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61,005 resultsShowing papers similar to Development of a Classification Model for Physiological Parameters in Relation to Ecological Aspects Based on Cohort Data
ClearDevelopment of Cohort-Based Prediction Model for Human Health in Relation to Ecological Aspects
Researchers developed a cohort-based prediction model linking ecological factors including environmental conditions, socioeconomic constraints, and demographic parameters to human health outcomes. The model was designed to serve as a reference tool for ecosystem modeling and to assess health vulnerability to infectious diseases and environmental stressors across populations.
Aquatic ecosystem indices, linking ecosystem health to human health risks
Researchers reviewed indicators used to assess aquatic ecosystem health and found that most existing tools don't adequately capture the risks that degraded water ecosystems pose to human health and well-being. They propose a new set of combined indicators — covering chemical contaminants, pathogens, and biological markers — to better link ecosystem health monitoring to human health outcomes.
Novel concept for the healthy population influencing factors
This paper introduced a novel conceptual framework for identifying the factors that influence population health, integrating environmental, behavioral, and socioeconomic determinants. The framework is proposed as a tool for public health planning and health impact assessment.
Linking coastal environmental and health observations for human wellbeing
This paper proposes a framework for linking coastal environmental monitoring data with human health observations to create integrated coastal health indicators, identifying locations where climate change and pollution may create hotspots of health concern. The approach aims to improve understanding of how coastal environmental quality affects human wellbeing.
Development of Ecosystem Health Assessment (EHA) and Application Method: A Review
This review traces the development of ecosystem health assessment methods, comparing biological indicator approaches and index system methods and analyzing how they have been applied to assess the health of aquatic, terrestrial, and urban ecosystems under anthropogenic stress.
DNA Methylation Biomarkers-Based Human Age Prediction Using Machine Learning
A machine learning model was developed to predict biological age from DNA methylation biomarkers, demonstrating performance applicable to both healthy individuals and disease cohorts. The study contributes to the growing field of epigenetic aging clocks with potential applications in assessing environmental health impacts and disease risk.
Environmental determinants of cardiovascular-kidney-metabolic health: interactive roles of air pollution, heat waves, and green spaces
This study examined how multiple environmental stressors, including air pollution, heat waves, and green spaces, interact to influence cardiovascular disease risk in populations with cardiovascular-kidney-metabolic conditions. The findings provide evidence that these environmental factors do not act in isolation but interact in ways that affect overall disease risk. The study highlights the importance of integrated environmental and public health strategies for vulnerable populations.
Health Promotion Effects of Sports Training Based on HMM Theory and Big Data
Researchers developed a human health status evaluation model for post-exercise states by combining Hidden Markov Model (HMM) theory with big data analytics to collect and analyze physiological parameters during and after sports training. The system demonstrated the ability to assess health status and guide exercise recommendations with improved prediction accuracy.
The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science
This paper proposes an exposome framework for predicting environmental health based on systemic homeostasis, integrating data from microbial ecosystems, chemical exposures, and recycled resources to evaluate environmental illness. The approach aims to model how combined exposures disrupt biological balance in organisms and ecosystems.
Hierarchy of Demographic and Social Determinants of Mental Health
Not relevant to microplastics — this is an epidemiology study using machine learning to rank demographic and social predictors of mental health outcomes across 270,000 adults in 32 countries.
Scale validation and prediction of environmental health literacy in Brazil
Researchers surveyed nearly 400 people in Brazil to measure environmental health literacy — how well people understand the links between pollution and human health — and found that education, income, age, and ethnicity were the strongest predictors of awareness levels. The findings can help policymakers design targeted communication strategies for communities most vulnerable to environmental health risks.
Risk Factors for Cardiovascular and Metabolic Disease: Integrating Traditional and Novel Paradigms
This review integrates traditional cardiovascular risk factors with emerging environmental risk factors including microplastics, air pollution, and endocrine disruptors, arguing that a significant proportion of cardiovascular events occur in individuals considered low-risk by conventional models alone.
Estilos de vida, sostenibilidad y salud planetaria
This study examined the relationship between lifestyle habits, environmental sustainability, and planetary health, exploring how daily actions such as diet, transportation, and consumption patterns affect both human health and environmental well-being.
Public perceptions of climate change and health – A cross-sectional survey study
Researchers conducted a cross-sectional survey to assess public perceptions of the links between climate change and human health, examining awareness of how rising temperatures, extreme weather, air pollution, and environmental degradation affect morbidity and mortality. The study found variable levels of public understanding across demographic groups, with implications for health communication and climate policy engagement.
A Novel Hybrid IOT Based Artificial Intelligence Algorithm for Toxicity Prediction In The Environment And Its Effect On Human Health
Researchers proposed a hybrid IoT-based artificial intelligence framework for predicting environmental toxicity and its effects on human health, combining sensor networks with machine learning to improve real-time assessment of chemical exposure risks in the environment.
Environmental Chemicals: Integrative Approach to Human Biomonitoring and Health Effects
This review presents an integrative framework for human biomonitoring of environmental chemicals — including microplastics, heavy metals, and endocrine disruptors — linking population-level exposure data with health outcomes to inform policy decisions on chemical risk management.
Drinking water potability prediction using machine learning approaches: a case study of Indian rivers
Researchers applied machine learning techniques to predict drinking water quality in Indian rivers based on key parameters like pH, dissolved oxygen, and bacterial counts. Their models achieved high accuracy in classifying water as potable or non-potable. The study demonstrates how data-driven approaches could help developing countries monitor water safety more efficiently, especially in regions where traditional testing infrastructure is limited.
Analysis of Modifiable, Non-Modifiable, and Physiological Risk Factors of Non-Communicable Diseases in Indonesia: Evidence from the 2018 Indonesian Basic Health Research
This study analyzed modifiable, non-modifiable, and physiological risk factors for non-communicable diseases in Indonesia using national health survey data. The findings suggest that these risk factors have a significant influence on non-communicable disease prevalence, providing evidence that could inform cross-sector health promotion and early detection strategies.
Common issues of data science on the eco-environmental risks of emerging contaminants.
This review examines common methodological pitfalls in data science approaches to emerging contaminants research, highlighting issues such as data leakage, inadequate ecological complexity, and over-reliance on laboratory data. Researchers argue that future work should integrate ensemble models, spatiotemporal causal frameworks, and field-based validation to close gaps between data-driven predictions and real-world environmental outcomes.
Improving the assessment of ecosystem and wildlife health: microbiome as an early indicator
Researchers reviewed evidence that the microbiome — the community of microorganisms living in environments and within animals — can serve as an early warning system for ecosystem disturbance, rapidly reflecting the impact of human activities before other signs of harm are visible.
The Morpho-Physio-Biochemical Attributes of Urban Trees for Resilience in Regional Ecosystems in Cities: A Mini-Review
This mini-review examines the morphological, physiological, and biochemical traits that determine urban tree resilience to stressors like CO2, drought, and heat. The authors propose an integrated research framework linking ecosystem resilience to urban tree physiological responses to combined environmental stressors.
Exploring educators’ perception of issues involving Planetary Health: A qualitative study in the Brazilian Amazon
Researchers investigated how teachers in a riverside Brazilian Amazon school perceive planetary health issues, finding that while educators recognized environmental connections to community wellbeing, formal planetary health education remains largely absent from basic school curricula.
Trait emotional intelligence and ecological outcomes: the role of connectedness to nature
Researchers found that people with higher emotional intelligence show stronger connections to nature, which in turn promotes more environmentally responsible behaviors. The study suggests that environmental education programs targeting emotional intelligence could be effective at encouraging ecological action.
Predicting effects of multiple interacting global change drivers across trophic levels
Researchers proposed a framework using reaction norms to predict how multiple interacting global change drivers simultaneously affect vital rates and population dynamics across trophic levels, addressing a key challenge in ecology and conservation.