Zero-Sum Regression Scale Invariant Molecular Data Analysis

In biomedicine, it is still an outstanding issue that the absolute scale of omics data gets lost due to technical limitations. This causes that the original scale first has to be approximated by normalization techniques before analysis methods can be applied. However, there are competing normalization strategies based on different assumptions about the structure of the underlying data. Due to these different assumptions, normalization methods can yield different results, which can also affect the outcome of concluding analysis methods. Thus, another concept is to resolve this issue by using scale invariant data analysis methods. This thesis shows how generalized linear regression methods can be extended with a scale invariance for log-transformed omics data by enforcing an additional constraint called zero-sum. Therefore, an efficient coordinate descent algorithm is developed and the advantages of this approach shown in simulations and on omics data. The corresponding open source software zeroSum is available at https://github.com/rehbergT/zeroSum

Measuring critical transitions in financial markets

Tipping points in complex systems are structural transitions from one state to another. In financial markets these critical points are connected to systemic risks, which have led to financial crisis in the past. Due to this, researchers are studying tipping points with different methods. This paper introduces a new method which bridges the gap between real-world portfolio management and statistical facts in financial markets in order to give more insight into the mechanics of financial markets

Reference point insensitive molecular data analysis

Motivation: In biomedicine, every molecular measurement is relative to a reference point, like a fixed aliquot of RNA extracted from a tissue, a defined number of blood cells, or a defined volume of biofluid. Reference points are often chosen for practical reasons. For example, we might want to assess the metabolome of a diseased organ but can only measure metabolites in blood or urine. In this case, the observable data only indirectly reflects the disease state. The statistical implications of these discrepancies in reference points have not yet been discussed. Results: Here, we show that reference point discrepancies compromise the performance of regression models like the LASSO. As an alternative, we suggest zero-sum regression for a reference point insensitive analysis. We show that zero-sum regression is superior to the LASSO in case of a poor choice of reference point both in simulations and in an application that integrates intestinal microbiome analysis with metabolomics. Moreover, we describe a novel coordinate descent based algorithm to fit zero-sum elastic nets

Indications and measures of medical emergency teams: a retrospective evaluation of in-hospital emergency operations of the German Resuscitation Register

Jansen G, Scholz SS, Rehberg S, Wnent J, Grasner J-T, Seewald S. Indications and measures of medical emergency teams: a retrospective evaluation of in-hospital emergency operations of the German Resuscitation Register. Minerva Anestesiologica. 2022.BACKGROUND: The present study examines characteristics and interventions of medical emergency teams (MET) in in-hospital emergency care.; METHODS: Analysis of all in-hospital emergencies in patients ≥18 years at 62 hospitals with established MET from the database of the German Resuscitation Registry between 2014-2019. The evaluation covered indications for activation using the ABCDE-scheme, time intervals of arrival and patient care as well as the performed invasive/medical interventions.; RESULTS: Out of 62 hospitals 14,166 in-hospital emergencies (male: 8,033 (56.7%); mean age: 64±18 years) were included. Causes of activation were Circulation- [5.760 (40.7%)], Disability- [4.076 (28.8%)], Breathing-[3.649 (25.8%)] and Airway-problems [1.589 (11.2%). Average arrival time at the emergency scene was 4±3 minutes, supply time of MET was 24±23 minutes. Endotracheal intubation was required in 1,757 (12.4%) and difficult intubation occurred in 201 (11.4%) patients with the necessity for cricothyroidotomy in 8 cases (3.9%). Invasive blood-pressure-measurement was indicated in 1,074 (7.6%) patients. Catecholamines were required for haemodynamic stabilization in 2,421 (17.1%) patients [norepinephrine: 1,520 (10.7%), epinephrine: 430 (3.0%), dobutamine: 26 (0.2%)].; CONCLUSIONS: Current in-hospital emergency care requires special skills in invasive haemodynamic and airway interventions. Recommendations from professional societies are necessary to optimise equipment (e.g. videolaryngoscopy, invasive blood pressure management), training, care algorithms and staff composition against the background of an increasing shortage of resources in the healthcare system

Scale-Invariant Biomarker Discovery in Urine and Plasma Metabolite Fingerprints

Metabolomics data is typically scaled to a common reference like a constant volume of body fluid, a constant creatinine level, or a constant area under the spectrum. Such scaling of the data, however, may affect the selection of biomarkers and the biological interpretation of results in unforeseen ways. Here, we studied how both the outcome of hypothesis tests for differential metabolite concentration and the screening for multivariate metabolite signatures are affected by the choice of scale. To overcome this problem for metabolite signatures and to establish a scale-invariant biomarker discovery algorithm, we extended linear zero-sum regression to the logistic regression framework and showed in two applications to H-1 NMR-based metabolomics data how this approach overcomes the scaling problem. Logistic zero-sum regression is available as an R package as well as a high-performance computing implementation that can be downloaded at https://github.com/rehbergT/zeroSum

Working memory training increases neural efficiency in Parkinson’s disease: a randomized controlled trial

Impairment of working memory and executive functions is already frequently observed in early stages of Parkinson’s disease. Improvements in working memory performance in this cohort could potentially be achieved via working memory training. However, the specific neural mechanisms underlying different working memory processes such as maintenance as opposed to manipulation are largely under-investigated in Parkinson’s disease. Moreover, the plasticity of these correlates as a function of working memory training is currently unknown in this population. Thus, the working memory subprocesses of maintenance and manipulation were assessed in 41 cognitively healthy patients with Parkinson’s disease using a newly developed working memory paradigm and functional MRI. Nineteen patients were randomized to a 5-week home-based digital working memory training intervention while the remaining patients entered a control, wait list condition. Working memory task-related activation patterns and context-dependent functional connectivity, as well as the change of these neural correlates as a function of training, were assessed. While both working memory processes activated an extended frontoparietal–cerebellar network, only the manipulation of items within working memory also recruited the anterior striatum. The intervention effect on the neural correlates was small, but decreased activation in areas relevant for working memory could be observed, with activation changes correlating with behavioural change. Moreover, training seemed to result in decreased functional connectivity when pure maintenance was required, and in a reorganization of functional connectivity when items had to be manipulated. In accordance with the neural efficacy hypothesis, training resulted in overall reduced activation and reorganized functional connectivity, with a differential effect on the different working memory processes under investigation. Now, larger trials including follow-up examinations are needed to further explore the long-term effects of such interventions on a neural level and to estimate the clinical relevance to potentially delay cognitive decline in cognitively healthy patients with Parkinson’s disease.Keywords: idiopathic Parkinson’s disease, home-based working memory training, functional magnetic resonance imaging, blood oxygen level dependent signal, functional connectivit

Synergy of interleukin 10 and toll-like receptor 9 signalling in B cell proliferation: Implications for lymphoma pathogenesis

A network of autocrine and paracrine signals defines B cell homeostasis and is thought to be involved in transformation processes. Investigating interactions of these microenvironmental factors and their relation to proto-oncogenes as c-Myc (MYC) is fundamental to understand the biology of B cell lymphoma. Therefore, B cells with conditional MYC expression were stimulated with CD40L, insulin-like growth factor 1, alpha-IgM, Interleukin-10 (IL10) and CpG alone or in combination. The impact of forty different interventions on cell proliferation was investigated in MYC deprived cells and calculated by linear regression. Combination of CpG and IL10 led to a strong synergistic activation of cell proliferation (S-phase/doubling of total cell number) comparable to cells with high MYC expression. A synergistic up-regulation of CDK4, CDK6 and CCND3 expression by IL10 and CpG treatment was causal for this proliferative effect as shown by qRT-PCR analysis and inhibition of the CDK4/6 complex by PD0332991. Furthermore, treatment of stimulated MYC deprived cells with MLN120b, ACHP, Pyridone 6 or Ruxolitinib showed that IL10/CpG induced proliferation and CDK4 expression were JAK/STAT3 and IKK/NF-jB dependent. This was further supported by STAT3 and p65/RELA knockdown experiments, showing strongest effects on cell proliferation and CDK4 expression after double knockdown. Additionally, chromatin immunoprecipitation revealed a dual binding of STAT3 and p65 to the proximal promotor of CDK4 after IL10/CpG treatment. Therefore, the observed synergism of IL10R and TLR9 signalling was able to induce proliferation in a comparable way as aberrant MYC and might play a role in B cell homeostasis or transformation

Scale-Invariant Biomarker Discovery in Urine and Plasma Metabolite Fingerprints

Metabolomics data is typically scaled to a common reference like a constant volume of body fluid, a constant creatinine level, or a constant area under the spectrum. Such scaling of the data, however, may affect the selection of biomarkers and the biological interpretation of results in unforeseen ways. Here, we studied how both the outcome of hypothesis tests for differential metabolite concentration and the screening for multivariate metabolite signatures are affected by the choice of scale. To overcome this problem for metabolite signatures and to establish a scale-invariant biomarker discovery algorithm, we extended linear zero-sum regression to the logistic regression framework and showed in two applications to ¹H NMR-based metabolomics data how this approach overcomes the scaling problem. Logistic zero-sum regression is available as an R package as well as a high-performance computing implementation that can be downloaded at https://github.com/rehbergT/zeroSum

Feasibility of using real-world free thyroxine data from the US and Europe to enable fast and efficient transfer of reference intervals from one population to another

Objectives: The direct approach for determining reference intervals (RIs) is not always practical. This study aimed to generate evidence that a real-world data (RWD) approach could be applied to transfer free thyroxine RIs determined in one population to a second population, presenting an alternative to performing multiple RI determinations. Design and methods: Two datasets (US, n = 10,000; Europe, n = 10,000) were created from existing RWD. Descriptive statistics, density plots and cumulative distributions were produced for each data set and comparisons made. Cumulative probabilities at the lower and upper limits of the RIs were identified using an empirical cumulative distribution function. According to these probabilities, estimated percentiles for each dataset and estimated differences between the two sets of percentiles were obtained by case resampling bootstrapping. The estimated differences were then evaluated against a pre-determined acceptance criterion of ≤7.8% (inter-individual biological variability). The direct approach was used to validate the RWD approach. Results: The RWD approach provided similar descriptive statistics for both populations (mean: US = 16.1 pmol/L, Europe = 16.4 pmol/L; median: US = 15.4 pmol/L, Europe = 15.8 pmol/L). Differences between the estimated percentiles at the upper and lower limits of the RIs fulfilled the pre-determined acceptance criterion and the density plots and cumulative distributions demonstrated population homogeneity. Similar RI distributions were observed using the direct approach. Conclusions: This study provides evidence that a RWD approach can be used to transfer RIs determined in one population to another