We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Microplastics: a cancer-causing agent for humans and prospects for identification using AI and modern approaches
AI summary Read the abstract
Tiny plastic particles called microplastics, smaller than a grain of rice, some invisible to the eye, can get into your body through the air you breathe, the food you eat, and even your skin, where they may trigger harmful biological reactions linked to cancer risk. This review paper pulls together existing research on how these particles behave in the body and highlights promising AI-based tools that could help scientists detect and study microplastics more effectively. The takeaway: microplastics are a growing health concern worth watching, and better detection methods are key to understanding the real risks they pose.
Microplastics (MPs) are synthetic/polymeric particulate solids having a regular shape with dimensions ranging from 1 µm to 5 mm and hydrophobic nature [ 1 ]. However, microplastics can be of irregular shapes as well. Most MPs in various ecosystems are secondary, weathered MPs, which often exhibit very irregular and unusual shapes. The major source of MPs in the environment is due to their non-biodegradable nature, which is poorly biodegradable or decomposed completely and narrowed down in soil or water in a very micro-size range [ 2 ]. These plastics can enter the body via three pathways i.e., inhalation, ingestion and skin. Cellular uptake involves a regulated mechanism with complex biomolecular interactions and this uptake results in a biological response which results in the toxic effects of these MPs (Fig. 1 ) [ 3 ].
More Papers Like This
Application of Artificial Intelligence to Improve Microplastic Identification
AI summary Read the abstract
This review examines how artificial intelligence -- including machine learning and computer vision approaches -- can improve the identification, classification, and measurement of microplastics during environmental sampling and analysis. AI-assisted identification addresses a major bottleneck in microplastic research by reducing the time and expertise required to process large sample volumes, enabling faster and more consistent data generation needed to assess human health exposure at scale.
Prospective Application of Artificial Intelligence Towards the Detection, and Classifications of Microplastics with Bibliometric Analysis
AI summary Read the abstract
Using bibliometric analysis to map the microplastics research landscape, this study proposes integrating AI techniques — including machine learning with FTIR spectroscopy, holographic imaging, and 3D modeling — to overcome the labor-intensive limitations of traditional MP identification and classification methods. Scalable, AI-driven detection tools are critical for accelerating MP monitoring and research, enabling the high-throughput data collection needed to understand population-level exposure and inform regulatory standards.
Micro(Nano)Plastics and the Risk of Cancer Development: Calling for Attention to a Macro Issue
AI summary Read the abstract
Researchers examined the potential role of micro- and nanoplastics in cancer development, reviewing mechanistic evidence for how these ubiquitous particles may disrupt cellular processes and promote tumor-associated pathways. The paper calls for greater scientific and regulatory attention to the oncological risks of chronic low-level plastic particle exposure.
Micro-nano Plastics: Development of Carcinogenicity
AI summary Read the abstract
Researchers reviewed how micro- and nanoplastics may contribute to cancer development, examining the biological pathways by which these particles — now found throughout the human body — could trigger cell damage, inflammation, and potentially carcinogenic processes.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.