Using geospatial mapping to predict and compare gambling harm hotspots in urban, rural and coastal areas of a large county in England.

Background: Disordered gambling is a public health problem with interconnections with health and social inequality, and adverse impacts on physical and mental health. Mapping technologies have been used to explore gambling in the UK, though most were based in urban locations.

Methods: We used routine data sources and geospatial mapping software to predict where gambling related harm would be most prevalent within a large English county, host to urban, rural and coastal communities.

Ambulance attendance for substance and/or alcohol use in a pandemic: Interrupted time series analysis of incidents.

Introduction: The ambulance attendance for substance and/or alcohol use in a pandemic (ASAP) study explores incidents during the COVID-19 lockdown in the East Midlands region of the United Kingdom (23 March-4 July 2020).

Method: Retrospective cross-sectional count per day of ambulance attendances from the East Midlands Ambulance Service Trust. Ambulance attendances relating to alcohol or other drug use in the year prior, during lockdown and weeks following, were examined using interrupted time series analysis by patient demographics and geographical location.

Alcohol and other substance use during the COVID-19 pandemic: A systematic review.

Background: Although evidence suggests substance and alcohol use may change during the Covid-19 pandemic there has been no full review of the evidence around this.

Methods: A systematic review of all available evidence was carried out to document and interpret the frequency and severity of alcohol and other substance use during the Covid-19 pandemic and their relationship to demographic and mental health variables that may suggest further clinical implications. Peer reviewed articles in MEDLINE, Embase, PsycINFO, CINAHL complete and Sociological Abstracts were searched from December 2019 until November 2020.

Self-monitoring for recurrence of secondary atrial fibrillation following non-cardiac surgery or acute illness: A pilot study.

Background: Atrial fibrillation (AF) secondary to non-cardiac surgery and medical illness is common and, although often transient, is associated with an increased risk of stroke and mortality. This pilot study tested the feasibility of self-monitoring to detect recurrent AF in this setting and the frequency with which it occurred.

Methods: Patients with new secondary AF after non-cardiac surgery or medical illness that reverted to sinus rhythm before discharge were recruited in three tertiary hospitals in Australia.

Integrating interactive computational modeling in biology curricula.

While the use of computer tools to simulate complex processes such as computer circuits is normal practice in fields like engineering, the majority of life sciences/biological sciences courses continue to rely on the traditional textbook and memorization approach. To address this issue, we explored the use of the Cell Collective platform as a novel, interactive, and evolving pedagogical tool to foster student engagement, creativity, and higher-level thinking. Cell Collective is a Web-based platform used to create and simulate dynamical models of various biological processes.

Modeling and simulation of the exposure-response and dropout pattern of guanfacine extended-release in pediatric patients with ADHD.

Guanfacine extended-release (GXR) is a selective α2A-adrenergic receptor agonist approved in the United States for once-daily administration for the treatment of attention-deficit hyperactivity disorder (ADHD) in children and adolescents ages 6-17 years old either as monotherapy or adjunctive to stimulant medications. This analysis integrates exposure-response, placebo, and dropout data from 10 clinical trials that used GXR in adolescents and children with ADHD. In these trials, the ADHD Rating Scale-IV (ADHD RS-IV) score was collected longitudinally within patients over the course of 6-13 weeks.

Sensitivity analysis of biological Boolean networks using information fusion based on nonadditive set functions.

Background: An algebraic method for information fusion based on nonadditive set functions is used to assess the joint contribution of Boolean network attributes to the sensitivity of the network to individual node mutations. The node attributes or characteristics under consideration are: in-degree, out-degree, minimum and average path lengths, bias, average sensitivity of Boolean functions, and canalizing degrees. The impact of node mutations is assessed using as target measure the average Hamming distance between a non-mutated/wild-type network and a mutated network.

A comprehensive, multi-scale dynamical model of ErbB receptor signal transduction in human mammary epithelial cells.

The non-receptor tyrosine kinase Src and receptor tyrosine kinase epidermal growth factor receptor (EGFR/ErbB1) have been established as collaborators in cellular signaling and their combined dysregulation plays key roles in human cancers, including breast cancer. In part due to the complexity of the biochemical network associated with the regulation of these proteins as well as their cellular functions, the role of Src in EGFR regulation remains unclear. Herein we present a new comprehensive, multi-scale dynamical model of ErbB receptor signal transduction in human mammary epithelial cells.

Bio-logic builder: a non-technical tool for building dynamical, qualitative models.

Computational modeling of biological processes is a promising tool in biomedical research. While a large part of its potential lies in the ability to integrate it with laboratory research, modeling currently generally requires a high degree of training in mathematics and/or computer science. To help address this issue, we have developed a web-based tool, Bio-Logic Builder, that enables laboratory scientists to define mathematical representations (based on a discrete formalism) of biological regulatory mechanisms in a modular and non-technical fashion.

Dynamics of influenza virus and human host interactions during infection and replication cycle.

The replication and life cycle of the influenza virus is governed by an intricate network of intracellular regulatory events during infection, including interactions with an even more complex system of biochemical interactions of the host cell. Computational modeling and systems biology have been successfully employed to further the understanding of various biological systems, however, computational studies of the complexity of intracellular interactions during influenza infection is lacking. In this work, we present the first large-scale dynamical model of the infection and replication cycle of influenza, as well as some of its interactions with the host's signaling machinery.

The Cell Collective: toward an open and collaborative approach to systems biology.

Background: Despite decades of new discoveries in biomedical research, the overwhelming complexity of cells has been a significant barrier to a fundamental understanding of how cells work as a whole. As such, the holistic study of biochemical pathways requires computer modeling. Due to the complexity of cells, it is not feasible for one person or group to model the cell in its entirety.

Personal health budgets: a new way of accessing complementary therapies?

The popularity and use of complementary and alternative therapies and medicines (CAM) has remained high in the UK and many other countries over at least the last two decades. Access to such modalities via publicly funded health and welfare systems has remained very limited over the same period. Personal health budgets, designed to offer significant control and personal choice over health care, offer a potential mechanism for some individuals to access publicly funded CAM treatments more directly.

ChemChains: a platform for simulation and analysis of biochemical networks aimed to laboratory scientists.

Background: New mathematical models of complex biological structures and computer simulation software allow modelers to simulate and analyze biochemical systems in silico and form mathematical predictions. Due to this potential predictive ability, the use of these models and software has the possibility to compliment laboratory investigations and help refine, or even develop, new hypotheses. However, the existing mathematical modeling techniques and simulation tools are often difficult to use by laboratory biologists without training in high-level mathematics, limiting their use to trained modelers.

Emergent decision-making in biological signal transduction networks.

The complexity of biochemical intracellular signal transduction networks has led to speculation that the high degree of interconnectivity that exists in these networks transforms them into an information processing network. To test this hypothesis directly, a large scale model was created with the logical mechanism of each node described completely to allow simulation and dynamical analysis. Exposing the network to tens of thousands of random combinations of inputs and analyzing the combined dynamics of multiple outputs revealed a robust system capable of clustering widely varying input combinations into equivalence classes of biologically relevant cellular responses.

Double-blind, randomized, placebo-controlled study of high-dose HMG CoA reductase inhibitor therapy on ventricular remodeling, pro-inflammatory cytokines and neurohormonal parameters in patients with chronic systolic heart failure.

Background: Statins decrease mortality in patients with coronary artery disease. However, chronic heart failure (CHF) patients were often excluded in such trials. Statins possess pharmacologic properties (independent of cholesterol lowering) that may be beneficial on ventricular remodeling in such patients.

Scalar equations for synchronous Boolean networks with biological applications.

One way of coping with the complexity of biological systems is to use the simplest possible models which are able to reproduce at least some nontrivial features of reality. Although two value Boolean models have a long history in technology, it is perhaps a little bit surprising that they can also represent important features of living organizms. In this paper, the scalar equation approach to Boolean network models is further developed and then applied to two interesting biological models.