Analysis of Nonlinear Associations between Prenatal Methylmercury Exposure from Fish Consumption and Neurodevelopmental Outcomes in the Seychelles Main Cohort at 17 Years

A scoping review and quality assessment of machine learning techniques in identifying maternal risk factors during the peripartum phase for adverse child development

Maternal exposure to environmental risk factors (e.g., heavy metal exposure) or mental health problems during the peripartum phase has been shown to lead to negative and lasting impacts on child development and life in adulthood. Given the importance of identifying early markers within highly complex and heterogeneous perinatal factors, machine learning techniques emerge as a promising tool. The main goal of the current scoping review was to summarize the evidence on the application of machine learning techniques in predicting or identifying risk factors during peripartum for child development. A critical appraisal was also conducted to evaluate various aspects, including representativeness, data leakage, validation, performance metrics, and interpretability. A systematic search was conducted in PubMed, Web of Science, Scopus, and Google Scholar to identify studies published prior to the 14th of January 2025. Review selection and data extraction were performed by three independent reviewers. After removing duplicates, the searches yielded 10,336 studies, of which 60 studies were included in the final report. Among these 60 machine learning studies, a majority were pattern-focused, using machine learning primarily as a tool to more accurately describe associations between variables, while 16 studies were prediction-focused (26.7%), exploring the predictive performance of their models. For prediction-focused machine learning studies, a diverse range of methodologies was observed. The quality assessment showed that all studies had some important criteria that were not fully met, with deviations ranging from minor to major, limiting the interpretability and generalizability of the reported findings. Future research should aim at addressing these limitations to enhance the robustness and applicability of machine learning models in this field.

Single cell RNA sequencing detects persistent cell type- and methylmercury exposure paradigm-specific effects in a human cortical neurodevelopmental model

The developing human brain is uniquely vulnerable to methylmercury (MeHg) resulting in lasting effects especially in developing cortical structures. Here we assess by single-cell RNA sequencing (scRNAseq) persistent effects of developmental MeHg exposure in a differentiating cortical human-induced pluripotent stem cell (hiPSC) model which we exposed to in vivo relevant and non-cytotoxic MeHg (0.1 and 1.0 μM) concentrations. The cultures were exposed continuously for 6 days either once only during days 4-10, a stage representative of neural epithelial- and radial glia cells, or twice on days 4-10 and days 14-20, a somewhat later stage which includes intermediate precursors and early postmitotic neurons. After the completion of MeHg exposure the cultures were differentiated further until day 38 and then assessed for persistent MeHg-induced effects by scRNAseq. We report subtle, but significant changes in the population size of different cortical cell types/stages and cell cycle. We also observe MeHg-dependent differential gene expression and altered biological processes as determined by Gene Ontology analysis. Our data demonstrate that MeHg results in changes in gene expression in human developing cortical neurons that manifest well after cessation of exposure and that these changes are cell type-, developmental stage-, and exposure paradigm-specific.

Machine Learning Within Studies of Early-Life Environmental Exposures and Child Health: Review of the Current Literature and Discussion of Next Steps

PURPOSE OF REVIEW

The goal of this article is to review the use of machine learning (ML) within studies of environmental exposures and children's health, identify common themes across studies, and provide recommendations to advance their use in research and practice.

RECENT FINDINGS

We identified 42 articles reporting upon the use of ML within studies of environmental exposures and children's health between 2017 and 2019. The common themes among the articles were analysis of mixture data, exposure prediction, disease prediction and forecasting, analysis of complex data, and causal inference. With the increasing complexity of environmental health data, we anticipate greater use of ML to address the challenges that cannot be handled by traditional analytics. In order for these methods to beneficially impact public health, the ML techniques we use need to be appropriate for our study questions, rigorously evaluated and reported in a way that can be critically assessed by the scientific community.