Modelling of patient journey in chronic spontaneous urticaria: Increasing awareness and education by shorten patients' disease journey in Germany.

BACKGROUND: Chronic spontaneous urticaria (CSU) is both physically and emotionally stressful, and guideline recommendations are often not optimally implemented in clinical practice. The objective of this study was to provide an overview on the patient journey in CSU and to develop a mathematical model based on solid data. METHODS: The journey of CSU patients in Germany was traced through literature review and expert meetings that included medical experts, pharmacists and representatives of patient organizations. The current situation's main challenges in the patient journey (education, collaboration and disease management) were discussed in depth. Then, a probabilistic model was developed in a co-creation approach to simulate the impact of three potential improvement strategies: (1) patient education campaign, (2) medical professional education programme and (3) implementation of a disease management programme (DMP). RESULTS: Chronic spontaneous urticaria patients are severely burdened by delays in diagnosis and optimal medical care. Our simulation indicates that in Germany, it takes on average of 3.8 years for patients to achieve disease control in Germany. Modelling all three optimization strategies resulted in a reduction to 2.5 years until CSU symptom control. On a population level, the proportion of CSU patients with disease control increased from 44.2% to 58.1%. CONCLUSION: In principle, effective CSU medications and a disease-specific guideline are available. However, implementation of recommendations is lagging in practice. The approach of quantitative modelling of the patient journey validates obstacles and shows a clear effect of multiple interventions on the patient journey. The data generated by our simulation can be used to identify strategies for improving patient care. Our approach might helping in understanding and improving the management of patients beyond CSU.

Synaptic Mechanisms underlying Temporally Precise Information Processing in the VNLL, an auditory brainstem nucleus.

Large glutamatergic, somatic synapses mediate temporally precise information transfer. In the ventral nucleus of the lateral lemniscus (VNLL), an auditory brainstem nucleus, the signal of an excitatory large somatic synapse is sign inverted to generate rapid feed forward inhibition with high temporal acuity at sound onsets, a mechanism involved in the suppression of spurious frequency information. The mechanisms of the synaptically driven input-output functions in the VNLL are not fully resolved. Here, we show in Mongolian gerbils of both sexes that for stimulation frequencies up to 200 Hz the EPSC kinetics together with short-term plasticity allow for faithful transmission with only a small increase in latency. Glutamatergic currents are exclusively mediated by AMPARs and NMDARs. Short-term plasticity is frequency dependent and composed of an initial facilitation followed by depression. Physiologically relevant output generation is limited by the decrease in synaptic conductance through short-term plasticity (STP). At this endbulb synapse, STP acts as a low pass filter and increases the dynamic range of the conductance dependent input-output relation, while NMDAR signaling slightly increases the sensitivity of the input-output function. Our computational model shows that STP-mediated filtering limits the intensity dependence of the spike output, thus maintaining selectivity to sound transients. Our results highlight the interaction of cellular features that together give rise to the computations in the circuit.Significant statementAuditory information processing in the brainstem is a prerequisite for generating our auditory representation of the environment. Thereby, many processing steps rely on temporally precise filtering. Precise feed forward inhibition is a key motif in auditory brainstem processing and produced through sign inversion at several large somatic excitatory synapses. A particular feature of the ventral nucleus of the lateral lemniscus is to produce temporally precise onset inhibition with little temporal variance independent of sound intensity. Our cell-physiology and modeling data explain how the synaptic characteristics of different current components and their short-term plasticity are tuned to establish sound intensity-invariant onset inhibition that is crucial for filtering out spurious frequency information.

Analyzing Patient Trajectories With Artificial Intelligence.

In digital medicine, patient data typically record health events over time (eg, through electronic health records, wearables, or other sensing technologies) and thus form unique patient trajectories. Patient trajectories are highly predictive of the future course of diseases and therefore facilitate effective care. However, digital medicine often uses only limited patient data, consisting of health events from only a single or small number of time points while ignoring additional information encoded in patient trajectories. To analyze such rich longitudinal data, new artificial intelligence (AI) solutions are needed. In this paper, we provide an overview of the recent efforts to develop trajectory-aware AI solutions and provide suggestions for future directions. Specifically, we examine the implications for developing disease models from patient trajectories along the typical workflow in AI: problem definition, data processing, modeling, evaluation, and interpretation. We conclude with a discussion of how such AI solutions will allow the field to build robust models for personalized risk scoring, subtyping, and disease pathway discovery.

A phenomenological spiking model for octopus cells in the posterior-ventral cochlear nucleus.

Octopus cells in the posteroventral cochlear nucleus exhibit characteristic onset responses to broad band transients but are little investigated in response to more complex sound stimuli. In this paper, we propose a phenomenological, but biophysically motivated, modeling approach that allows to simulate responses of large populations of octopus cells to arbitrary sound pressure waves. The model depends on only few parameters and reproduces basic physiological characteristics like onset firing and phase locking to amplitude modulations. Simulated responses to speech stimuli suggest that octopus cells are particularly sensitive to high-frequency transients in natural sounds and their sustained firing to phonemes provides a population code for sound level.

Traditional and Digital Biomarkers: Two Worlds Apart?

The identification and application of biomarkers in the clinical and medical fields has an enormous impact on society. The increase of digital devices and the rise in popularity of health-related mobile apps has produced a new trove of biomarkers in large, diverse, and complex data. However, the unclear definition of digital biomarkers, population groups, and their intersection with traditional biomarkers hinders their discovery and validation. We have identified current issues in the field of digital biomarkers and put forth suggestions to address them during the DayOne Workshop with participants from academia and industry. We have found similarities and differences between traditional and digital biomarkers in order to synchronize semantics, define unique features, review current regulatory procedures, and describe novel applications that enable precision medicine.

From hype to reality: data science enabling personalized medicine.

BACKGROUND: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. CONCLUSIONS: There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.

Towards a systems approach for chronic diseases, based on health state modeling.

Rising pressure from chronic diseases means that we need to learn how to deal with challenges at a different level, including the use of systems approaches that better connect across fragments, such as disciplines, stakeholders, institutions, and technologies. By learning from progress in leading areas of health innovation (including oncology and AIDS), as well as complementary indications (Alzheimer's disease), I try to extract the most enabling innovation paradigms, and discuss their extension to additional areas of application within a systems approach. To facilitate such work, a Precision, P4 or Systems Medicine platform is proposed, which is centered on the representation of health states that enable the definition of time in the vision to provide the right intervention for the right patient at the right time and dose. Modeling of such health states should allow iterative optimization, as longitudinal human data accumulate. This platform is designed to facilitate the discovery of links between opportunities related to a) the modernization of diagnosis, including the increased use of omics profiling, b) patient-centric approaches enabled by technology convergence, including digital health and connected devices, c) increasing understanding of the pathobiological, clinical and health economic aspects of disease progression stages, d) design of new interventions, including therapies as well as preventive measures, including sequential intervention approaches. Probabilistic Markov models of health states, e.g. those used for health economic analysis, are discussed as a simple starting point for the platform. A path towards extension into other indications, data types and uses is discussed, with a focus on regenerative medicine and relevant pathobiology.

Exploring Glucocorticoid Receptor Agonists Mechanism of Action Through Mass Cytometry and Radial Visualizations.

Recent advances in combining flow cytometry and mass spectrometry have led to the development of mass cytometry, allowing for the interrogation of complex cell populations on an unprecedented scale. The volumes and high dimensionality of mass cytometry data pose significant challenges in terms of analysis and visualization. We implement a method called Radviz, where multidimensional single cell data can be visualized as a projection that maintains the original dimensions and data complexity whilst facilitating analysis and visualization. This enables identification of changes in populations, focusing the analysis on the most relevant aspect of large multidimensional datasets. To highlight the potential of Radviz, we profiled peripheral mononuclear blood cells (PBMCs) from three healthy donors and showed donor-specific differences in the number and composition of cell populations. In a second study, we explored the anti-inflammatory effects of two glucocorticoid receptor (GR) ligands (cpd6 and cpd11) compared to dexamethasone (Dex) on human primary macrophages. Standard analysis at the population level showed that cpd6 and cpd11 have an overall anti-inflammatory profile similar to that of Dex. CyTOF profiling and Radviz-driven analysis at the single cell level confirmed this observation, and identified a concentration-dependent effect of cpd6 that was not detected at the population level. Altogether, Radviz combines the strengths of a projection method, reducing the dimensionality of datasets, with that of a scatter plot, where the identity of each point can be inferred from the distance to the axis. This enables the visual exploration, analysis, and interpretation of complex, high dimensional data. © 2016 International Clinical Cytometry Society.

Calcium-activated chloride channel ANO1 promotes breast cancer progression by activating EGFR and CAMK signaling.

The calcium-activated chloride channel anoctamin 1 (ANO1) is located within the 11q13 amplicon, one of the most frequently amplified chromosomal regions in human cancer, but its functional role in tumorigenesis has remained unclear. The 11q13 region is amplified in ∼15% of breast cancers. Whether ANO1 is amplified in breast tumors, the extent to which gene amplification contributes to ANO1 overexpression, and whether overexpression of ANO1 is important for tumor maintenance have remained unknown. We have found that ANO1 is amplified and highly expressed in breast cancer cell lines and primary tumors. Amplification of ANO1 correlated with disease grade and poor prognosis. Knockdown of ANO1 in ANO1-amplified breast cancer cell lines and other cancers bearing 11q13 amplification inhibited proliferation, induced apoptosis, and reduced tumor growth in established cancer xenografts. Moreover, ANO1 chloride channel activity was important for cell viability. Mechanistically, ANO1 knockdown or pharmacological inhibition of its chloride-channel activity reduced EGF receptor (EGFR) and calmodulin-dependent protein kinase II (CAMKII) signaling, which subsequently attenuated AKT, v-src sarcoma viral oncogene homolog (SRC), and extracellular signal-regulated kinase (ERK) activation in vitro and in vivo. Our results highlight the involvement of the ANO1 chloride channel in tumor progression and provide insights into oncogenic signaling in human cancers with 11q13 amplification, thereby establishing ANO1 as a promising target for therapy in these highly prevalent tumor types.

Protein sequence databases.

Protein sequence databases do not contain just the sequence of the protein itself but also annotation that reflects our knowledge of its function and contributing residues. In this chapter, we will discuss various public protein sequence databases, with a focus on those that are generally applicable. Special attention is paid to issues related to the reliability of both sequence and annotation, as those are fundamental to many questions researchers will ask. Using both well-annotated and scarcely annotated human proteins as examples, it will be shown what information about the targets can be collected from freely available Internet resources and how this information can be used. The results are shown to be summarized in a simple graphical model of the protein's sequence architecture highlighting its structural and functional modules.

ProteinArchitect: protein evolution above the sequence level.

BACKGROUND: While many authors have discussed models and tools for studying protein evolution at the sequence level, molecular function is usually mediated by complex, higher order features such as independently folding domains and linear motifs that are based on or embedded in a particular arrangment of features such as secondary structure elements, transmembrane domains and regions with intrinsic disorder. This 'protein architecture' can, in its most simplistic representation, be visualized as domain organization cartoons that can be used to compare proteins in terms of the order of their mostly globular domains. METHODOLOGY: Here, we describe a visual approach and a webserver for protein comparison that extend the domain organization cartoon concept. By developing an information-rich, compact visualization of different protein features above the sequence level, potentially related proteins can be compared at the level of propensities for secondary structure, transmembrane domains and intrinsic disorder, in addition to PFAM domains. A public Web server is available at www.proteinarchitect.net, while the code is provided at protarchitect.sourceforge.net. CONCLUSIONS/SIGNIFICANCE: Due to recent advances in sequencing technologies we are now flooded with millions of predicted proteins that await comparative analysis. In many cases, mature tools focused on revealing hits with considerable global or local similarity to well-characterized proteins will not be able to lead us to testable hypotheses about a protein's function, or the function of a particular region. The visual comparison of different types of protein features with ProteinArchitect will be useful when assessing the relevance of similarity search hits, to discover subgroups in protein families and superfamilies, and to understand protein regions with conserved features outside globular regions. Therefore, this approach is likely to help researchers to develop testable hypotheses about a protein's function even if is somewhat distant from the more characterized proteins, by facilitating the discovery of features that are conserved above the sequence level for comparison and further experimental investigation.

Lineage-specific polycomb targets and de novo DNA methylation define restriction and potential of neuronal progenitors.

Cellular differentiation entails loss of pluripotency and gain of lineage- and cell-type-specific characteristics. Using a murine system that progresses from stem cells to lineage-committed progenitors to terminally differentiated neurons, we analyzed DNA methylation and Polycomb-mediated histone H3 methylation (H3K27me3). We show that several hundred promoters, including pluripotency and germline-specific genes, become DNA methylated in lineage-committed progenitor cells, suggesting that DNA methylation may already repress pluripotency in progenitor cells. Conversely, we detect loss and acquisition of H3K27me3 at additional targets in both progenitor and terminal states. Surprisingly, many neuron-specific genes that become activated upon terminal differentiation are Polycomb targets only in progenitor cells. Moreover, promoters marked by H3K27me3 in stem cells frequently become DNA methylated during differentiation, suggesting context-dependent crosstalk between Polycomb and DNA methylation. These data suggest a model how de novo DNA methylation and dynamic switches in Polycomb targets restrict pluripotency and define the developmental potential of progenitor cells.

Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome.

To gain insight into the function of DNA methylation at cis-regulatory regions and its impact on gene expression, we measured methylation, RNA polymerase occupancy and histone modifications at 16,000 promoters in primary human somatic and germline cells. We find CpG-poor promoters hypermethylated in somatic cells, which does not preclude their activity. This methylation is present in male gametes and results in evolutionary loss of CpG dinucleotides, as measured by divergence between humans and primates. In contrast, strong CpG island promoters are mostly unmethylated, even when inactive. Weak CpG island promoters are distinct, as they are preferential targets for de novo methylation in somatic cells. Notably, most germline-specific genes are methylated in somatic cells, suggesting additional functional selection. These results show that promoter sequence and gene function are major predictors of promoter methylation states. Moreover, we observe that inactive unmethylated CpG island promoters show elevated levels of dimethylation of Lys4 of histone H3, suggesting that this chromatin mark may protect DNA from methylation.