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Immunotoxicity is the critical endpoint used by some regulatory agencies to establish toxicity values for perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS). However, the hypothesis that exposure to certain per- and polyfluoroalkyl substances (PFAS) causes immune dysregulation is subject to much debate. An independent, international expert panel was engaged utilizing methods to reduce bias and “groupthink”. The panel concluded there is moderate evidence that PFOS and PFOA are immunotoxic, based primarily on evidence from animal data. However, species concordance and human relevance cannot be well established due to data limitations. The panel recommended additional testing that includes longer-term exposures, evaluates both genders, includes other species of animals, tests lower dose levels, assesses more complete measures of immune responses, and elucidates the mechanism of action. Panel members agreed that the Faroe Islands cohort data should not be used as the primary basis for deriving PFAS risk assessment values. The panel agreed that vaccine antibody titer is not useful as a stand-alone metric for risk assessment. Instead, PFOA and PFOS toxicity values should rely on multiple high-quality studies, which are currently not available for immune suppression. The panel concluded that the available PFAS immune epidemiology studies suffer from weaknesses in study design that preclude their use, whereas available animal toxicity studies provide comprehensive dataset to derive points of departure (PODs) for non-immune endpoints. The panel recommends accounting for potential PFAS immunotoxicity by applying a database uncertainty factor to POD values derived from animal studies for other more robustly supported critical effects.

This compendium is a product of the Commission’s Green Recovery Task Force, a group of world-renowned economists, academics, environmentalists, and private sector experts convened to discuss and provide recommendations on how to build economic resilience and reduce inequality as we recover from the COVID-19 pandemic and work towards a more equitable, sustainable, and inclusive future.

Applying risk assessment and management tools to plutonium disposition is a long-standing challenge for the U.S. government. The science is complicated, which has helped push risk assessment and management tools in new creative directions. Yet, communicating effectively about increasingly complicated risk-science issues like plutonium disposition requires careful planning and speakers who can address why specific tools are selected, the past record of applying these tools, why assumptions sometimes are applied instead of reliable data, and how uncertainty is characterized. Speakers addressing risk issues must also overcome obstacles in communication arising from expert-audience differences in knowledge and legal restrictions on disclosing information. This perspective seeks to highlight and illustrate five key risk questions, about probabilistic risk assessment (PRA) and performance assessment (PA) in the context of managing plutonium defense nuclear waste: objectives, experience, gaps, transparency, and difficulty of applying and communicating using each tool. While the general public needs to be involved, some issues require a level of expertise that is typically beyond local communities and therefore an expert panel should support community access.

Are dose-response relationships for benzene and health effects such as myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) supra-linear, with disproportionately high risks at low concentrations, e.g. below 1 ppm? To investigate this hypothesis, we apply recent mode of action (MoA) and mechanistic information and modern data science techniques to quantify air benzene-urinary metabolite relationships in a previously studied data set for Tianjin, China factory workers. We find that physiologically based pharmacokinetics (PBPK) models and data for Tianjin workers show approximately linear production of benzene metabolites for air benzene (AB) concentrations below about 15 ppm, with modest sublinearity at low concentrations (e.g. below 5 ppm). Analysis of the Tianjin worker data using partial dependence plots reveals that production of metabolites increases disproportionately with increases in air benzene (AB) concentrations above 10 ppm, exhibiting steep sublinearity (J shape) before becoming saturated. As a consequence, estimated cumulative exposure is not an adequate basis for predicting risk. Risk assessments must consider the variability of exposure concentrations around estimated exposure concentrations to avoid over-estimating risks at low concentrations. The same average concentration for a specified duration is disproportionately risky if it has higher variance. Conversely, if chronic inflammation via activation of inflammasomes is a critical event for induction of MDS and other health effects, then sufficiently low concentrations of benzene are predicted not to cause increased risks of inflammasome-mediated diseases, no matter how long the duration of exposure. Thus, we find no evidence that the dose-response relationship is supra-linear at low doses; instead sublinear or zero excess risk at low concentrations is more consistent with the data. A combination of physiologically based pharmacokinetic (PBPK) modeling, Bayesian network (BN) analysis and inference, and partial dependence plots appears a promising and practical approach for applying current data science methods to advance benzene risk assessment.

Background The MIVI Q aspiration catheters have been shown to achieve significantly greater flow rates than other intracranial aspiration catheters in vitro. We describe our initial real-world experience with the MIVI Q catheter in emergent acute ischemic stroke (AIS) caused by distal and medium vessel occlusions (DMVO). Methods Data was collated from a prospectively maintained database which included patients from October 2019 to December 2022. Occlusion demographics, thrombectomy technique, reperfusion scoring, procedural complications and disposition were assessed. The primary outcome of interest was rate of successful reperfusion defined as thrombolysis in cerebral infarction (TICI) score 2b-3. Secondary outcomes included rate of first pass effect (FPE) and complications. Results We included 64 target occlusions in 51 patients. The Q catheter successfully reached the DMVO in all occlusions. Successful reperfusion was achieved in 49/64 (76.6%) occlusions, and TICI scores were similar for primary and secondary DMVOs (P value = 0.41). FPE was achieved in 39/64 (60.9%) occlusions and did not differ between primary and secondary DMVOs (P value = 0.13). Reperfusion hemorrhage occurred in 3/64 (4.7%) cases, small volume subarachnoid hemorrhage in 3/64 (4.7%) cases, and small hemorrhagic transformation in 1/64 (1.6%) cases; the rate of complications did not differ based on primary versus secondary DMVO (P value = 0.29). Conclusion The MIVI Q catheter is both safe and effective. Our real-world experience supports the superior flow rates demonstrated in vitro and translates into high rates of successful reperfusion in AIS caused by DMVO in clinical practice.

This article, the eighth in the series, presents kinetic and photochemical data sheets evaluated by the IUPAC Task Group on Atmospheric Chemical Kinetic Data Evaluation. It covers the gas-phase thermal and photochemical reactions of organic species with four, or more, carbon atoms (≥ C4) available on the IUPAC website in 2021, including thermal reactions of closed-shell organic species with HO and NO3 radicals and their photolysis. The present work is a continuation of volume II (Atkinson et al., 2006), with new reactions and updated data sheets for reactions of HO (77 reactions) and NO3 (36 reactions) with ≥ C4 organics, including alkanes, alkenes, dienes, aromatics, oxygenated, organic nitrates and nitro compounds in addition to photochemical processes for nine species. The article consists of a summary table (Table 1), containing the recommended kinetic parameters for the evaluated reactions, and a supplement containing the data sheets, which provide information upon which recommendations are made.

Do faster slaughter line speeds for young chickens increase risk of Salmonella contamination? We analyze data collected in 2018–2019 from 97 slaughter establishments processing young chickens to examine the extent to which differences in slaughter line speeds across establishments operating under the same inspection system explain observed differences in their microbial quality, specifically frequencies of positive Salmonella samples. A variety of off-the-shelf statistical and machine learning techniques applied to the data to identify and visualize correlations and potential causal relationships among variables showed that the presence of Salmonella or other indicators of process control, such as noncompliance records for regulations associated with process control and food safety, are not significantly increased in establishments with higher line speeds (e.g., above 140 birds per min) compared with establishments with lower line speeds when establishments are operating under the conditions present in this study. This included some establishments operating under specific criteria to obtain a waiver for line speed. A null hypothesis advanced over 30 yr ago by the National Research Council that increased line speeds result in a product that is not contaminated more often than before line speeds were increased, appears to be fully consistent with these recent data.

Healthcare professionals (HCPs) play a key role in the monitoring of severe adverse drug reactions (ADRs). The present study aims to explore practices and barriers of HCPs in severe ADR monitoring and reporting, to evaluate their attitudes towards the monitoring and to assess the related factors. Self-administered questionnaires produced in hard copy and Google form were sent to 510 HCPs by stratified random sampling. Of the 350 HCPs that responded (68.6%), 44.9% had ever monitored ADRs. The most common practices were the observation of abnormal symptoms for ADR identification (88.5%), discontinuation of the suspected drug for ADR management (88.5%) and advice on recurrent drug allergy for ADR prevention (88.5%). Most HCPs (93.0%) obtained further patient history to identify severe ADRs. The uncertainty of the causal relationship was a major barrier to ADR reporting (60.0%). Pharmacists were more involved with practices in ADR monitoring and reporting (OR 20.405; p < 0.001), whereas longer work experience (>20 years) was negatively related to the practices (OR 0.271; p = 0.024). Over one-third (37.6%) of HCPs had a positive attitude towards severe ADR monitoring. In conclusion, the practices in severe ADR monitoring varied among different professions. However, the barriers to the reporting of ADRs still exist; hence, improving knowledge and cooperation among HCPs should be promoted.

Decision analysis and risk analysis have grown up around a set of organizing questions: what might go wrong, how likely is it to do so, how bad might the consequences be, what should be done to maximize expected utility and minimize expected loss or regret, and how large are the remaining risks? In probabilistic causal models capable of representing unpredictable and novel events, probabilities for what will happen, and even what is possible, cannot necessarily be determined in advance. Standard decision and risk analysis questions become inherently unanswerable ("undecidable") for realistically complex causal systems with "open-world" uncertainties about what exists, what can happen, what other agents know, and how they will act. Recent artificial intelligence (AI) techniques enable agents (e.g., robots, drone swarms, and automatic controllers) to learn, plan, and act effectively despite open-world uncertainties in a host of practical applications, from robotics and autonomous vehicles to industrial engineering, transportation and logistics automation, and industrial process control. This article offers an AI/machine learning perspective on recent ideas for making decision and risk analysis (even) more useful. It reviews undecidability results and recent principles and methods for enabling intelligent agents to learn what works and how to complete useful tasks, adjust plans as needed, and achieve multiple goals safely and reasonably efficiently when possible, despite open-world uncertainties and unpredictable events. In the near future, these principles could contribute to the formulation and effective implementation of more effective plans and policies in business, regulation, and public policy, as well as in engineering, disaster management, and military and civil defense operations. They can extend traditional decision and risk analysis to deal more successfully with open-world novelty and unpredictable events in large-scale real-world planning, policymaking, and risk management.

We examine how Bayesian network (BN) learning and analysis methods can help to meet several methodological challenges that arise in interpreting significant regression coefficients in exposure-response regression modeling. As a motivating example, we consider the challenge of interpreting positive regression coefficients for blood lead level (BLL) as a predictor of mortality risk for nonsmoking men. We first note that practices such as dichotomizing or categorizing continuous confounders (e.g. income), omitting potentially important socioeconomic confounders (e.g. education), and assuming specific parametric regression model forms leave unclear to what extent a positive regression coefficient reflects these modeling choices, rather than a direct dependence of mortality risk on exposure. Therefore, significant exposure-response coefficients in parametric regression models do not necessarily reveal the extent to which reducing exposure-related variables (e.g. BLL) alone, while leaving fixed other correlates of exposure and mortality risks (e.g. education, income, etc.) would reduce adverse outcome risks (e.g. mortality risks). We then consider how BN structure-learning and inference algorithms and nonparametric estimation methods (partial dependence plots) can be used to clarify dependencies between variables, variable selection, confounding, and quantification of joint effects of multiple factors on risk, including possible high-order interactions and nonlinearities. We conclude that these details must be carefully modeled to determine whether a data set provides evidence that exposure itself directly affects risks; and that BN and nonparametric effect estimation and uncertainty quantification methods can complement regression modeling and help to improve the scientific basis for risk management decisions and policy-making by addressing these issues.