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Children are routinely exposed to chemicals known or suspected of harming brain development. Targeting Environmental Neuro-Development Risks (Project TENDR), an alliance of leading scientists, health professionals, and advocates, is working to protect children from these toxic chemicals and pollutants, especially the disproportionate exposures experienced by children from families with low incomes and families of color. This scoping review was initiated to map existing literature on disparities in neurodevelopmental outcomes for U.S. children from population groups who have been historically economically/socially marginalized and exposed to seven exemplar neurotoxicants: combustion-related air pollution (AP), lead (Pb), mercury (Hg), organophosphate pesticides (OPs), phthalates (Phth), polybrominated diphenyl ethers (PBDEs), and polychlorinated biphenyls (PCBs). Systematic literature searches for the seven exemplar chemicals, informed by the Population, Exposure, Comparator, Outcome (PECO) framework, were conducted through 18 November 2022, using PubMed, CINAHL Plus (EBSCO), GreenFILE (EBSCO), and Web of Science sources. We examined these studies regarding authors' conceptualization and operationalization of race, ethnicity, and other indicators of sociodemographic and socioeconomic disadvantage; whether studies presented data on exposure and outcome disparities and the patterns of those disparities; and the evidence of effect modification by or interaction with race and ethnicity. Two hundred twelve individual studies met the search criteria and were reviewed, resulting in 218 studies or investigations being included in this review. AP and Pb were the most commonly studied exposures. The most frequently identified neurodevelopmental outcomes were cognitive and behavioral/psychological. Approximately a third (74 studies) reported investigations of interactions or effect modification with 69% (51 of 74 studies) reporting the presence of interactions or effect modification. However, less than half of the studies presented data on disparities in the outcome or the exposure, and fewer conducted formal tests of heterogeneity. Ninety-two percent of the 165 articles that examined race and ethnicity did not provide an explanation of their constructs for these variables, creating an incomplete picture. As a whole, the studies we reviewed indicated a complex story about how racial and ethnic minority and low-income children may be disproportionately harmed by exposures to neurotoxicants, and this has implications for targeting interventions, policy change, and other necessary investments to eliminate these health disparities. We provide recommendations on improving environmental epidemiological studies on environmental health disparities. To achieve environmental justice and health equity, we recommend concomitant strategies to eradicate both neurotoxic chemical exposures and systems that perpetuate social inequities. https://doi.org/10.1289/EHP11750.

The toxicity of microplastics (MPs) has attracted wide attention from researchers. Previous studies have indicated that MPs produce toxic effects on a variety of organs in aquatic organisms and mammals. However, the exact neurotoxicity of MPs in mammals is still unclear. We aimed to confirm the neurotoxicity of chronic exposure to polystyrene MPs (PS-MPs) at environmental pollution concentrations. In the present study, mice were provided drinking water containing and PS-MPs with diameters of 0.5, 4, and for 180 consecutive days. After the exposure period, the mice were anesthetized to gain brain tissues. The accumulation of PS-MPs in brain tissues, integrity of the blood-brain barrier, inflammation, and spine density were detected. We evaluated learning and memory ability by the Morris water maze and novel object recognition tests. We observed the accumulation of PS-MPs with various particle diameters (0.5, 4, and ) in the brains of exposed mice. Meanwhile, exposed mice also exhibited disruption of the blood-brain barrier, higher level of dendritic spine density, and an inflammatory response in the hippocampus. In addition, exposed mice exhibited cognitive and memory deficits compared with control mice as determined using the Morris water maze and novel object recognition tests, respectively. There was a concentration-dependent trend, but no particle size-dependent differences were seen in the neurotoxicity of MPs. Collectively, our results suggested that PS-MPs exposure can lead to learning and memory dysfunctions and induce neurotoxic effects in mice, findings which have wide-ranging implications for the public regarding the potential risks of MPs. https://doi.org/10.1289/EHP10255.

A new era of education and research based on chatbots and artificial intelligence is quickly growing. However, the application of these new systems is associated with several challenges and limitations, mainly related to ethics. This paper explores the potential use of AI systems and chatbots in the academic field and their impact on research and education from an ethical perspective. Through a qualitative methodology, the researcher perform exploratory research and data collection based on expert analysis and interpretation. The researcher conducted a comprehensive review of the main potential challenges associated with the use of chatbots in education and research to identify current practices, challenges, and opportunities. This explorative work provides a foundational understanding of the studied topic. It also helps us to better understand the subjective experiences and perspectives of the observed phenomenon, and uncovers their meanings and proposes potential solutions to the observed issues. This study examines the advantages and limitations of AI systems and chatbots, as well as their role in supporting human expertise and judgment. The paper also discusses the ethical challenges related to the use of AI systems and chatbots in research, as well as the potential for misuse and exploitation. It also proposes effective solutions to the observed ethical dilemmas. The research admits that we live in a new era of AI-based education and research. The observed technological advancements will definitely shift research processes and transform educative systems, especially in term of assessments. Digital assessments are going to disappear and assessment methods need to be more creative and innovative. The paper highlights the necessity of adaptation to the new reality of AI systems and chatbots. Co-living, sustainability and continuous adaptation to the development of these systems will become a matter of emergency. Raising awareness, adopting appropriate legislations and solidifying ethical values will strengthen research and protect educational systems. The presence of AI systems and chatbots in education needs to be considered as an opportunity for development rather than a threat.

Background The shape of the exposure-response curve for long-term ambient fine particulate (PM 2.5 ) exposure and cause-specific mortality is poorly understood, especially for rural populations and underrepresented minorities. Methods We used hybrid machine learning and Cox proportional hazard models to assess the association of long-term PM 2.5 exposures on specific causes of death for 53 million U.S. Medicare beneficiaries (aged ≥65) from 2000 to 2008. Models included strata for age, sex, race, and ZIP code and controlled for neighborhood socio-economic status (SES) in our main analyses, with approximately 4 billion person-months of follow-up, and additionally for warm season average of 1-h daily maximum ozone exposures in a sensitivity analysis. The impact of non-traffic PM 2.5 on mortality was examined using two stage models of PM 2.5 and nitrogen dioxide (NO 2 ). Results A 10 μg /m 3 increase in 12-month average PM 2.5 prior to death was associated with a 5% increase in all-cause mortality, as well as an 8.8, 5.6, and 2.5% increase in all cardiovascular disease (CVD)-, all respiratory-, and all cancer deaths, respectively, in age, gender, race, ZIP code, and SES-adjusted models. PM 2.5 exposures, however, were not associated with lung cancer mortality. Results were not sensitive to control for ozone exposures. PM 2.5 -mortality associations for CVD- and respiratory-related causes were positive and significant for beneficiaries irrespective of their sex, race, age, SES and urbanicity, with no evidence of a lower threshold for response or of lower Risk Ratios (RRs) at low PM 2.5 levels. Associations between PM 2.5 and CVD and respiratory mortality were linear and were higher for younger, Black and urban beneficiaries, but were largely similar by SES. Risks associated with non-traffic PM 2.5 were lower than that for all PM 2.5 and were null for respiratory and lung cancer-related deaths. Conclusions PM 2.5 was associated with mortality from CVD, respiratory, and all cancer, but not lung cancer. PM 2.5 -associated risks of CVD and respiratory mortality were similar across PM 2.5 levels, with no evidence of a threshold. Blacks, urban, and younger beneficiaries were most vulnerable to the long-term impacts of PM 2.5 on mortality.

The hidden costs of artificial intelligence, from natural resources and labor to privacy and freedom What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? In this book Kate Crawford reveals how this planetary network is fueling a shift toward undemocratic governance and increased inequality. Drawing on more than a decade of research, award-winning science, and technology, Crawford reveals how AI is a technology of extraction: from the energy and minerals needed to build and sustain its infrastructure, to the exploited workers behind \"automated\" services, to the data AI collects from us. Rather than taking a narrow focus on code and algorithms, Crawford offers us a political and a material perspective on what it takes to make artificial intelligence and where it goes wrong. While technical systems present a veneer of objectivity, they are always systems of power. This is an urgent account of what is at stake as technology companies use artificial intelligence to reshape the world.

The introduction of accessible generative artificial intelligence opens promising opportunities for the implementation of personalized learning methods in any educational environment. Personalized learning has been conceptualized for a long time, but it has only recently become realistic and truly achievable. In this paper, we propose an affordable and sustainable approach toward personalizing learning materials as part of the complete educational process. We have created a tool within a pre-existing learning management system at a software engineering college that automatically generates learning materials based on the learning outcomes provided by the professor for a particular class. The learning materials were composed in three distinct styles, the initial one being the traditional professor style and the other two variations adopting a pop-culture influence, namely Batman and Wednesday Addams. Each lesson, besides being delivered in three different formats, contained automatically generated multiple-choice questions that students could use to check their progress. This paper contains complete instructions for developing such a tool with the help of large language models using OpenAI’s API and an analysis of the preliminary experiment of its usage performed with the help of 20 college students studying software engineering at a European university. Participation in the study was optional and on voluntary basis. Each student’s tool usage was quantified, and two questionnaires were conducted: one immediately after subject completion and another 6 months later to assess both immediate and long-term effects, perceptions, and preferences. The results indicate that students found the multiple variants of the learning materials really engaging. While predominantly utilizing the traditional variant of the learning materials, they found this approach inspiring, would recommend it to other students, and would like to see it more in classes. The most popular feature were the automatically generated quiz-style tests that they used to assess their understanding. Preliminary evidence suggests that the use of various versions of learning materials leads to an increase in students’ study time, especially for students who have not mastered the topic otherwise. The study’s small sample size of 20 students restricts its ability to generalize its findings, but its results provide useful early insights and lay the groundwork for future research on AI-supported educational strategies.

Background “The impacts of the Coronavirus Disease 2019 (COVID-19) pandemic and the shutdown it triggered at universities across the world, led to a great degree of social isolation among university staff and students. The aim of this study was to identify the perceived consequences of this on staff and their work and on students and their studies at universities. Method The study used a variety of methods, which involved an on-line survey on the influences of social isolation using a non-probability sampling. More specifically, two techniques were used, namely a convenience sampling (i.e. involving members of the academic community, which are easy to reach by the study team), supported by a snow ball sampling (recruiting respondents among acquaintances of the participants). A total of 711 questionnaires from 41 countries were received. Descriptive statistics were deployed to analyse trends and to identify socio-demographic differences. Inferential statistics were used to assess significant differences among the geographical regions, work areas and other socio-demographic factors related to impacts of social isolation of university staff and students. Results The study reveals that 90% of the respondents have been affected by the shutdown and unable to perform normal work or studies at their institution for between 1 week to 2 months. While 70% of the respondents perceive negative impacts of COVID 19 on their work or studies, more than 60% of them value the additional time that they have had indoors with families and others. . Conclusions While the majority of the respondents agree that they suffered from the lack of social interaction and communication during the social distancing/isolation, there were significant differences in the reactions to the lockdowns between academic staff and students. There are also differences in the degree of influence of some of the problems, when compared across geographical regions. In addition to policy actions that may be deployed, further research on innovative methods of teaching and communication with students is needed in order to allow staff and students to better cope with social isolation in cases of new or recurring pandemics.