Environmental Health

Explicating the transformative role of artificial intelligence in designing targeted nanomedicine.

INTRODUCTION: Artificial intelligence (AI) has emerged as a transformative force in nanomedicine, revolutionizing drug delivery, diagnostics, and personalized treatment. While nanomedicine offers precise targeted drug delivery and reduced toxic effects, its clinical translation is hindered by biological complexity, unpredictable in vivo behavior, and inefficient trial-and-error approaches.

ShieldVLM: Safeguarding the Multimodal Implicit Toxicity via Deliberative Reasoning with LVLMs

Toxicity detection in multimodal text-image content faces growing challenges, especially with multimodal implicit toxicity, where each modality appears benign on its own but conveys hazard when combined. Multimodal implicit toxicity appears not only as formal statements in social platforms but also prompts that can lead to toxic dialogs from Large Vision-Language Models (LVLMs). Despite the succ...

Mapping Regional Meteorological Processes to Ozone Variability in the North China Plain and the Yangtze River Delta, China.

High-concentration ozone threatens human health and ecosystems, modulated by dynamic, multiscale meteorological processes. Existing machine learning s...

Molecular Insights into the Binding of Organophosphate Flame Retardant Key Metabolites with Mineralocorticoid and Estrogen Receptors.

Flame retardants (FR) encompass a wide range of chemicals designed to inhibit and reduce the spread of fire by forming protective layers on materials....

Brick Kiln Dataset for Pakistan's IGP Region Using AI.

Brick kilns are a major source of air pollution in Pakistan, with many operating without regulation. A key challenge in Pakistan and across the Indo-G...

Machine learning-assisted source identification and probabilistic ecological-health risk assessment of heavy metal(loid)s in urban park soils.

The accumulation of heavy metal(loid)s (HMs) in the soils of urban parks in industrial cities has raised global concerns because of their environmenta...

Artificial Intelligence in Molecular Optimization: Current Paradigms and Future Frontiers.

Molecular optimization plays a pivotal role in many domains since it holds promise for improving the properties of lead molecules. The advent of artif...

An Explanation of Intrinsic Self-Correction via Linear Representations and Latent Concepts

We provide an explanation for the performance gains of intrinsic self-correction, a process where a language model iteratively refines its outputs w...

Hidden threats beneath: uncovering the bio-accessible hazards of chromite-asbestos mine waste and their impacts on rice components via multi-machine learning algorithm.

The chromite-asbestos mining leaves behind tonnes of toxic waste, contaminating nearby agricultural fields with potentially toxic elements (PTEs). Ove...

Illusions of Intimacy: Emotional Attachment and Emerging Psychological Risks in Human-AI Relationships

Emotionally responsive social chatbots, such as those produced by Replika and Character.AI, increasingly serve as companions that offer empathy, sup...

An AI-driven framework for the prediction of personalised health response to air pollution

Air pollution poses a significant threat to public health, causing or exacerbating many respiratory and cardiovascular diseases. In addition, climat...

Prediction of airborne bacterial concentrations and identification of critical factors in contaminated waste facilities: Insights into interpretable machine learning models.

The efficient prediction of airborne bacterial concentrations is crucial for better understanding and management of environmental sanitation risks in ...

Interpretable machine learning models for predicting the antitumor effects of metal and metal oxide nanomaterials.

Understanding the toxic behavior of metal and metal oxide nanoparticles (M/MOx NPs) is essential for effective tumor diagnosis and treatment, yet gene...

AI-driven approaches for air pollution modelling: A comprehensive systematic review.

In recent years, air quality levels have become a global issue with the rise of harmful pollutants and their effects on climate change. Urban areas ar...

SERS-ATB: A comprehensive database server for antibiotic SERS spectral visualization and deep-learning identification.

The rapid and accurate identification of antibiotics in environmental samples is critical for addressing the growing concern of antibiotic pollution, ...

Distribution characteristics of volatile organic compounds and its multidimensional impact on ozone formation in arid regions based on machine learning algorithms.

Volatile Organic Compounds (VOCs) are key components of atmospheric pollution and play a critical role in ozone (O) formation. Understanding their dis...

Artificial intelligence enhanced microfluidic system for multiplexed point-of-care-testing of biological thiols.

Cysteamine (CA) serves as a cystine-depleting agent employed in the management of cystinosis and a range of other medical conditions. Monitoring blood...

Amazon's climate crossroads: analyzing air pollution and health impacts under machine learning-based temperature increase scenarios in Northern Mato Grosso, Brazil.

Air pollution has long been a public health concern in South America, now increasingly linked to climate change. In Brazil, this issue is particularly...

Synergistic Integration of Aggregation-Induced Emission and FRET Mechanisms in Conjugated Polymers via Molecular Engineering for Ultrasensitive, Rapid, and Discriminative Detection of Perfluoroalkyl Substances.

The global contamination of water bodies by persistent organic pollutants (perfluoroalkyl substances (PFAS)) has generated significant societal concer...

Artificial Intelligence Approaches to Modeling Equivalent Circulating Density for Improved Drilling Mud Management.

Proper management of equivalent circulating density (ECD) plays a crucial role in drilling processes since poor control can lead to serious well contr...