OBJECTS: Health Behaviours in School-aged Children (HBSC) is an international survey programme aiming to investigate adolescents' health behaviours, subjective perception of health status, wellbeing, and the related contextual information. Our scoping review aimed to synthesise the evidence from HBSC about the relationship between family environmental contributors and adolescents' health-related outcomes. METHODS: We searched previous studies from six electronic databases. Two researchers identified the qualified publications independently by abstract and full-text screening with the assistance of an NLP-based AI instrument, ASReview. Publications were included if they were based on HBSC data and investigated the effects of family environment on adolescents' health outcomes. Researches addressed family-related factors as mediators or moderators were also included. RESULTS: A total of 241 articles were included. Family environmental contributors could be mapped into six categories: (1) Demographic backgrounds (N = 177); (2) General family's psycho-socio functions (N = 44); (3) Parenting behaviours (N = 100); (4) Parental health behaviours (N = 7); (5) Family activities (N = 24); and (6) Siblings (N = 7). Except for 75 papers that assessed family variables as moderators (N = 70) and mediators (N = 7), the others suggested family environment was an independent variable. Only five studies employed the data-driven approach. CONCLUSION: Our results suggest most research studies focussed on the influences of family demographic backgrounds on adolescents' health. The researches related to parental health behaviours and siblings are most inadequate. Besides, we recommend further research studies to focus on the mediator/moderator roles of the family, for exploring the deep mechanism of the family's impacts. Also, it would be valuable to consider data-driven analysis more in the future, as HBSC has mass variables and data.

• Klíčová slova
• AI-assisted scoping review, HBSC database, adolescents’ health, family environment, parenting behaviour,
• Publikační typ
• časopisecké články MeSH
• scoping review MeSH

Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-based epidemiology, and advancing environmental toxicology research. In recent years, artificial intelligence (AI) has been increasingly applied to enhance chemical analysis and monitoring of contaminants in environmental water and wastewater. However, their specific roles targeting pharmaceuticals and personal care products (PPCPs) have not been reviewed sufficiently. This review aims to narrow the gap by highlighting, scoping, and discussing the incorporation of AI during the detection and quantification of PPCPs when utilising chemical analysis equipment and interpreting their monitoring data for the first time. In the chemical analysis of PPCPs, AI-assisted prediction of chromatographic retention times and collision cross-sections (CCS) in suspect and non-target screenings using high-resolution mass spectrometry (HRMS) enhances detection confidence, reduces analysis time, and lowers costs. AI also aids in interpreting spectroscopic analysis results. However, this approach still cannot be applied in all matrices, as it offers lower sensitivity than liquid chromatography coupled with tandem or HRMS. For the interpretation of monitoring of PPCPs, unsupervised AI methods have recently presented the capacity to survey regional or national community health and socioeconomic factors. Nevertheless, as a challenge, long-term monitoring data sources are not given in the literature, and more comparative AI studies are needed for both chemical analysis and monitoring. Finally, AI assistance anticipates more frequent applications of CCS prediction to enhance detection confidence and the use of AI methods in data processing for wastewater-based epidemiology and community health surveillance.

• Klíčová slova
• Artificial intelligence, Contaminants of emerging concern, High-resolution mass spectrometry, PPCPs, Quantitative structure retention relationship, Suspect and non-targeted screening, Wastewater-based epidemiology,
• MeSH
• chemické látky znečišťující vodu * analýza   MeSH
• kosmetické přípravky * analýza   MeSH
• léčivé přípravky analýza   MeSH
• monitorování životního prostředí * metody   MeSH
• odpadní voda * chemie   analýza   MeSH
• umělá inteligence * MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• Názvy látek
• chemické látky znečišťující vodu * MeSH
• kosmetické přípravky * MeSH
• léčivé přípravky MeSH
• odpadní voda * MeSH