Spatial distribution, pollution level and human health risk assessment of heavy metals in urban street dust at neighbourhood scale.

Urban street dust (UStD) is a vital issue for human health and is crucial for urban sustainability. This study aims to enhance the creation of safe, affordable, and resilient cities by examining environmental contamination and health risks in urban residential areas. Specifically, it investigates the concentrations and spatial distribution of chromium (Cr), cadmium (Cd), nickel (Ni), copper (Cu), lead (Pb), and zinc (Zn) in UStD in Yenimahalle, Ankara. The mean concentrations of Zn, Cr, Pb, Cd, Ni, and Cu in UStD were 97.98, 66.88, 55.22, 52.45, 38.37, and 3.81 mg/kg, respectively. The geoaccumulation pollution index (Igeo) values for these elements were: Cd (5.12), Ni (1.61), Cr (1.21), Pb (1.13), Cu (0.78), and Zn (0.24). These indices indicate that the area is moderately polluted with Cr, Pb, and Ni, uncontaminated to moderately contaminated with Cu and Zn, and extremely polluted with Cd. The hazard index (HI) values for Cr, Cd, Ni, Cu, Pb, and Zn were below the non-carcinogenic risk threshold for adults, indicating no significant risk. However, for children, the HI values for Pb, Ni, Cd, and Zn were 3.37, 1.80, 1.25, and 1.25, respectively, suggesting a higher risk. Carcinogenic risk (RI) of Cd, Ni, and Pb was significant for both children and adults, indicating that exposure through ingestion, inhalation, and dermal contact is hazardous. The findings highlight the need for strategic mitigation measures for both natural and anthropogenic activities, providing essential insights for residents, policymakers, stakeholders, and urban planners.

Modeling US blood donor deferrals under a policy of individual risk assessment for HIV risk sexual behavior.

BACKGROUND: In May 2023, the Food and Drug Administration (FDA) released final guidance for blood donor eligibility that recommended the elimination of 3-month deferral for men who have sex with men (MSM) and the related deferral for women who have sex with MSM. In its place, FDA introduced an individual risk assessment policy of asking all presenting blood donors, regardless of sex or gender, if they have had a new partner or more than one sexual partner in the last 3 months and deferring those who also report anal sex (penile-anal intercourse) during this period. We modeled the possible impact of this policy on the US blood donor base. STUDY DESIGN AND METHODS: We developed a computational model to estimate the percentage of blood donors who would be deferred under a policy of individual HIV risk assessment. The model incorporated demographic information about donors and national survey data on HIV risk behaviors and included age and sex distributions and dependencies. RESULTS: Our model estimates that approximately 1.2% of US blood donors would be deferred under the individual HIV risk assessment paradigm. DISCUSSION: The model predicts a relatively minor effect of replacing the time-based deferral for MSM with individual risk-based deferral for sexual behavior. As US blood centers implement this new policy, the effect may be mitigated by donor gains, which warrant further study. The new policy is unlikely to adversely affect the availability of blood and blood components.

Review of Poliovirus Transmission and Economic Modeling to Support Global Polio Eradication: 2020-2024.

Continued investment in the development and application of mathematical models of poliovirus transmission, economics, and risks leads to their use in support of polio endgame strategy development and risk management policies. This study complements an earlier review covering the period 2000-2019 and discusses the evolution of studies published since 2020 by modeling groups supported by the Global Polio Eradication Initiative (GPEI) partners and others. We systematically review modeling papers published in English in peer-reviewed journals from 2020-2024.25 that focus on poliovirus transmission and health economic analyses. In spite of the long-anticipated end of poliovirus transmission and the GPEI sunset, which would lead to the end of its support for modeling, we find that the number of modeling groups supported by GPEI partners doubled and the rate of their publications increased. Modeling continued to play a role in supporting GPEI and national/regional policies, but changes in polio eradication governance, decentralized management and decision-making, and increased heterogeneity in modeling approaches and findings decreased the overall impact of modeling results. Meanwhile, the failure of the 2016 globally coordinated cessation of type 2 oral poliovirus vaccine use for preventive immunization and the introduction of new poliovirus vaccines and formulation, increased the complexity and uncertainty of poliovirus transmission and economic models and policy recommendations during this time.

Assessment of hydrocarbon degradation capacity and kinetic modeling of Chlorella vulgaris and Scenedesmus quadricauda for crude oil phycoremediation under mixotrophic conditions.

Crude oil spills imperil aquatic ecosystems globally, prompting innovative solutions such as microalgae-based bioremediation. This study explores the potential of Chlorella vulgaris and Scenedesmus quadricauda, for crude oil spill phycoremediation under mixotrophic conditions and varying crude oil concentrations (0.5-2%). C. vulgaris demonstrated notable resilience, thriving up to 1% crude oil exposure, while S. quadricauda adapted to lower concentrations. Optimal growth for both was observed at 0.5% exposure. Chlorophyll a content in C. vulgaris increases at 0.5% exposure but declines above 1%, while a decline was noticeable in chlorophyll b in treatment groups above 1%. Carotenoid levels varied, displaying the highest levels at higher concentrations above 1.5%. Similarly, S. quadricauda showed increased chlorophyll a content at 0.5% exposure, with stable carotenoid levels and a decline in chlorophyll b content at higher concentrations. GC/MS analyses indicated C. vulgaris efficiently degraded aliphatic compounds like decane and tridecane, surpassing S. quadricauda in degrading both aliphatic and aromatic hydrocarbons. Growth kinetics was best represented by the modified Gompertz and logistic models. These findings highlight the species-specific adaptability and optimal concentration for microalgae to degrade crude oil effectively, advancing phycoremediation processes and strategies critical for environmental restoration.

This study marks the first exploration of both Chlorella vulgaris and the previously unexplored Scenedesmus quadricauda for crude oil phycoremediation potential under mixotrophic conditions. Additionally, it pioneers the modeling and study of algae growth kinetics in response to crude oil exposure. Notably, this research demonstrated the adaptability and efficiency of C. vulgaris in degrading crude oil components under mixotrophic conditions up to a level of 1%, while S. quadricauda showed similar capabilities at a concentration of 0.5%.

Use of Real-World FHIR Data Combined with Context-Sensitive Decision Modeling to Guide Sentinel Biopsy in Melanoma.

Background: To support clinical decision-making at the point of care, the "best next step" based on Standard Operating Procedures (SOPs) and actual accurate patient data must be provided. To do this, textual SOPs have to be transformed into operable clinical algorithms and linked to the data of the patient being treated. For this linkage, we need to know exactly which data are needed by clinicians at a certain decision point and whether these data are available. These data might be identical to the data used within the SOP or might integrate a broader view. To address these concerns, we examined if the data used by the SOP is also complete from the point of view of physicians for contextual decision-making. Methods: We selected a cohort of 67 patients with stage III melanoma who had undergone adjuvant treatment and mainly had an indication for a sentinel biopsy. First, we performed a step-by-step simulation of the patient treatment along our clinical algorithm, which is based on a hospital-specific SOP, to validate the algorithm with the given Fast Healthcare Interoperability Resources (FHIR)-based data of our cohort. Second, we presented three different decision situations within our algorithm to 10 dermatooncologists, focusing on the concrete patient data used at this decision point. The results were conducted, analyzed, and compared with those of the pure algorithmic simulation. Results: The treatment paths of patients with melanoma could be retrospectively simulated along the clinical algorithm using data from the patients' electronic health records. The subsequent evaluation by dermatooncologists showed that the data used at the three decision points had a completeness between 84.6% and 100.0% compared with the data used by the SOP. At one decision point, data on "patient age (at primary diagnosis)" and "date of first diagnosis" were missing. Conclusions: The data needed for our decision points are available in the FHIR-based dataset. Furthermore, the data used at decision points by the SOP and hence the clinical algorithm are nearly complete compared with the data required by physicians in clinical practice. This is an important precondition for further research focusing on presenting decision points within a treatment process integrated with the patient data needed.

A New Quantitative Tool for the Ultrasonographic Assessment of Tendons: A Reliability and Validity Study on the Patellar Tendon.

Ultrasound is widely used for tendon assessment due to its safety, affordability, and portability, but its subjective nature poses challenges. This study aimed to develop a new quantitative analysis tool based on artificial intelligence to identify statistical patterns of healthy and pathological tendons. Furthermore, we aimed to validate this new tool by comparing it to experts' subjective assessments. A pilot database including healthy controls and patients with patellar tendinopathy was constructed, involving 14 participants with asymptomatic (n = 7) and symptomatic (n = 7) patellar tendons. Ultrasonographic images were assessed twice, utilizing both the new quantitative tool and the subjective scoring method applied by an expert across five regions of interest. The database contained 61 variables per image. The robustness of the clinical and quantitative assessments was tested via reliability analyses. Lastly, the prediction accuracy of the quantitative features was tested via cross-validated generalized linear mixed-effects logistic regressions. These analyses showed high reliability for quantitative variables related to "Bone" and "Quality", with ICCs above 0.75. The ICCs for "Edges" and "Thickness" varied but mostly exceeded 0.75. The results of this study show that certain quantitative variables are capable of predicting an expert's subjective assessment with generally high cross-validated AUC scores. A new quantitative tool for the ultrasonographic assessment of the tendon was designed. This system is shown to be a reliable and valid method for evaluating the patellar tendon structure.

Advanced optical assessment and modeling of extrusion bioprinting.

In the context of tissue engineering, biofabrication techniques are employed to process cells in hydrogel-based matrices, known as bioinks, into complex 3D structures. The aim is the production of functional tissue models or even entire organs. The regenerative production of biological tissues adheres to a multitude of criteria that ultimately determine the maturation of a functional tissue. These criteria are of biological nature, such as the biomimetic spatial positioning of different cell types within a physiologically and mechanically suitable matrix, which enables tissue maturation. Furthermore, the processing, a combination of technical procedures and biological materials, has proven highly challenging since cells are sensitive to stress, for example from shear and tensile forces, which may affect their vitality. On the other hand, high resolutions are pursued to create optimal conditions for subsequent tissue maturation. From an analytical perspective, it is prudent to first investigate the printing behavior of bioinks before undertaking complex biological tests. According to our findings, conventional shear rheological tests are insufficient to fully characterize the printing behavior of a bioink. For this reason, we have developed optical methods that, complementarily to the already developed tests, allow for quantification of printing quality and further viscoelastic modeling of bioinks.

Carcinogenic formaldehyde in U.S. residential buildings: Mass inventories, human health impacts, and associated healthcare costs.

Formaldehyde, a human carcinogen, is formulated into building materials in the U.S. and worldwide. We used literature information and mass balances to obtain order-of-magnitude estimates of formaldehyde inventories in U.S. residential buildings as well as associated exposures, excess morbidity, and healthcare costs along with other economic ramifications. Use of formaldehyde in building materials dates to the 1940s and continues today unabated, despite its international classification in 2004 as a human carcinogen. Global production of formaldehyde was about 32 million metric tons (MMT) in 2006. In the U.S., 5.7 ± 0.05 to 7.4 ± 0.125 MMT of formaldehyde were produced annually from 2006 to 2022, with 65 ± 5 % of this mass (3.7 ± 0.03 to 4.8 ± 0.08 MMT) entering building materials. For a typical U.S. residential building constructed in 2022, we determined an average total mass of formaldehyde containing chemicals of 48.2 ± 10.1 kg, equivalent to 207 ± 40 g of neat formaldehyde per housing unit. When extrapolated to the entire U.S. housing stock, this equates to 29,800 ± 5760 metric tons of neat formaldehyde. If the health threshold in indoor air of 0.1 mg/m3 is never surpassed in a residential building, safe venting of embedded formaldehyde would take years. Using reported indoor air exceedances, up to 645 ± 33 excess cancer cases may occur U.S. nationwide annually generating up to US$65 M in cancer treatment costs alone, not counting ~16,000 ± 1000 disability adjusted life-years. Other documents showed health effects of formaldehyde exist, but could not be quantified reliably, including sick building syndrome outcomes such as headache, asthma, and various respiratory illnesses. Opportunities to improve indoor air exposure assessments are discussed with special emphasis on monitoring of building wastewater. Safer alternatives to formaldehyde in building products exist and are recommended for future use.

Improving the spatial and temporal resolution of burden of disease measures with Bayesian models.

This paper contributes to the field by addressing the critical issue of enhancing the spatial and temporal resolution of health data. Although Bayesian methods are frequently employed to address this challenge in various disciplines, the application of Bayesian spatio-temporal models to burden of disease (BOD) studies remains limited. Our novelty lies in the exploration of two existing Bayesian models that we show to be applicable to a wide range of BOD data, including mortality and prevalence, thereby providing evidence to support the adoption of Bayesian modeling in full BOD studies in the future. We illustrate the benefits of Bayesian modeling with an Australian case study involving asthma and coronary heart disease. Our results showcase the effectiveness of Bayesian approaches in increasing the number of small areas for which results are available and improving the reliability and stability of the results compared to using data directly from surveys or administrative sources.

Cost-effectiveness of dialysis and kidney transplantation to treat end-stage renal disease in Ukraine.

OBJECTIVE: Aim: To determine the economic feasibility of using kidney transplantation compared to hemodialysis in end-stage renal disease in the long term in countries with a low and medium level of economic development using the example of Ukraine. PATIENTS AND METHODS: Materials and Methods: The cost effectiveness analysis method was used. Conducted Markov modeling and comparison of the consequences of kidney transplantation and hemodialysis in terms of treatment costs and the number of added years of life for a cohort of 1,675 patients were carried out. The incremental cost-effectiveness ratio is defined. RESULTS: Results: Based on the results of modeling, it was determined that among 1,675 patients with end-stage kidney disease in Ukraine, 1,248 (74.5%) will remain alive after 10 years of treatment when kidney transplantation technology is used. The highest costs will be in the first year ($25,864), and in subsequent years - about $5,769. With the use of hemodialysis technology, only 728 patients (43.5%) will be alive after 10 years, the cost of treating one patient per year is $11,351. The use of kidney transplantation adds 3191 years of quality life for 1675 patients compared with hemodialysis (1.9 years per patient). CONCLUSION: Conclusions: Kidney transplantation is an economically feasible technology for Ukraine, as the incremental cost-effectiveness ratio is $4694, which is 1.04 times higher than Ukraine's GDP per capita. The results of the study allow us to recommend that decision-makers in countries with a low and medium level of economic development give priority in financing to renal transplantation.

Creating Computational Models of Ion Channel Dynamics.

Markov models are widely used to represent ion channel protein configurations as different states in the model's topology. Such models allow for dynamic simulation of ion channel kinetics through the simulated application of voltage potentials across a cell membrane. In this chapter, we present a general method for creating Markov models of ion channel kinetics using computational optimization alongside a fully featured example model of a cardiac potassium channel. Our methods cover designing training protocols, iteratively testing potential model topologies for structure identification, creation of algorithms for model simulation, as well as methods for assessing the quality of fit for a finalized model.

Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability Framework for Safe and Effective Large Language Models in Medical Education: Narrative Review and Qualitative Study.

BACKGROUND: The world has witnessed increased adoption of large language models (LLMs) in the last year. Although the products developed using LLMs have the potential to solve accessibility and efficiency problems in health care, there is a lack of available guidelines for developing LLMs for health care, especially for medical education. OBJECTIVE: The aim of this study was to identify and prioritize the enablers for developing successful LLMs for medical education. We further evaluated the relationships among these identified enablers. METHODS: A narrative review of the extant literature was first performed to identify the key enablers for LLM development. We additionally gathered the opinions of LLM users to determine the relative importance of these enablers using an analytical hierarchy process (AHP), which is a multicriteria decision-making method. Further, total interpretive structural modeling (TISM) was used to analyze the perspectives of product developers and ascertain the relationships and hierarchy among these enablers. Finally, the cross-impact matrix-based multiplication applied to a classification (MICMAC) approach was used to determine the relative driving and dependence powers of these enablers. A nonprobabilistic purposive sampling approach was used for recruitment of focus groups. RESULTS: The AHP demonstrated that the most important enabler for LLMs was credibility, with a priority weight of 0.37, followed by accountability (0.27642) and fairness (0.10572). In contrast, usability, with a priority weight of 0.04, showed negligible importance. The results of TISM concurred with the findings of the AHP. The only striking difference between expert perspectives and user preference evaluation was that the product developers indicated that cost has the least importance as a potential enabler. The MICMAC analysis suggested that cost has a strong influence on other enablers. The inputs of the focus group were found to be reliable, with a consistency ratio less than 0.1 (0.084). CONCLUSIONS: This study is the first to identify, prioritize, and analyze the relationships of enablers of effective LLMs for medical education. Based on the results of this study, we developed a comprehendible prescriptive framework, named CUC-FATE (Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability), for evaluating the enablers of LLMs in medical education. The study findings are useful for health care professionals, health technology experts, medical technology regulators, and policy makers.

Providing predictive models for quality parameters of groundwater resources in arid areas of central Iran: A case study of kashan plain.

Groundwater pollution can occur due to both anthropogenic and natural causes, leading to a decline in water quality and posing a threat to human health and the environment. The pollution of ground water resources with chemical pollutants is often considered. To manage water resources sustainably, ensuring their quality and quantity is crucial. Yet, testing groundwater can be expensive and time-consuming. So, using modeling to predict the chemical parameters of groundwater resources is considered to be an efficient and economical method. In this study, we examined three models to predict groundwater quality in dry regions by using R programming language. The random forest (RF) outperformed the other models in developing predictive models for water quality. Also, the multiple linear regression (MLR) model demonstrated strong performance, particularly in predicting total hardness (TH) in Aran Va Bidgol groundwater resources. The decision tree (DT) model did well but had lower performance than the RF model in predicting quality parameters. This approach can be efficacious in the field of effective management and protection of groundwater resources and enables the assessment of risks related to water resources.

An Assessment of the Performance Limitations of the Integrated QuantifilerTM Trio-HRM Assay: A Forensic Tool Designed to Identify Mixtures at the Quantification Stage.

Although guidelines exist for identifying mixtures, these measures often occur at the end-point of analysis and are protracted. To facilitate early mixture detection, we integrated a high-resolution melt (HRM) mixture screening assay into the qPCR step of the forensic workflow, producing the integrated QuantifilerTM Trio-HRM assay. The assay, when coupled with a prediction tool, allowed for 75.0% accurate identification of the contributor status of a sample (single source vs. mixture). To elucidate the limitations of the developed qPCR-HRM assay, developmental validation studies were conducted assessing the reproducibility and samples with varying DNA ratios, contributors, and quality. From this work, it was determined that the integrated QuantifilerTM Trio-HRM assay is capable of accurately identifying mixtures with up to five contributors and mixtures at ratios up to 1:100. Further, the optimal performance concentration range was found to be between 0.025 and 0.5 ng/µL. With these results, evidentiary-like DNA samples were then analyzed, resulting in 100.0% of the mixture samples being accurately identified; furthermore, every time a sample was predicted as a single source, it was true, giving confidence to any single-source calls. Overall, the integrated QuantifilerTM Trio-HRM assay has exhibited an enhanced ability to discern mixture samples from single-source samples at the qPCR stage under commonly observed conditions regardless of the contributor's sex.

Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design.

Across many fields, scenario modeling has become an important tool for exploring long-term projections and how they might depend on potential interventions and critical uncertainties, with relevance to both decision makers and scientists. In the past decade, and especially during the COVID-19 pandemic, the field of epidemiology has seen substantial growth in the use of scenario projections. Multiple scenarios are often projected at the same time, allowing important comparisons that can guide the choice of intervention, the prioritization of research topics, or public communication. The design of the scenarios is central to their ability to inform important questions. In this paper, we draw on the fields of decision analysis and statistical design of experiments to propose a framework for scenario design in epidemiology, with relevance also to other fields. We identify six different fundamental purposes for scenario designs (decision making, sensitivity analysis, situational awareness, horizon scanning, forecasting, and value of information) and discuss how those purposes guide the structure of scenarios. We discuss other aspects of the content and process of scenario design, broadly for all settings and specifically for multi-model ensemble projections. As an illustrative case study, we examine the first 17 rounds of scenarios from the U.S. COVID-19 Scenario Modeling Hub, then reflect on future advancements that could improve the design of scenarios in epidemiological settings.

Centralized or Onsite Testing? Examining the Costs of Water Quality Monitoring in Rural Africa.

Rural water systems in Africa have room to improve water quality monitoring. However, the most cost-effective approach for microbial water testing remains uncertain. This study compared the cost per E. coli test (membrane filtration) of four approaches representing different levels of centralization: (i) one centralized laboratory serving all water systems, (ii) a mobile laboratory serving all systems, (iii) multiple semi-centralized laboratories serving clusters of systems, and (iv) decentralized analysis at each system. We employed Monte Carlo analyses to model the costs of these approaches in three real-world contexts in Ghana and Uganda and in hypothetical simulations capturing various conditions across rural Africa. Centralized testing was the lowest cost in two real-world settings and the widest variety of simulations, especially those with water systems close to a central laboratory (<36 km). Semi-centralized testing was the lowest cost in one real-world setting and in simulations with clustered water systems and intermediate sampling frequencies (1-2 monthly samples per system). The mobile lab was the lowest cost in the fewest simulations, requiring few systems and infrequent sampling. Decentralized testing was cost-effective for remote systems and frequent sampling, but only if sampling did not require a dedicated vehicle. Alternative low-cost testing methods could make decentralized testing more competitive.

Cost-Effectiveness of Increasing Access to Colorectal Cancer Diagnosis: Analysis From Thailand.

OBJECTIVES: The purpose of this study is to evaluate the cost-effectiveness of increasing access to colorectal cancer (CRC) diagnosis, considering resource limitations in Thailand. METHODS: We analyzed the cost-effectiveness of increasing access to fecal immunochemical test screening (strategy I), symptom evaluation (strategy II), and their combination through healthcare and societal perspectives using Colo-Sim, a simulation model of CRC care. We extended our analysis by adding a risk-stratification score (RS) to the strategies. We analyzed all strategies under the currently limited annual colonoscopy capacity and sufficient capacity. We estimated quality-adjusted life-years (QALYs) and costs over 2023 to 2047 and performed sensitivity analyses. RESULTS: Annual costs for CRC care will increase over 25 years in Thailand, resulting in a cumulative cost of 323B Thai baht (THB). Each strategy results in higher QALYs gained and additional costs. With the current colonoscopy capacity and willingness-to-pay threshold of 160 000 THB, strategy I with and without RS is not cost-effective. Strategy II + RS is the most cost-effective, resulting in 0.68 million QALYs gained with additional costs of 66B THB. Under sufficient colonoscopy capacity, all strategies are deemed cost-effective, with the combined approach (strategy I + II + RS) being the most favorable, achieving the highest QALYs (1.55 million) at an additional cost of 131 billion THB. This strategy also maintains the highest probability of being cost-effective at any willingness-to-pay threshold above 96 000 THB. CONCLUSIONS: In Thailand, fecal immunochemical test screening, symptom evaluation, and RS use can achieve the highest QALYs; however, boosting colonoscopy capacity is essential for cost-effectiveness.

Quantitative microbial risk assessment (QMRA) tool for modelling pathogen infection risk to wastewater treatment plant workers.

Wastewater treatment plants (WWTPs) provide vital services to the public by removing contaminants from wastewater prior to environmental discharge or reuse for beneficial purposes. WWTP workers occupationally exposed to wastewater can be at risk of respiratory or gastrointestinal diseases. The study objectives were to: (1) quantify pathogens and pathogen indicators in wastewater aerosols near different WWTP processes/unit operations, (2) develop a QMRA model for multi-pathogen and multi-exposure pathway risks, and (3) create a web-based application to perform and communicate risk calculations for wastewater workers. Case studies for seven different WWTP job tasks were performed investigating infection risk across nine different enteric and respiratory pathogens. It was observed that the ingestion risk among job tasks was highest for "walking the WWTP," which involved exposure from splashing, bioaerosols, and hand-to-mouth contact from touching contaminated surfaces. There was also a notable difference in exposure risk during peak (5:00am-9:00am) and non-peak hours (9:00am- 5:00am), with risks during the peak flow hours of the early morning assumed to be 5 times greater than non-peak hours. N95 respirator usage reduced median respiratory risks by 77 %. The developed tool performs multiple QMRA calculations to estimate WWTP workers' infection risks from accidental ingestion or inhalation of wastewater from multiple pathogens and exposure scenarios, which can inform risk management strategies to protect occupational health. However, more data are needed to reduce uncertainty in model estimates, including comparative data for pathogen concentrations in wastewater during peak and non-peak hours. QMRA tools will increase accessibility of risk models for utilization in decision-making.

Using qualitative system dynamics modeling to understand overdose bystander behavior in the context of Connecticut's Good Samaritan Laws and identify effective policy options.

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.

Innovative approaches to cardiovascular safety pharmacology assessment.

This editorial prefaces the annual themed issue on safety pharmacology (SP) methods which has been published since 2004 in the Journal of Pharmacological and Toxicological Methods (JPTM). Here we highlight content derived from the 2023 Safety Pharmacology Society (SPS) meeting held in Brussels, Belgium. The meeting generated 138 abstracts, reproduced in the current volume of JPTM. As in prior years, the manuscripts reflect various areas of innovation in SP including in silico modeling of stroke volume, cardiac output and systemic vascular resistance, computational approaches that compare drug-induced proarrhythmic sensitivity of human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), an evaluation of the utility of the corrected J-Tpeak and Tpeak-to-Tend parameters from the ECG as potential proarrhythmia biomarkers, and the applicability of nonclinical concentration-QTc (C-QTc) modeling of data derived from the conduct of the in vivo QTc study as a component of the core battery of safety pharmacology studies.