How exposure to chronic stress contributes to the development of type 2 diabetes: A complexity science approach.

Chronic stress contributes to the onset of type 2 diabetes (T2D), yet the underlying etiological mechanisms are not fully understood. Responses to stress are influenced by earlier experiences, sex, emotions and cognition, and involve a complex network of neurotransmitters and hormones, that affect multiple biological systems. In addition, the systems activated by stress can be altered by behavioral, metabolic and environmental factors. The impact of stress on metabolic health can thus be considered an emergent process, involving different types of interactions between multiple variables, that are driven by non-linear dynamics at different spatiotemporal scales. To obtain a more comprehensive picture of the links between chronic stress and T2D, we followed a complexity science approach to build a causal loop diagram (CLD) connecting the various mediators and processes involved in stress responses relevant for T2D pathogenesis. This CLD could help develop novel computational models and formulate new hypotheses regarding disease etiology.

Embracing complexity in sepsis.

Sepsis involves the dynamic interplay between a pathogen, the host response, the failure of organ systems, medical interventions and a myriad of other factors. This together results in a complex, dynamic and dysregulated state that has remained ungovernable thus far. While it is generally accepted that sepsis is very complex indeed, the concepts, approaches and methods that are necessary to understand this complexity remain underappreciated. In this perspective we view sepsis through the lens of complexity theory. We describe the concepts that support viewing sepsis as a state of a highly complex, non-linear and spatio-dynamic system. We argue that methods from the field of complex systems are pivotal for a fuller understanding of sepsis, and we highlight the progress that has been made over the last decades in this respect. Still, despite these considerable advancements, methods like computational modelling and network-based analyses continue to fly under the general scientific radar. We discuss what barriers contribute to this disconnect, and what we can do to embrace complexity with regards to measurements, research approaches and clinical applications. Specifically, we advocate a focus on longitudinal, more continuous biological data collection in sepsis. Understanding the complexity of sepsis will require a huge multidisciplinary effort, in which computational approaches derived from complex systems science must be supported by, and integrated with, biological data. Such integration could finetune computational models, guide validation experiments, and identify key pathways that could be targeted to modulate the system to the benefit of the host. We offer an example for immunological predictive modelling, which may inform agile trials that could be adjusted throughout the trajectory of disease. Overall, we argue that we should expand our current mental frameworks of sepsis, and embrace nonlinear, system-based thinking in order to move the field forward.

Scar formation from the perspective of complexity science: a new look at the biological system as a whole.

A burn wound is a complex systemic disease at multiple levels. Current knowledge of scar formation after burn injury has come from traditional biological and clinical studies. These are normally focused on just a small part of the entire process, which has limited our ability to sufficiently understand the underlying mechanisms and to predict systems behaviour. Scar formation after burn injury is a result of a complex biological system-wound healing. It is a part of a larger whole. In this self-organising system, many components form networks of interactions with each other. These networks of interactions are typically non-linear and change their states dynamically, responding to the environment and showing emergent long-term behaviour. How molecular and cellular data relate to clinical phenomena, especially regarding effective therapies of burn wounds to achieve minimal scarring, is difficult to unravel and comprehend. Complexity science can help bridge this gap by integrating small parts into a larger whole, such that relevant biological mechanisms and data are combined in a computational model to better understand the complexity of the entire biological system. A better understanding of the complex biological system of post-burn scar formation could bring research and treatment regimens to the next level. The aim of this review/position paper is to create more awareness of complexity in scar formation after burn injury by describing the basic principles of complexity science and its potential for burn care professionals.

New Approach to Privacy-Preserving Clinical Decision Support Systems for HIV Treatment.

BACKGROUND: HIV treatment prescription is a complex process. Clinical decision support systems (CDSS) are a category of health information technologies that can assist clinicians to choose optimal treatments based on clinical trials and expert knowledge. The usability of some CDSSs for HIV treatment would be significantly improved by using the knowledge obtained by treating other patients. This knowledge, however, is mainly contained in patient records, whose usage is restricted due to privacy and confidentiality constraints. METHODS: A treatment effectiveness measure, containing valuable information for HIV treatment prescription, was defined and a method to extract this measure from patient records was developed. This method uses an advanced cryptographic technology, known as secure Multiparty Computation (henceforth referred to as MPC), to preserve the privacy of the patient records and the confidentiality of the clinicians' decisions. FINDINGS: Our solution enables to compute an effectiveness measure of an HIV treatment, the average time-to-treatment-failure, while preserving privacy. Experimental results show that our solution, although at proof-of-concept stage, has good efficiency and provides a result to a query within 24 min for a dataset of realistic size. INTERPRETATION: This paper presents a novel and efficient approach HIV clinical decision support systems, that harnesses the potential and insights acquired from treatment data, while preserving the privacy of patient records and the confidentiality of clinician decisions.

Convection and the Extracellular Matrix Dictate Inter- and Intra-Biofilm Quorum Sensing Communication in Environmental Systems.

The mechanisms and impact of bacterial quorum sensing (QS) for the coordination of population-level behaviors are well studied under laboratory conditions. However, it is unclear how, in otherwise open environmental systems, QS signals accumulate to sufficient concentration to induce QS phenotypes, especially when quorum quenching (QQ) organisms are also present. We explore the impact of QQ activity on QS signaling in spatially organized biofilms in scenarios that mimic open systems of natural and engineered environments. Using a functionally differentiated biofilm system, we show that the extracellular matrix, local flow, and QQ interact to modulate communication. In still aqueous environments, convection facilitates signal dispersal while the matrix absorbs and relays signals to the cells. This process facilitates inter-biofilm communication even at low extracellular signal concentrations. Within the biofilm, the matrix further regulates the transport of the competing QS and QQ molecules, leading to heterogenous QS behavior. Importantly, only extracellular QQ enzymes can effectively control QS signaling, suggesting that the intracellular QQ enzymes may not have evolved to degrade environmental QS signals for competition.

The impact of pace of life on pedestrian heat stress: A computational modelling approach.

Elevated walking speed is an indicator of increased pace of life in cities, caused by environmental pressures inherent to urban environments, which lead to short- and long-term consequences for health and well-being. In this paper we investigate the effect of walking speed on heat stress. We define the heat-stress-optimal walking speed and estimate its values for a wide range of air temperatures with the use of computational modelling of metabolic heat production and thermal regulation. The heat-stress-optimal walking speed shows three distinct phases in relation to air temperature, determined by different modes of interaction between the environment and physiology. Simulation results suggest that different temperature regimes require walking speed adaptation to preserve heat balance. Empirical data collected for Singapore reveals elevated average walking speed, which is not responsive to slight changes in microclimate (4-5 °C). The proposed computational model predicts the amount of additional heat produced by an individual due to the high pace of life. We conclude that there are direct implications of the high pace of life in cities on the immediate heat stress of people, and we show how a lower walking speed significantly reduces self-overheating and improves thermal comfort.

Medication management support in diabetes: a systematic assessment of diabetes self-management apps.

BACKGROUND: Smartphone apps are becoming increasingly popular for supporting diabetes self-management. A key aspect of diabetes self-management is appropriate medication-taking. This study aims to systematically assess and characterise the medication management features in diabetes self-management apps and their congruence with best-practice evidence-based criteria. METHODS: The Google Play and Apple app stores were searched in June 2018 using diabetes-related terms in the English language. Apps with both medication and blood glucose management features were downloaded and evaluated against assessment criteria derived from international medication management and diabetes guidelines. RESULTS: Our search yielded 3369 Android and 1799 iOS potentially relevant apps; of which, 143 apps (81 Android, 62 iOS) met inclusion criteria and were downloaded and assessed. Over half 58.0% (83/143) of the apps had a medication reminder feature; 16.8% (24/143) had a feature to review medication adherence; 39.9% (57/143) allowed entry of medication-taking instructions; 5.6% (8/143) provided information about medication; and 4.2% (6/143) displayed motivational messages to encourage medication-taking. Only two apps prompted users on the use of complementary medicine. Issues such as limited medication logging capacity, faulty reminder features, unclear medication adherence assessment, and visually distracting excessive advertising were observed during app assessments. CONCLUSIONS: A large proportion of diabetes self-management apps lacked features for enhancing medication adherence and safety. More emphasis should be given to the design of medication management features in diabetes apps to improve their alignment to evidence-based best practice.

eHealth in the future of medications management: personalisation, monitoring and adherence.

BACKGROUND: Globally, healthcare systems face major challenges with medicines management and medication adherence. Medication adherence determines medication effectiveness and can be the single most effective intervention for improving health outcomes. In anticipation of growth in eHealth interventions worldwide, we explore the role of eHealth in the patients' medicines management journey in primary care, focusing on personalisation and intelligent monitoring for greater adherence. DISCUSSION: eHealth offers opportunities to transform every step of the patient's medicines management journey. From booking appointments, consultation with a healthcare professional, decision-making, medication dispensing, carer support, information acquisition and monitoring, to learning about medicines and their management in daily life. It has the potential to support personalisation and monitoring and thus lead to better adherence. For some of these dimensions, such as supporting decision-making and providing reminders and prompts, evidence is stronger, but for many others more rigorous research is urgently needed. CONCLUSIONS: Given the potential benefits and barriers to eHealth in medicines management, a fine balance needs to be established between evidence-based integration of technologies and constructive experimentation that could lead to a game-changing breakthrough. A concerted, transdisciplinary approach adapted to different contexts, including low- and middle-income contries is required to realise the benefits of eHealth at scale.

Promoter sequence determines the relationship between expression level and noise.

The ability of cells to accurately control gene expression levels in response to extracellular cues is limited by the inherently stochastic nature of transcriptional regulation. A change in transcription factor (TF) activity results in changes in the expression of its targets, but the way in which cell-to-cell variability in expression (noise) changes as a function of TF activity, and whether targets of the same TF behave similarly, is not known. Here, we measure expression and noise as a function of TF activity for 16 native targets of the transcription factor Zap1 that are regulated by it through diverse mechanisms. For most activated and repressed Zap1 targets, noise decreases as expression increases. Kinetic modeling suggests that this is due to two distinct Zap1-mediated mechanisms that both change the frequency of transcriptional bursts. Notably, we found that another mechanism of repression by Zap1, which is encoded in the promoter DNA, likely decreases the size of transcriptional bursts, producing a unique transcriptional state characterized by low expression and low noise. In addition, we find that further reduction in noise is achieved when a single TF both activates and represses a single target gene. Our results suggest a global principle whereby at low TF concentrations, the dominant source of differences in expression between promoters stems from differences in burst frequency, whereas at high TF concentrations differences in burst size dominate. Taken together, we show that the precise amount by which noise changes with expression is specific to the regulatory mechanism of transcription and translation that acts at each gene.

The influence of memory on indoor environment exploration: A numerical study.

Understanding human behavior in the context of exploration and navigation is an important but challenging problem. Such understanding can help in the design of safe structures and spaces that implicitly aid humans during evacuation or other emergency situations. In particular, the role that memory plays in this process is something that is crucial to understand. In this paper, we develop a novel serious game-based experimental approach to understanding the non-randomness and the impact of memory on the human exploration process. We show that a simple memory model, with a depth of between 6 and 8 steps, is sufficient to approximate a 'human-like' level of exploration efficiency. We also demonstrate the advantages that a game-based experimental methodology brings to these kinds of experiments in the amount of data that can be collected as compared to traditional experiments. We feel that these findings have important implications for 'safety-by-design' in complex infrastructural structures.

A novel feature-based approach to extract drug-drug interactions from biomedical text.

MOTIVATION: Knowledge of drug-drug interactions (DDIs) is crucial for health-care professionals to avoid adverse effects when co-administering drugs to patients. As most newly discovered DDIs are made available through scientific publications, automatic DDI extraction is highly relevant. RESULTS: We propose a novel feature-based approach to extract DDIs from text. Our approach consists of three steps. First, we apply text preprocessing to convert input sentences from a given dataset into structured representations. Second, we map each candidate DDI pair from that dataset into a suitable syntactic structure. Based on that, a novel set of features is used to generate feature vectors for these candidate DDI pairs. Third, the obtained feature vectors are used to train a support vector machine (SVM) classifier. When evaluated on two DDI extraction challenge test datasets from 2011 and 2013, our system achieves F-scores of 71.1% and 83.5%, respectively, outperforming any state-of-the-art DDI extraction system. AVAILABILITY AND IMPLEMENTATION: The source code is available for academic use at http://www.biosemantics.org/uploads/DDI.zip.

RegaDB: community-driven data management and analysis for infectious diseases.

SUMMARY: RegaDB is a free and open source data management and analysis environment for infectious diseases. RegaDB allows clinicians to store, manage and analyse patient data, including viral genetic sequences. Moreover, RegaDB provides researchers with a mechanism to collect data in a uniform format and offers them a canvas to make newly developed bioinformatics tools available to clinicians and virologists through a user friendly interface. AVAILABILITY AND IMPLEMENTATION: Source code, binaries and documentation are available on http://rega.kuleuven.be/cev/regadb. RegaDB is written in the Java programming language, using a web-service-oriented architecture.

Time-frequency methods for structural health monitoring.

Detection of early warning signals for the imminent failure of large and complex engineered structures is a daunting challenge with many open research questions. In this paper we report on novel ways to perform Structural Health Monitoring (SHM) of flood protection systems (levees, earthen dikes and concrete dams) using sensor data. We present a robust data-driven anomaly detection method that combines time-frequency feature extraction, using wavelet analysis and phase shift, with one-sided classification techniques to identify the onset of failure anomalies in real-time sensor measurements. The methodology has been successfully tested at three operational levees. We detected a dam leakage in the retaining dam (Germany) and "strange" behaviour of sensors installed in a Boston levee (UK) and a Rhine levee (Germany).

Protocol for analyzing emergence dynamics of diabetes with obesity using numerical continuation and bifurcation analysis.

Type 2 diabetes (T2D) is a multifactorial disease that slowly and inconspicuously progresses over years. Here, we present a protocol for analyzing slow progression dynamics of T2D with obesity. We describe steps for using software to exploit the differences between the timescales of the metabolic variables and using numerical continuation and bifurcation analysis. We detail procedures to analyze bi-stable system dynamics and identify the thresholds that separate healthy and diabetic states. For complete details on the use and execution of this protocol, please refer to Yildirim et al. (2023).1.

An in silico modeling approach to understanding the dynamics of the post-burn immune response.

Introduction: Burns are characterized by a massive and prolonged acute inflammation, which persists for up to months after the initial trauma. Due to the complexity of the inflammatory process, Predicting the dynamics of wound healing process can be challenging for burn injuries. The aim of this study was to develop simulation models for the post-burn immune response based on (pre)clinical data. Methods: The simulation domain was separated into blood and tissue compartments. Each of these compartments contained solutes and cell agents. Solutes comprise pro-inflammatory cytokines, anti-inflammatory cytokines and inflammation triggering factors. The solutes diffuse around the domain based on their concentration profiles. The cells include mast cells, neutrophils, and macrophages, and were modeled as independent agents. The cells are motile and exhibit chemotaxis based on concentrations gradients of the solutes. In addition, the cells secrete various solutes that in turn alter the dynamics and responses of the burn wound system. Results: We developed an Glazier-Graner-Hogeweg method-based model (GGH) to capture the complexities associated with the dynamics of inflammation after burn injuries, including changes in cell counts and cytokine levels. Through simulations from day 0 - 4 post-burn, we successfully identified key factors influencing the acute inflammatory response, i.e., the initial number of endothelial cells, the chemotaxis threshold, and the level of chemoattractants. Conclusion: Our findings highlight the pivotal role of the initial endothelial cell count as a key parameter for intensity of inflammation and progression of acute inflammation, 0 - 4 days post-burn.

A robust approach to extract biomedical events from literature.

MOTIVATION: The abundance of biomedical literature has attracted significant interest in novel methods to automatically extract biomedical relations from the literature. Until recently, most research was focused on extracting binary relations such as protein-protein interactions and drug-disease relations. However, these binary relations cannot fully represent the original biomedical data. Therefore, there is a need for methods that can extract fine-grained and complex relations known as biomedical events. RESULTS: In this article we propose a novel method to extract biomedical events from text. Our method consists of two phases. In the first phase, training data are mapped into structured representations. Based on that, templates are used to extract rules automatically. In the second phase, extraction methods are developed to process the obtained rules. When evaluated against the Genia event extraction abstract and full-text test datasets (Task 1), we obtain results with F-scores of 52.34 and 53.34, respectively, which are comparable to the state-of-the-art systems. Furthermore, our system achieves superior performance in terms of computational efficiency. AVAILABILITY: Our source code is available for academic use at http://dl.dropbox.com/u/10256952/BioEvent.zip.

Superinfection with drug-resistant HIV is rare and does not contribute substantially to therapy failure in a large European cohort.

BACKGROUND: Superinfection with drug resistant HIV strains could potentially contribute to compromised therapy in patients initially infected with drug-sensitive virus and receiving antiretroviral therapy. To investigate the importance of this potential route to drug resistance, we developed a bioinformatics pipeline to detect superinfection from routinely collected genotyping data, and assessed whether superinfection contributed to increased drug resistance in a large European cohort of viremic, drug treated patients. METHODS: We used sequence data from routine genotypic tests spanning the protease and partial reverse transcriptase regions in the Virolab and EuResist databases that collated data from five European countries. Superinfection was indicated when sequences of a patient failed to cluster together in phylogenetic trees constructed with selected sets of control sequences. A subset of the indicated cases was validated by re-sequencing pol and env regions from the original samples. RESULTS: 4425 patients had at least two sequences in the database, with a total of 13816 distinct sequence entries (of which 86% belonged to subtype B). We identified 107 patients with phylogenetic evidence for superinfection. In 14 of these cases, we analyzed newly amplified sequences from the original samples for validation purposes: only 2 cases were verified as superinfections in the repeated analyses, the other 12 cases turned out to involve sample or sequence misidentification. Resistance to drugs used at the time of strain replacement did not change in these two patients. A third case could not be validated by re-sequencing, but was supported as superinfection by an intermediate sequence with high degenerate base pair count within the time frame of strain switching. Drug resistance increased in this single patient. CONCLUSIONS: Routine genotyping data are informative for the detection of HIV superinfection; however, most cases of non-monophyletic clustering in patient phylogenies arise from sample or sequence mix-up rather than from superinfection, which emphasizes the importance of validation. Non-transient superinfection was rare in our mainly treatment experienced cohort, and we found a single case of possible transmitted drug resistance by this route. We therefore conclude that in our large cohort, superinfection with drug resistant HIV did not compromise the efficiency of antiretroviral treatment.

Using network analysis to identify leverage points based on causal loop diagrams leads to false inference.

Network analysis is gaining momentum as an accepted practice to identify which factors in causal loop diagrams (CLDs)-mental models that graphically represent causal relationships between a system's factors-are most likely to shift system-level behaviour, known as leverage points. This application of network analysis, employed to quantitatively identify leverage points without having to use computational modelling approaches that translate CLDs into sets of mathematical equations, has however not been duly reflected upon. We evaluate whether using commonly applied network analysis metrics to identify leverage points is justified, focusing on betweenness- and closeness centrality. First, we assess whether the metrics identify the same leverage points based on CLDs that represent the same system but differ in inferred causal structure-finding that they provide unreliable results. Second, we consider conflicts between assumptions underlying the metrics and CLDs. We recognise six conflicts suggesting that the metrics are not equipped to take key information captured in CLDs into account. In conclusion, using betweenness- and closeness centrality to identify leverage points based on CLDs is at best premature and at worst incorrect-possibly causing erroneous identification of leverage points. This is problematic as, in current practice, the results can inform policy recommendations. Other quantitative or qualitative approaches that better correspond with the system dynamics perspective must be explored.

A data-driven computational model for obesity-driven diabetes onset and remission through weight loss.

Obesity is a major risk factor for the development of type 2 diabetes (T2D), where a sustained weight loss may result in T2D remission in individuals with obesity. To design effective and feasible intervention strategies to prevent or reverse T2D, it is imperative to study the progression of T2D and remission together. Unfortunately, this is not possible through experimental and observational studies. To address this issue, we introduce a data-driven computational model and use human data to investigate the progression of T2D with obesity and remission through weight loss on the same timeline. We identify thresholds for the emergence of T2D and necessary conditions for remission. We explain why remission is only possible within a window of opportunity and the way that window depends on the progression history of T2D, individual's metabolic state, and calorie restrictions. These findings can help to optimize therapeutic intervention strategies for T2D prevention or treatment.