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. At the molecular level, DOT1L activity prevents AP differentiation by promoting transcription of metabolic genes. Mechanistically, DOT1L inhibition reduces activity of an EZH2/PRC2 pathway, converging on increased expression of asparagine synthetase (ASNS), a microcephaly associated gene. Overexpression of ASNS in APs phenocopies DOT1L inhibition, and also increases neuronal differentiation of APs. Our data suggest that DOT1L activity/PRC2 crosstalk controls AP lineage progression by regulating asparagine metabolism.

Stratified neural networks in a time-to-event setting.

Deep neural networks are frequently employed to predict survival conditional on omics-type biomarkers, e.g., by employing the partial likelihood of Cox proportional hazards model as loss function. Due to the generally limited number of observations in clinical studies, combining different data sets has been proposed to improve learning of network parameters. However, if baseline hazards differ between the studies, the assumptions of Cox proportional hazards model are violated. Based on high dimensional transcriptome profiles from different tumor entities, we demonstrate how using a stratified partial likelihood as loss function allows for accounting for the different baseline hazards in a deep learning framework. Additionally, we compare the partial likelihood with the ranking loss, which is frequently employed as loss function in machine learning approaches due to its seemingly simplicity. Using RNA-seq data from the Cancer Genome Atlas (TCGA) we show that use of stratified loss functions leads to an overall better discriminatory power and lower prediction error compared to their non-stratified counterparts. We investigate which genes are identified to have the greatest marginal impact on prediction of survival when using different loss functions. We find that while similar genes are identified, in particular known prognostic genes receive higher importance from stratified loss functions. Taken together, pooling data from different sources for improved parameter learning of deep neural networks benefits largely from employing stratified loss functions that consider potentially varying baseline hazards. For easy application, we provide PyTorch code for stratified loss functions and an explanatory Jupyter notebook in a GitHub repository.

Rheuma-VOR study: optimising healthcare of rheumatic diseases by multiprofessional coordinating centres.

OBJECTIVES: Early diagnosis of inflammatory arthritis is critical to prevent joint damage and functional incapacities. However, the discrepancy between recommendations of early diagnosis and reality is remarkable. The Rheuma-VOR study aimed to improve the time to diagnosis of patients with early arthritis by coordinating cooperation between primary care physicians, specialists and patients in Germany. METHODS: This prospective non-randomised multicentre study involved 2340 primary care physicians, 72 rheumatologists, 4 university hospitals and 4 rheumatology centres in 4 German Federal States. The two coprimary endpoints (time to diagnosis and screening performance of primary care physicians) were evaluated for early versus late implementation phase. Additionally, time to diagnosis and secondary endpoints (decrease of disease activity, increase in quality of life and overall well-being, improvement of fatigue, depression, functional ability, and work ability, reduction in drug and medical costs and hospitalisation) were compared with a reference cohort of the German Rheumatism Research Centre (DRFZ) reflecting standard care. RESULTS: A total of 7049 patients were enrolled in the coordination centres and 1537 patients were diagnosed with a rheumatic disease and consented to further participation. A follow-up consultation after 1 year was realised in 592 patients. The time to diagnosis endpoint and the secondary endpoints were met. In addition, the calculation of cost-effectiveness shows that Rheuma-VOR has a dominant cost-benefit ratio compared with standard care. DISCUSSION: Rheuma-VOR has shown an improvement in rheumatological care, patient-reported outcome parameters and cost savings by coordinating the cooperation of primary care physicians, rheumatologists and patients, in a nationwide approach.

Alpha-1-antitrypsin-deficiency is associated with lower cardiovascular risk: an approach based on federated learning.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is an inflammatory multisystemic disease caused by environmental exposures and/or genetic factors. Inherited alpha-1-antitrypsin deficiency (AATD) is one of the best recognized genetic factors increasing the risk for an early onset COPD with emphysema. The aim of this study was to gain a better understanding of the associations between comorbidities and specific biomarkers in COPD patients with and without AATD to enable future investigations aimed, for example, at identifying risk factors or improving care. METHODS: We focused on cardiovascular comorbidities, blood high sensitivity troponin (hs-troponin) and lipid profiles in COPD patients with and without AATD. We used clinical data from six German University Medical Centres of the MIRACUM (Medical Informatics Initiative in Research and Medicine) consortium. The codes for the international classification of diseases (ICD) were used for COPD as a main diagnosis and for comorbidities and blood laboratory data were obtained. Data analyses were based on the DataSHIELD framework. RESULTS: Out of 112,852 visits complete information was available for 43,057 COPD patients. According to our findings, 746 patients with AATD (1.73%) showed significantly lower total blood cholesterol levels and less cardiovascular comorbidities than non-AATD COPD patients. Moreover, after adjusting for the confounder factors, such as age, gender, and nicotine abuse, we confirmed that hs-troponin is a suitable predictor of overall mortality in COPD patients. The comorbidities associated with AATD in the current study differ from other studies, which may reflect geographic and population-based differences as well as the heterogeneous characteristics of AATD. CONCLUSION: The concept of MIRACUM is suitable for the analysis of a large healthcare database. This study provided evidence that COPD patients with AATD have a lower cardiovascular risk and revealed that hs-troponin is a predictor for hospital mortality in individuals with COPD.

Genetic Cell Ablation Reveals Clusters of Local Self-Renewing Microglia in the Mammalian Central Nervous System.

During early embryogenesis, microglia arise from yolk sac progenitors that populate the developing central nervous system (CNS), but how the tissue-resident macrophages are maintained throughout the organism's lifespan still remains unclear. Here, we describe a system that allows specific, conditional ablation of microglia in adult mice. We found that the microglial compartment was reconstituted within 1 week of depletion. Microglia repopulation relied on CNS-resident cells, independent from bone-marrow-derived precursors. During repopulation, microglia formed clusters of highly proliferative cells that migrated apart once steady state was achieved. Proliferating microglia expressed high amounts of the interleukin-1 receptor (IL-1R), and treatment with an IL-1R antagonist during the repopulation phase impaired microglia proliferation. Hence, microglia have the potential for efficient self-renewal without the contribution of peripheral myeloid cells, and IL-1R signaling participates in this restorative proliferation process.

Gene expression variability in long-term survivors of childhood cancer and cancer-free controls in response to ionizing irradiation.

BACKGROUND: Differential expression analysis is usually adjusted for variation. However, most studies that examined the expression variability (EV) have used computations affected by low expression levels and did not examine healthy tissue. This study aims to calculate and characterize an unbiased EV in primary fibroblasts of childhood cancer survivors and cancer-free controls (N0) in response to ionizing radiation. METHODS: Human skin fibroblasts of 52 donors with a first primary neoplasm in childhood (N1), 52 donors with at least one second primary neoplasm (N2 +), as well as 52 N0 were obtained from the KiKme case-control study and exposed to a high (2 Gray) and a low dose (0.05 Gray) of X-rays and sham- irradiation (0 Gray). Genes were then classified as hypo-, non-, or hyper-variable per donor group and radiation treatment, and then examined for over-represented functional signatures. RESULTS: We found 22 genes with considerable EV differences between donor groups, of which 11 genes were associated with response to ionizing radiation, stress, and DNA repair. The largest number of genes exclusive to one donor group and variability classification combination were all detected in N0: hypo-variable genes after 0 Gray (n = 49), 0.05 Gray (n = 41), and 2 Gray (n = 38), as well as hyper-variable genes after any dose (n = 43). While after 2 Gray positive regulation of cell cycle was hypo-variable in N0, (regulation of) fibroblast proliferation was over-represented in hyper-variable genes of N1 and N2+. In N2+, 30 genes were uniquely classified as hyper-variable after the low dose and were associated with the ERK1/ERK2 cascade. For N1, no exclusive gene sets with functions related to the radiation response were detected in our data. CONCLUSION: N2+ showed high degrees of variability in pathways for the cell fate decision after genotoxic insults that may lead to the transfer and multiplication of DNA-damage via proliferation, where apoptosis and removal of the damaged genome would have been appropriate. Such a deficiency could potentially lead to a higher vulnerability towards side effects of exposure to high doses of ionizing radiation, but following low-dose applications employed in diagnostics, as well.

Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges.

BACKGROUND: In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions. METHODS: Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 "High-dimensional data" of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD. RESULTS: The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided. CONCLUSIONS: This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses.

Synthetic observations from deep generative models and binary omics data with limited sample size.

Deep generative models can be trained to represent the joint distribution of data, such as measurements of single nucleotide polymorphisms (SNPs) from several individuals. Subsequently, synthetic observations are obtained by drawing from this distribution. This has been shown to be useful for several tasks, such as removal of noise, imputation, for better understanding underlying patterns, or even exchanging data under privacy constraints. Yet, it is still unclear how well these approaches work with limited sample size. We investigate such settings specifically for binary data, e.g. as relevant when considering SNP measurements, and evaluate three frequently employed generative modeling approaches, variational autoencoders (VAEs), deep Boltzmann machines (DBMs) and generative adversarial networks (GANs). This includes conditional approaches, such as when considering gene expression conditional on SNPs. Recovery of pair-wise odds ratios (ORs) is considered as a primary performance criterion. For simulated as well as real SNP data, we observe that DBMs generally can recover structure for up to 300 variables, with a tendency of over-estimating ORs when not carefully tuned. VAEs generally get the direction and relative strength of pairwise relations right, yet with considerable under-estimation of ORs. GANs provide stable results only with larger sample sizes and strong pair-wise relations in the data. Taken together, DBMs and VAEs (in contrast to GANs) appear to be well suited for binary omics data, even at rather small sample sizes. This opens the way for many potential applications where synthetic observations from omics data might be useful.

How do (false) positively screened patients experience a screening programme for liver cirrhosis or fibrosis in Germany? A qualitative study.

OBJECTIVE: This study aimed to explore psychosocial consequences of (false) positive liver screening results and to identify influencing factors for perceived strain within a multistage screening programme for liver cirrhosis and fibrosis in Germany. METHODS: Between June 2018 and May 2019, all positively screened patients were asked to participate in the study (n = 158). N = 11 telephone interviews and n = 4 follow-up interviews were conducted. Semi-structured telephone interviews were carried out. The analysis followed a structuring content analysis approach. Thereby, categories were first defined deductively. Second, the categories were revised inductively based on the data. RESULTS: The main themes found regarding the consequences of the screening were categorised in emotional reactions and behavioural reactions. Few respondents described negative emotional consequences related to screening. Those seem to be mostly driven by suboptimal patient-provider communication and might be worsened when transparent information transfer fails to happen. As a result, patients sought information and support in their social environment. All patients reported positive attitudes towards liver screening. CONCLUSION: To reduce the potential occurrence of psychosocial consequences during the screening process, medical screening should be performed in the context of transparent information. Regular health communication on the side of health professionals and increasing patients' health literacy might contribute to avoiding negative emotions in line with screening. PATIENT OR PUBLIC CONTRIBUTION: This study recognises the wide-ranging patients' perspectives regarding the consequences of liver screening which should be taken into consideration when implementing a new screening programme to ensure a patient-centred approach.

German Version of the Mobile Agnew Relationship Measure: Translation and Validation Study.

BACKGROUND: The mobile Agnew Relationship Measure (mARM) is a self-report questionnaire for the evaluation of digital mental health interventions and their interactions with users. With the global increase in digital mental health intervention research, translated measures are required to conduct research with local populations. OBJECTIVE: The aim of this study was to translate and validate the original English version of the mARM into a German version (mARM-G). METHODS: A total of 2 native German speakers who spoke English as their second language conducted forward translation of the original items. This version was then back translated by 2 native German speakers with a fluent knowledge of English. An independent bilingual reviewer then compared these drafts and created a final German version. The mARM-G was validated by 15 experts in the field of mobile app development and 15 nonexperts for content validity and face validity; 144 participants were recruited to conduct reliability testing as well as confirmatory factor analysis. RESULTS: The content validity index of the mARM-G was 0.90 (expert ratings) and 0.79 (nonexperts). The face validity index was 0.89 (experts) and 0.86 (nonexperts). Internal consistency for the entire scale was Cronbach α=.91. Confirmatory factor analysis results were as follows: the chi-square statistic to df ratio was 1.66. Comparative Fit Index was 0.87 and the Tucker-Lewis Index was 0.86. The root mean square error of approximation was 0.07. CONCLUSIONS: The mARM-G is a valid and reliable tool that can be used for future studies in German-speaking countries.

App-Controlled Treatment Monitoring and Support for Patients With Head and Neck Cancer Undergoing Radiotherapy: Results From a Prospective Randomized Controlled Trial.

BACKGROUND: Head and neck cancers (HNCs) are very common malignancies, and treatment often requires multimodal approaches, including radiotherapy and chemotherapy. Patients with HNC often display a high symptom burden, both due to the disease itself and the adverse effects of the multimodal therapy. Close telemonitoring of symptoms and quality of life during the course of treatment may help to identify those patients requiring early medical support. OBJECTIVE: The App-Controlled Treatment Monitoring and Support for Patients With Head and Neck Cancer (APCOT) trial aimed to investigate the feasibility of integrating electronic patient-reported outcomes (ePROs) in the treatment surveillance pathway of patients with HNC during the course of their radiotherapy. Additionally, the influence of app-based ePRO monitoring on global and disease-specific quality of life and patient satisfaction with treatment was assessed. METHODS: Patients undergoing radiotherapy for histologically proven HNCs at the Department of Radiation Oncology, University Medical Center Freiburg, Germany, were enrolled in this trial and monitored by weekly physician appointments. Patients were randomized between additional ePRO monitoring on each treatment day or standard-of-care monitoring. Feasibility of ePRO monitoring was defined as ≥80% of enrolled patients answering ≥80% of their daily app-based questions. Quality of life and patient satisfaction were assessed by the European Organisation for Research and Treatment of Cancer Core Quality of Life Questionnaire (QLQ-C30), the head and neck cancer module (H&N35), and the validated Patient Satisfaction Questionnaire Short Form (PSQ-18) at the completion of treatment and compared between trial arms. RESULTS: A total of 100 patients were enrolled in this trial, and 93 patients were evaluable. All patients (100%) in the experimental arm answered ≥80% of the ePRO questions during treatment, reaching the predefined threshold for the feasibility of ePRO monitoring (P<.001 in the binomial test). No clinical or patient-specific factor was found to influence feasibility. Global health and most domains of the general quality of life were comparable between trial arms, but an increased HNC-specific symptom burden was reported by patients undergoing ePRO surveillance. ePRO monitoring resulted in improved patient satisfaction regarding interpersonal manners (P=.01), financial aspects (P=.01), and time spent with a doctor (P=.01). CONCLUSIONS: This trial demonstrated the feasibility of incorporating daily app-based ePRO surveillance for patients with HNC undergoing radiotherapy. Our data, for the first time, demonstrate that telemonitoring in this setting led to increased reporting of HNC-specific symptom burden and significantly improved several domains of patient satisfaction. Further analyses are needed to assess whether our findings hold true outside the context of a clinical trial. TRIAL REGISTRATION: German Clinical Trials Register DRKS00020491; https://drks.de/search/en/trial/DRKS00020491.

Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods.

When modeling longitudinal biomedical data, often dimensionality reduction as well as dynamic modeling in the resulting latent representation is needed. This can be achieved by artificial neural networks for dimension reduction and differential equations for dynamic modeling of individual-level trajectories. However, such approaches so far assume that parameters of individual-level dynamics are constant throughout the observation period. Motivated by an application from psychological resilience research, we propose an extension where different sets of differential equation parameters are allowed for observation subperiods. Still, estimation for intra-individual subperiods is coupled for being able to fit the model also with a relatively small dataset. We subsequently derive prediction targets from individual dynamic models of resilience in the application. These serve as outcomes for predicting resilience from characteristics of individuals, measured at baseline and a follow-up time point, and selecting a small set of important predictors. Our approach is seen to successfully identify individual-level parameters of dynamic models that allow to stably select predictors, that is, resilience factors. Furthermore, we can identify those characteristics of individuals that are the most promising for updates at follow-up, which might inform future study design. This underlines the usefulness of our proposed deep dynamic modeling approach with changes in parameters between observation subperiods.

Structured Early detection of Asymptomatic Liver Cirrhosis: Results of the population-based liver screening program SEAL.

BACKGROUND & AIMS: Detection of patients with early cirrhosis is of importance to prevent the occurrence of complications and improve prognosis. The SEAL program aimed at evaluating the usefulness of a structured screening procedure to detect cirrhosis as early as possible. METHODS: SEAL was a prospective cohort study with a control cohort from routine care data. Individuals participating in the general German health check-up after the age of 35 ("Check-up 35") at their primary care physicians were offered a questionnaire, liver function tests (aspartate and alanine aminotransferase [AST and ALT]), and follow-up. If AST/ALT levels were elevated, the AST-to-platelet ratio index (APRI) score was calculated, and patients with a score >0.5 were referred to a liver expert in secondary and/or tertiary care. RESULTS: A total of 11,859 participants were enrolled and available for final analysis. The control group comprised 349,570 participants of the regular Check-up 35. SEAL detected 488 individuals with elevated APRI scores (4.12%) and 45 incident cases of advanced fibrosis/cirrhosis. The standardized incidence of advanced fibrosis/cirrhosis in the screening program was slightly higher than in controls (3.83‰ vs. 3.36‰). The comparison of the chance of fibrosis/cirrhosis diagnosis in SEAL vs. in standard care was inconclusive (marginal odds ratio 1.141, one-sided 95% CI 0.801, +Inf). Of note, when patients with decompensated cirrhosis at initial diagnosis were excluded from both cohorts in a post hoc analysis, SEAL was associated with a 59% higher chance of early cirrhosis detection on average than routine care (marginal odds ratio 1.590, one-sided 95% CI 1.080, +Inf; SEAL 3.51‰, controls: 2.21‰). CONCLUSIONS: The implementation of a structured screening program may increase the early detection rate of cirrhosis in the general population. In this context, the SEAL pathway represents a feasible and potentially cost-effective screening program. REGISTRATION: DRKS00013460 LAY SUMMARY: Detection of patients with early liver cirrhosis is of importance to prevent the occurrence of complications and improve prognosis. This study demonstrates that the implementation of a structured screening program using easily obtainable measures of liver function may increase the early detection rate of cirrhosis in the general population. In this context, the 'SEAL' pathway represents a feasible and potentially cost-effective screening program.

T-cell exhaustion and residency dynamics inform clinical outcomes in hepatocellular carcinoma.

BACKGROUND & AIMS: Despite recent translation of immunotherapies into clinical practice, the immunobiology of hepatocellular carcinoma (HCC), in particular the role and clinical relevance of exhausted and liver-resident T cells remain unclear. We therefore dissected the landscape of exhausted and resident T cell responses in the peripheral blood and tumor microenvironment of patients with HCC. METHODS: Lymphocytes were isolated from the blood, tumor and tumor-surrounding liver tissue of patients with HCC (n = 40, n = 10 treated with anti-PD-1 therapy). Phenotype, function and response to anti-PD-1 were analyzed by mass and flow cytometry ex vivo and in vitro, tissue residence was further assessed by immunohistochemistry and imaging mass cytometry. Gene signatures were analyzed in silico. RESULTS: We identified significant enrichment of heterogeneous populations of exhausted CD8+ T cells (TEX) in the tumor microenvironment. Strong enrichment of severely exhausted CD8 T cells expressing multiple immune checkpoints in addition to PD-1 was linked to poor progression-free and overall survival. In contrast, PD-1 was also expressed on a subset of more functional and metabolically active CD103+ tissue-resident memory T cells (TRM) that expressed few additional immune checkpoints and were associated with better survival. TEX enrichment was independent of BCLC stage, alpha-fetoprotein levels or age as a variable for progression-free survival in our cohort. These findings were in line with in silico gene signature analysis of HCC tumor transcriptomes from The Cancer Genome Atlas. A higher baseline TRM/TEX ratio was associated with disease control in anti-PD-1-treated patients. CONCLUSION: Our data provide information on the role of peripheral and intratumoral TEX-TRM dynamics in determining outcomes in patients with HCC. The dynamics between exhausted and liver-resident T cells have implications for immune-based diagnostics, rational patient selection and monitoring during HCC immunotherapies. LAY SUMMARY: The role of the immune response in hepatocellular carcinoma (HCC) remains unclear. T cells can mediate protection against tumor cells but are frequently dysfunctional and exhausted in cancer. We found that patients with a predominance of exhausted CD8+ T cells (TEX) had poor survival compared to patients with a predominance of tissue-resident memory T cells (TRM). This correlated with the molecular profile, metabolic and functional status of these cell populations. The enrichment of TEX was independently associated with prognosis in addition to disease stage, age and tumor markers. A high TRM proportion was also associated with better outcomes following checkpoint therapy. Thus, these T-cell populations are novel biomarkers with relevance in HCC.

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. Furthermore, by providing a generative model for the latent and observed variables, deep generative models can generate synthetic observations, which allow us to assess the uncertainty in the learned representations. While deep generative models are useful to learn the structure of high-dimensional omics data by efficiently capturing non-linear dependencies between genes, they are sometimes difficult to interpret due to their neural network building blocks. More precisely, to understand the relationship between learned latent variables and observed variables, e.g., gene transcript abundances and external phenotypes, is difficult. Therefore, we also illustrate current approaches that allow us to infer the relationship between learned latent variables and observed variables as well as external phenotypes. Thereby, we render deep learning approaches more interpretable. In an application with single-cell gene expression data, we demonstrate the utility of the discussed methods.

Radiation-response in primary fibroblasts of long-term survivors of childhood cancer with and without second primary neoplasms: the KiKme study.

BACKGROUND: The etiology and most risk factors for a sporadic first primary neoplasm in childhood or subsequent second primary neoplasms are still unknown. One established causal factor for therapy-associated second primary neoplasms is the exposure to ionizing radiation during radiation therapy as a mainstay of cancer treatment. Second primary neoplasms occur in 8% of all cancer survivors within 30 years after the first diagnosis in Germany, but the underlying factors for intrinsic susceptibilities have not yet been clarified. Thus, the purpose of this nested case-control study was the investigation and comparison of gene expression and affected pathways in primary fibroblasts of childhood cancer survivors with a first primary neoplasm only or with at least one subsequent second primary neoplasm, and controls without neoplasms after exposure to a low and a high dose of ionizing radiation. METHODS: Primary fibroblasts were obtained from skin biopsies from 52 adult donors with a first primary neoplasm in childhood (N1), 52 with at least one additional primary neoplasm (N2+), as well as 52 without cancer (N0) from the KiKme study. Cultured fibroblasts were exposed to a high [2 Gray (Gy)] and a low dose (0.05 Gy) of X-rays. Messenger ribonucleic acid was extracted 4 h after exposure and Illumina-sequenced. Differentially expressed genes (DEGs) were computed using limma for R, selected at a false discovery rate level of 0.05, and further analyzed for pathway enrichment (right-tailed Fisher's Exact Test) and (in-) activation (z ≥|2|) using Ingenuity Pathway Analysis. RESULTS: After 0.05 Gy, least DEGs were found in N0 (n = 236), compared to N1 (n = 653) and N2+ (n = 694). The top DEGs with regard to the adjusted p-value were upregulated in fibroblasts across all donor groups (SESN1, MDM2, CDKN1A, TIGAR, BTG2, BLOC1S2, PPM1D, PHLDB3, FBXO22, AEN, TRIAP1, and POLH). Here, we observed activation of p53 Signaling in N0 and to a lesser extent in N1, but not in N2+. Only in N0, DNA (excision-) repair (involved genes: CDKN1A, PPM1D, and DDB2) was predicted to be a downstream function, while molecular networks in N2+ were associated with cancer, as well as injury and abnormalities (among others, downregulation of MSH6, CCNE2, and CHUK). After 2 Gy, the number of DEGs was similar in fibroblasts of all donor groups and genes with the highest absolute log2 fold-change were upregulated throughout (CDKN1A, TIGAR, HSPA4L, MDM2, BLOC1SD2, PPM1D, SESN1, BTG2, FBXO22, PCNA, and TRIAP1). Here, the p53 Signaling-Pathway was activated in fibroblasts of all donor groups. The Mitotic Roles of Polo Like Kinase-Pathway was inactivated in N1 and N2+. Molecular Mechanisms of Cancer were affected in fibroblasts of all donor groups. P53 was predicted to be an upstream regulator in fibroblasts of all donor groups and E2F1 in N1 and N2+. Results of the downstream analysis were senescence in N0 and N2+, transformation of cells in N0, and no significant effects in N1. Seven genes were differentially expressed in reaction to 2 Gy dependent on the donor group (LINC00601, COBLL1, SESN2, BIN3, TNFRSF10A, EEF1AKNMT, and BTG2). CONCLUSION: Our results show dose-dependent differences in the radiation response between N1/N2+ and N0. While mechanisms against genotoxic stress were activated to the same extent after a high dose in all groups, the radiation response was impaired after a low dose in N1/N2+, suggesting an increased risk for adverse effects including carcinogenesis, particularly in N2+.

Data-driven prediction of COVID-19 cases in Germany for decision making.

BACKGROUND: The COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation. METHODS: We developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. The analysis uses data from Germany describing the COVID-19 spread from early 2020 until March 31st, 2021. RESULTS: The model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible. CONCLUSIONS: We successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units.

A nationwide registry for recurrent urolithiasis in the upper urinary tract - The RECUR study protocol.

BACKGROUND: Urinary stone disease is a widespread disease with tremendous impact on those affected and on societies around the globe. Nevertheless, clinical and health care research in this area seem to lag far behind cardiovascular diseases or cancer. This may be due to the lack of an immediate deadly threat from the disease and therefore less public and professional interest. However, the patients suffer from recurring, sometimes intense pain and often must be treated in hospital. Long-term morbidity includes doubled rates of chronic kidney disease and arterial hypertension after at least one stone-related event. Observational studies, more specifically, registries and other electronic data sets have been proposed as a means of filling critical gaps in evidence. We propose a nationwide digital and fully automated registry as part of the German Ministry for Education and Research (BMBF) call for the "establishment of model registries". METHODS: RECUR builds on the technical infrastructure of Germany's Medical Informatics Initiative. Local data integration centres (DIC) of participating medical universities will collect pseudonymized and harmonized data from respective hospital information systems. In addition to their clinical data, participants will provide patient reported outcomes using a mobile patient app. Scientific data exploration includes queries and analysis of federated data from DICs of eleven participating sites. All primary patient data will remain at the participating sites at all times. With comprehensive data from this longitudinal registry, we will be able to describe the disease burden, to determine and validate risk factors, and to evaluate treatments. Implementation and operation of the RECUR registry will be funded by the BMBF for five years. Subsequently, the registry is to be continued by the German Society of Urology without significant costs for study personnel. DISCUSSION: The proposed registry will substantially improve the structural and procedural framework for patients with recurrent urolithiasis. This includes advanced diagnostic algorithms and treatment pathways. The registry will help us identify those patients who will most benefit from specific interventions to prevent recurrences. The RECUR study protocol and the registry's technical architecture including full digitalization and automation of almost all registry-associated proceedings can be transferred to future registries. TRIAL REGISTRATION: This study is registered at the German Clinical Trial Register (Deutsches Register Klinischer Studien), DRKS-ID DRKS00026923 , date of registration January, 11th 2022.

Chronic venous insufficiency, cardiovascular disease, and mortality: a population study.

AIMS: Evidence regarding the health burden of chronic venous insufficiency (CVI), its clinical determinants, and impact on outcome is scarce. METHODS AND RESULTS: Systematic phenotyping of CVI according to established CEAP (Clinical-Etiologic-Anatomic-Pathophysiologic) classification was performed in 12 423 participants (age range: 40-80 years) of the Gutenberg Health Study from April 2012 to April 2017. Prevalence was calculated age- and sex-specifically. Multivariable Poisson regression models were calculated to evaluate the relation of CVI with cardiovascular comorbidities. Survival analyses were carried out to assess the CVI-associated risk of death. Replication of findings was done in an independent cohort study (MyoVasc, NCT04064450). The prevalence of telangiectasia/reticular, varicose veins, and CVI was 36.5% [95% confidence interval (CI), 35.6-37.4%], 13.3% [12.6-13.9%], and 40.8% [39.9-41.7%], respectively. Age, female sex, arterial hypertension, obesity, smoking, and clinically overt cardiovascular disease were identified as clinical determinants of CVI. Higher CEAP classes were associated with a higher predicted 10-year risk for incident cardiovascular disease in individuals free of cardiovascular disease (n = 9923). During a mean follow-up of 6.4 ± 1.6 years, CVI was a strong predictor of all-cause death independent of the concomitant clinical profile and medication [hazard ratio (HR) 1.46 (95% CI 1.19-1.79), P = 0. 0003]. The association of CVI with an increased risk of all-cause death was externally validated in the MyoVasc cohort [HR 1.51 (95% CI 1.11-2.05), P = 0.009]. CONCLUSION: Chronic venous insufficiency is highly prevalent in the population and is associated with the presence of cardiovascular risk factors and disease. Individuals with CVI experience an elevated risk of death, which is independent of age and sex, and present cardiovascular risk factors and comorbidities.

Deep dynamic modeling with just two time points: Can we still allow for individual trajectories?

Longitudinal biomedical data are often characterized by a sparse time grid and individual-specific development patterns. Specifically, in epidemiological cohort studies and clinical registries we are facing the question of what can be learned from the data in an early phase of the study, when only a baseline characterization and one follow-up measurement are available. Inspired by recent advances that allow to combine deep learning with dynamic modeling, we investigate whether such approaches can be useful for uncovering complex structure, in particular for an extreme small data setting with only two observations time points for each individual. Irregular spacing in time could then be used to gain more information on individual dynamics by leveraging similarity of individuals. We provide a brief overview of how variational autoencoders (VAEs), as a deep learning approach, can be linked to ordinary differential equations (ODEs) for dynamic modeling, and then specifically investigate the feasibility of such an approach that infers individual-specific latent trajectories by including regularity assumptions and individuals' similarity. We also provide a description of this deep learning approach as a filtering task to give a statistical perspective. Using simulated data, we show to what extent the approach can recover individual trajectories from ODE systems with two and four unknown parameters and infer groups of individuals with similar trajectories, and where it breaks down. The results show that such dynamic deep learning approaches can be useful even in extreme small data settings, but need to be carefully adapted.