk factor and internal carotid artery (ICA) plaques (wall thickness ≥ 1.

DAT SPECT Predicts Survival in Patients Assessed for Differential Diagnosis of Dementia.

Background: Dopamine transporter (DAT) SPECT is an established diagnostic procedure in dementia diagnostics, yet its prognostic value is currently unknown.

Objective: We evaluated the prognostic value of DAT SPECT in patients assessed for differential diagnosis of dementia.

Methods: We included all patients who had received DAT SPECT for differential diagnosis of dementia from 10/2008 to 06/2016 at our site and whose survival status could be obtained in 09/2019.

Synthetic single cell RNA sequencing data from small pilot studies using deep generative models.

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.

[123I]FP-CIT SPECT in Clinically Uncertain Parkinsonism Predicts Survival: A Data-Driven Analysis.

Background: Dopamine transporter SPECT is an established method to investigate nigrostriatal integrity in case of clinically uncertain parkinsonism.

Objective: The present study explores whether a data-driven analysis of [123I]FP-CIT SPECT is able to stratify patients according to mortality after SPECT.

Methods: Patients from our clinical registry were included if they had received [123I]FP-CIT SPECT between 10/2008 and 06/2016 for diagnosis of parkinsonism and if their vital status could be determined in 07/2017.

Modeling Activity Tracker Data Using Deep Boltzmann Machines.

Commercial activity trackers are set to become an essential tool in health research, due to increasing availability in the general population. The corresponding vast amounts of mostly unlabeled data pose a challenge to statistical modeling approaches. To investigate the feasibility of deep learning approaches for unsupervised learning with such data, we examine weekly usage patterns of Fitbit activity trackers with deep Boltzmann machines (DBMs).