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Abstract Background Good Samaritan Laws are a harm reduction policy intended to facilitate a reduction in fatal opioid overdoses by enabling bystanders, first responders, and health care providers to assist individuals experiencing an overdose without facing civil or criminal liability. However, Good Samaritan Laws may not be reaching their full impact in many communities due to a lack of knowledge of protections under these laws, distrust in law enforcement, and fear of legal consequences among potential bystanders. The purpose of this study was to develop a systems-level understanding of the factors influencing bystander responses to opioid overdose in the context of Connecticut’s Good Samaritan Laws and identify high-leverage policies for improving opioid-related outcomes and implementation of these laws in Connecticut (CT). Methods We conducted six group model building (GMB) workshops that engaged a diverse set of participants with medical and community expertise and lived bystander experience. Through an iterative, stakeholder-engaged process, we developed, refined, and validated a qualitative system dynamics (SD) model in the form of a causal loop diagram (CLD). Results Our resulting qualitative SD model captures our GMB participants’ collective understanding of the dynamics driving bystander behavior and other factors influencing the effectiveness of Good Samaritan Laws in the state of CT. In this model, we identified seven balancing (B) and eight reinforcing (R) feedback loops within four narrative domains: Narrative 1 - Overdose, Calling 911, and First Responder Burnout; Narrative 2 - Naloxone Use, Acceptability, and Linking Patients to Services; Narrative 3 - Drug Arrests, Belief in Good Samaritan Laws, and Community Trust in Police; and Narrative 4 - Bystander Naloxone Use, Community Participation in Harm Reduction, and Cultural Change Towards Carrying Naloxone. Conclusions Our qualitative SD model brings a nuanced systems perspective to the literature on bystander behavior in the context of Good Samaritan Laws. Our model, grounded in local knowledge and experience, shows how the hypothesized non-linear interdependencies of the social, structural, and policy determinants of bystander behavior collectively form endogenous feedback loops that can be leveraged to design policies to advance and sustain systems change.

Many public health challenges are characterized by complexity that reflects the dynamic systems in which they occur. Such systems involve multiple interdependent factors, actors, and sectors that influence health, and are a primary driver of challenges of insufficient implementation, sustainment, and scale of evidence-based public health interventions. Implementation science frameworks have been developed to help embed evidence-based interventions in diverse settings and identify key factors that facilitate or hinder implementation. These frameworks are largely static in that they do not explain the nature and dynamics of interrelationships among the identified determinants, nor how those determinants might change over time. Furthermore, most implementation science frameworks are top-down, deterministic, and linear, leaving critical gaps in understanding of both how to intervene on determinants of successful implementation and how to scale evidence-based solutions. Design thinking and systems science offer methods for transforming this problem-oriented paradigm into one that is solution-oriented. This article describes these two approaches and how they can be integrated into implementation science strategies to promote implementation, sustainment, and scaling of public health innovation, ultimately resulting in transformative systems changes that improve population health.

Abstract Background Although Good Samaritan laws (GSLs) have been widely adopted throughout the United States, their efficacy in individual states is often unknown. This paper offers an approach for assessing the impact of GSLs and insight for policy-makers and public health officials who wish to know whether they should expect to see outcomes from similar policy interventions. Methods Utilizing a system dynamics (SD) modeling approach, the research team conducted a policy evaluation to determine the impact of GSLs on opioid use disorder (OUD) in Connecticut and evaluated the GSL based upon the following health outcomes: (1) emergency department (ED) visits for overdose, (2) behavioral changes of bystanders, and (3) overdose deaths. Results The simulation model suggests that Connecticut’s GSL has not yet affected overdose deaths but has resulted in bystander behavioral changes, such as increased 911 calls for overdose. ED visits have increased as the number of opioid users has increased. Conclusions The simulation results indicate that the number of opioid-related deaths will continue to increase and that the GSL alone cannot effectively control the crisis. However, the SD approach that was used will allow policymakers to evaluate the effectiveness of the GSL over time using a simulation framework. This SD model demonstrates great potential by producing simulations that allow policymakers to assess multiple strategies for combating the opioid crisis and select optimal public health interventions.

Abstract Background Over 1.7 million episodes of agitation occur annually across the United States in emergency departments (EDs), some of which lead to workplace assaults on clinicians and require invasive methods like physical restraints to maintain staff and patient safety. Recent studies demonstrated that experiences of workplace violence contribute to symptoms of burnout, which may impact future decisions regarding use of physical restraints on agitated patients. To capture the dynamic interactions between clinicians and agitated patients under their care, we applied qualitative system dynamics methods to develop a model that describes feedback mechanisms of clinician burnout and the use of physical restraints to manage agitation. Methods We convened an interprofessional panel of clinician stakeholders and agitation experts for a series of model building sessions to develop the current model. The panel derived the final version of our model over ten sessions of iterative refinement and modification, each lasting approximately three to four hours. We incorporated findings from prior studies on agitation and burnout related to workplace violence, identifying interpersonal and psychological factors likely to influence our outcomes of interest to form the basis of our model. Results The final model resulted in five main sets of feedback loops that describe key narratives regarding the relationship between clinician burnout and agitated patients becoming physically restrained: (1) use of restraints decreases agitation and risk of assault, leading to increased perceptions of safety and decreasing use of restraints in a balancing feedback loop which stabilizes the system; (2) clinician stress leads to a perception of decreased safety and lower threshold to restrain, causing more stress in a negatively reinforcing loop; (3) clinician burnout leads to a decreased perception of colleague support which leads to more burnout in a negatively reinforcing loop; (4) clinician burnout leads to negative perceptions of patient intent during agitation, thus lowering threshold to restrain and leading to higher task load, more likelihood of workplace assaults, and higher burnout in a negatively reinforcing loop; and (5) mutual trust between clinicians causes increased perceptions of safety and improved team control, leading to decreased clinician stress and further increased mutual trust in a positively reinforcing loop. Conclusions Our system dynamics approach led to the development of a robust qualitative model that illustrates a number of important feedback cycles that underly the relationships between clinician experiences of workplace violence, stress and burnout, and impact on decisions to physically restrain agitated patients. This work identifies potential opportunities at multiple targets to break negatively reinforcing cycles and support positive influences on safety for both clinicians and patients in the face of physical danger.

The relationship between financial development and energy intensity is yet to be firmly established as the literature is newly emerging, and the few empirical studies that have been conducted provide conflicting results. While some conclude that there is a U-shaped relationship between financial development and energy intensity, others show a linear relationship between the two variables. This study investigates the relationship between financial development and energy intensity by focusing on the role of Islamic financial development. It covers 30 countries where Islamic banks are present. Using the fixed-effects panel model, the empirical results suggest that Islamic banking development significantly increases energy intensity in the sample countries. We also identify other important factors that increase it. These include carbon emissions, renewable energy use and energy imports. The findings point to the importance of designing policies to incentivise Islamic banks and Shari'ah-compliant investors to finance clean energy technologies as a potent tool for reducing energy intensity, achieving sustainable development, and greening Islamic finance.

BackgroundInjection drug use (IDU) is the leading risk factor for hepatitis C virus (HCV) transmission in the U.S. While the general risk factors for HCV transmission are known, there is limited work on how these factors interact and impact young people who inject drugs (YPWID).MethodsProject data were drawn from a study of 539 New York City (NYC) residents ages 18-29 who were recruited via Respondent-Driven Sampling and, reported past-month non-medical use of prescription opioids and/or heroin. Analyses are based on a subsample of 337 (62%) who reported injecting any drug in the past 12 months. All variables were assessed via self-report, except HCV status, which was established via rapid antibody testing. Integrating the observed statistical associations with extant literature on HCV risk, we also developed a qualitative system dynamics (SD) model to use as a supplemental data visualization tool to explore plausible pathways and interactions among key risk and protective factors for HCV.ResultsResults showed a 31% HCV antibody prevalence with an overall incidence of 10 per 100 person-years. HCV status was independently correlated with having shared cookers with two or more people (AOR = 2.17); injected drugs 4–6 years (AOR = 2.49) and 7 or more years (AOR = 4.95); lifetime homelessness (AOR = 2.52); and having been incarcerated two or more times (AOR = 1.99). These outcomes along with the extant literature on HCV risk were used to develop the qualitative SD model, which describes a causal hypothesis around non-linearities and feedback loop structures underlying the spread of HCV among YPWID.ConclusionsDespite ongoing harm reduction efforts, close to a third of YPWID in the community sample have been exposed to HCV, have risks for injection drug use, and face challenges with structural factors that may be preventing adequate intervention. The qualitative SD model explores these issues and contributes to a better understanding of how these various risk factors interact and what policies could potentially be effective in reducing HCV infections.

Diet and exercise are known to influence mental health. However, the interaction between diet, dietary practices, and exercise and its impact on the mood of young adults (YA) is poorly understood. YA are inherently at risk for mental distress. They tend to consume a low-quality diet and are generally active. The purpose of the study was to assess these relationships through validating causal loop diagrams (CLD) that describe these connections by using a system dynamic (SD) modeling methodology. Adults 18–29 years were invited to complete the Food-Mood questionnaire. The anonymous questionnaire link was distributed to several institutional listservs and via several social media platforms targeting young adults. A multi-level analysis, including machine learning techniques, was used to assess these relationships. The key findings were then built into gender based CLD, which suggest that a differential repertoire may be needed to optimize diet quality, exercise, and mental well-being. Additionally, a potential net threshold for dietary factors and exercise may be needed to achieve mental well-being in young adults. Moreover, our findings suggest that exercise may boost the enhancing effect of food groups on mental well-being and may lessen the negative impact of dietary impediments of mental well-being.

The second European Rhinology Research Forum organized by the European Forum for Research and Education in Allergy and Airway Diseases (EUFOREA) was held on 9-10th November 2017, combined with a specific symposium on air pollution and mobile Health technology (mHealth) with the GARD (Global Alliance against Chronic Respiratory Diseases) initiative of WHO (World Health Organization). Physicians from different specialties, researchers, as well as patients and industry representatives from more than 40 countries took part in the Forum. Relevant topics were debated with the aim of allowing the implementation of precision medicine (PM) in daily respiratory care. All debates started with positioning the current state of the art: identification of current gaps in practice, the current consensus and the need for implementation of novel approaches such as endotype-driven treatment, patient empowerment and eHealth tools. This report provides a summary of the outcomes of the brainstorming sessions of the European Rhinology Research Forum 2017, highlighting the research needs in PM, with personalized care, prediction of success of treatment, participation of the patient and prevention of disease as key drivers for improving current clinical practice.

ABSTRACT Objectives As estimated there are about 5.3 million who suffer from Alzheimer’s disease in United States. The incidence is increasing as the population is aging. Due to the increasing trend of Alzheimer’s disease, there is a lot of discussion on prevention efforts or slowing the incidence. Also, models that could predict individual risk of cognitive impairment are needed to assist in prevention efforts. In general dementia development has been associated with growth in various vascular, lifestyle and other risk factors. Epidemiological research provides evidence of some vascular, lifestyle and psychological risk factors that are modifiable and protective of disease incidence either independently or while interacting with other factors. However, as reported by National Institute of Aging, it is not yet clear whether health or lifestyle factors can prevent Alzheimer’s disease. The objective of this research project is to adopt a system dynamics modeling approach to study the interaction of several key factors including vascular, lifestyle and psychological aspects over the life course of individuals, to gain further understanding of Alzheimer’s disease incidence and evaluate prevention strategies. Both datasets of ‘Alzheimer's Disease Neuroimaging Initiative (ADNI)’ and ‘Health and Retirement Study (HRS)’ will be used for model development and validation. Approach A system dynamics approach is an optimal choice for addressing the goal of this proposal because different key factors interact over time and make Alzheimer’s disease incidence a complex problem. Furthermore, system dynamics approaches focus on understanding the relationship between the structure of a system and the resulting dynamic behaviors generated through multiple interacting feedback loops. Such an approach could be invaluable in studying dynamic problems arising in complex health, social, economic, or ecological systems. Results For the purpose of the proposal, the following stages are planned: 1. Develop a system dynamics simulation model at individual level that predicts the Alzheimer’s disease incidence over the life course, and aggregates individual level models to predict population level trends 2. Calibrate the resulting simulation model based upon longitudinal data trends employed from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Both cohorts with Alzheimer’s disease and control subjects from this database will be used to fine-tune the simulation model. Conclusion The final validated model would be used to test different hypotheses and evaluate various strategies and/or their combinations to help evaluate the efficacy of prevention strategies on Alzheimer’s disease incidence and its growth.

The number of Lyme disease (LD) cases in the northeastern United States has been dramatically increasing with over 300 000 new cases each year. This is due to numerous factors interacting over time including low public awareness of LD, risk behaviours and clothing choices, ecological and climatic factors, an increase in rodents within ecologically fragmented peri-urban built environments and an increase in tick density and infectivity in such environments. We have used a system dynamics (SD) approach to develop a simulation tool to evaluate the significance of risk factors in replicating historical trends of LD cases, and to investigate the influence of different interventions, such as increasing awareness, controlling clothing risk and reducing mouse populations, in reducing LD risk. The model accurately replicates historical trends of LD cases. Among several interventions tested using the simulation model, increasing public awareness most significantly reduces the number of LD cases. This model provides recommendations for LD prevention, including further educational programmes to raise awareness and control behavioural risk. This model has the potential to be used by the public health community to assess the risk of exposure to LD.

Lyme disease (LD) cases have been on the rise throughout the United States, costing the healthcare system up to $1.3 billion per year, and making LD one of the greatest threats to public health. Factors influencing the number of LD cases range from environmental to system-level variables, but little is known about the influence of vegetation (canopy, understory, and ground cover) and human behavioral risk on LD cases and exposure to infected ticks. We determined the influence of various risk factors on the risk of exposure to infected ticks on 22 different walkways using multinomial logistic regression. The model classifies the walkways into high-risk and low-risk categories with 90% accuracy, in which the understory, human risk, and number of rodents are significant indicators. These factors should be managed to control the risk of transmission of LD to humans.

Stretch is a mechanical parameter, which has been proposed previously to affect the biological activities in different tissues. This study explored its utility in determining plaque vulnerability.One hundred and six patients with mild to moderate carotid stenosis were recruited in this study (53 symptomatic and 53 asymptomatic). High resolution, multi-sequence magnetic resonance (MR) imaging was performed to delineate various plaque components. Finite element method was used to predict high stretch concentration within the plaque.During a two-year follow-up, 11 patients in symptomatic group and 3 in asymptomatic group experienced recurrent cerebrovascular events. Plaque stretch at systole and stretch variation during one cardiac cycle was greater in symptomatic group than those in the asymptomatic. Within the symptomatic group, a similar trend was observed in patients with recurrent events compared to those without.Plaques with high stretch concentration and large stretch variation are associated with increased risk of future cerebrovascular events.

Abstract Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of factors like inherent differences between various vendors, hardware upgrades, protocol changes, and scanner calibration drift, as well as to ensure consistent data for medical image processing techniques. Given the critical importance and widespread relevance of this issue, a vast array of image harmonization methodologies have emerged, with deep learning-based approaches driving substantial advancements in recent times. The goal of this review paper is to examine the latest deep learning techniques employed for image harmonization by analyzing cutting-edge architectural approaches in the field of medical image harmonization, evaluating both their strengths and limitations. This paper begins by providing a comprehensive fundamental overview of image harmonization strategies, covering three critical aspects: established imaging datasets, commonly used evaluation metrics, and characteristics of different scanners. Subsequently, this paper analyzes recent structural MRI (Magnetic Resonance Imaging) harmonization techniques based on network architecture, network learning algorithm, network supervision strategy, and network output. The underlying architectures include U-Net, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based generative models, transformer-based approaches, as well as custom-designed network architectures. This paper investigates the effectiveness of Disentangled Representation Learning (DRL) as a pivotal learning algorithm in harmonization. Lastly, the review highlights the primary limitations in harmonization techniques, specifically the lack of comprehensive quantitative comparisons across different methods. The overall aim of this review is to serve as a guide for researchers and practitioners to select appropriate architectures based on their specific conditions and requirements. It also aims to foster discussions around ongoing challenges in the field and shed light on promising future research directions with the potential for significant advancements.

Objective(s): Enhancement of proliferation, pluripotency, and self-renewal capacity as the unique features of MSCs can improve their therapeutic potential to regenerate tissues. In this context, crocin and curcumin, carotenoid compounds with outstanding medicinal properties, could be promising for cell protection and growth. This study aimed to evaluate the impact of nanocurcumin and crocin on BM-MSCs proliferation and pluripotency in vitro.Materials and Methods: BM-MSC were isolated from the iliac crest of SCI patients who were candidates for stem cell therapy. The effect of crocin and nanocurcumin on MSC proliferation was evaluated using MTT and PDT assay. The percentage of apoptotic MSCs was measured by flow cytometry. Furthermore, mRNA and protein expression of OCT4 and SOX2 as the proliferation and self-renewal related genes were quantified by real-time PCR and western blotting, respectively.Results: Our findings demonstrated that only low concentrations of nanocurcumin (0.3 and 0.7 µM) and crocin (2.5 5 µM) significantly affected MSCs proliferation and protected them from apoptosis. Also, crocin and nanocurcumin at low doses caused an elevation in the mRNA and protein expression levels of OCT4 and SOX2 genes. In contrast, high concentrations decreased the survival of MSCs and led to increased apoptosis compared with the untreated group.Conclusion: Our results suggest that using nanocurcumin and crocin separately in culturing MSCs can be considered proliferative agents to prepare the more advantageous tool for cell therapies. However, more in vitro and preclinical research is needed in this area.

Abstract This research was undertaken to assess the effect of tragacanth gum–chitin nanofiber (TG–CNF) film containing free (CEO) or encapsulated cumin essential oil (CNE) combined with oxygen absorber (OA) packaging on the shelf‐life of ready‐to‐cook (RTC) turkey breast burgers during chilled storage. The experimental groups were OA and TG–CNF as single treatments, TG–CNF + CEO, TG–CNF + CNE, and TG–CNF + OA as binary treatments, TG–CNF + CEO + OA and TG–CNF + CNE + OA as ternary treatments, and control. The samples were stored at 3°C for 20 days and analyzed for microbial, physicochemical, and sensory attributes. Binary treatments, when compared to single treatments, and ternary treatments, when compared to binary treatments, exhibited enhanced effectiveness in managing microbial growth, hindering physicochemical alterations, and decelerating sensory alterations. At day 20, TG–CNF + CNE + OA group was identified as the most effective group in inhibiting the growth of total mesophilic bacteria (TMB), total psychrophilic bacteria (TSB), and coliforms (final counts were 4.8, 4.16, and ≤1 log CFU/g, respectively), and TG–CNF + CNE + OA and TG–CNF + CEO + OA groups were known as the most effective groups in inhibiting lactic acid bacteria (LAB) (final counts were 4.71 and 5.15 log CFU/g, respectively). Furthermore, the TG–CNF + CNE + OA treatment proved to be the most effective group in reducing the total volatile nitrogen (TVN) (final level was 19.2 mg N/100 g) and thiobarbituric acid reactive substances (TBARS) (final level was 0.119 mg malondialdehyde (MDA)/kg). TG–CNF + CNE + OA and TG–CNF + CEO + OA were the most efficient groups to delay the increasing rate of cooking loss (final values were 23.3% and 24.6%) and pH (final values were 7.01 and 6.99). The sample's shelf‐life was 4 days in control and TG–CNF, 8 days in OA and TG–CNF + OA, 12 days in TG–CNF + CEO, 16 days in TG‐CNF + CNE and TG–CNF + CEO + OA, and at least 20 days in TG–CNF + CNE + OA. As a result, the incorporation of TG–CNF + CNE alongside OA packaging emerges as a highly effective active packaging method for preserving RTC turkey breast burgers during chilled storage.