We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Preliminary Report: Scalable Detection of Microplastics and Nanoplastics in Human Urine
AI summary Read the abstract
A non-destructive optical imaging workflow successfully detected both micro- and nanoplastic signals in spiked human urine, showing reproducible differences in spatial heterogeneity and aggregation behavior at varying concentrations. This scalable, non-invasive detection method could enable routine biomonitoring of human plastic exposure through urine sampling.
Microplastics and nanoplastics (MNPs) are emerging environmental contaminants with potential systemic health effects. Urine offers a non-invasive, repeatable matrix for exposure assessment, yet scalable detection methods remain limited. In this preliminary study, spiked human urine samples were analyzed using a non-destructive optical imaging and computational analysis workflow. Reproducible differences in spatial heterogeneity, aggregation behavior, and temporal evolution were observed, with nanoplastic conditions showing earlier and more pronounced organization at low concentrations. These findings demonstrate feasibility of detecting both micro- and nanoplastic-associated signals in intact biological fluids and support further development of scalable, urine-based monitoring approaches. Keywords: microplastics, nanoplastics, urine, optical imaging, scalable detection, biomonitoring
More Papers Like This
Preliminary Report: Scalable Detection of Microplastics and Nanoplastics in Human Urine
AI summary Read the abstract
A preliminary study validated a non-destructive optical imaging workflow for detecting both micro- and nanoplastics in spiked human urine samples, observing reproducible differences in particle aggregation and spatial organization between microplastic and nanoplastic conditions at low concentrations. A scalable, non-invasive urine-based detection method would transform biomonitoring capacity, enabling population-level tracking of internal microplastic exposure without the invasive sampling currently required.
Urine Diagnostics as a Scalable Platform for Assessing Microplastics and Nanoplastics as Environmental Exposures
AI summary Read the abstract
Urine is proposed as a scalable, non-invasive matrix for monitoring systemic microplastic and nanoplastic exposure, with early evidence of MNPs detectable in human urine and optical-computational methods offering high-throughput potential. Establishing reliable urinary biomonitoring would enable longitudinal population studies that are essential for understanding cumulative plastic body burden and its health consequences.
Urine Diagnostics as a Scalable Platform for Assessing Microplastics and Nanoplastics as Environmental Exposures
AI summary Read the abstract
Urine offers a promising non-invasive matrix for monitoring systemic microplastic and nanoplastic exposure, as the kidneys filter circulating particles that can then be detected and quantified. Developing scalable, contamination-controlled urine-based diagnostics could enable large-scale population surveillance of human MNP burden without requiring invasive tissue sampling.
Toward Scalable Detection of Microplastics and Nanoplastics in Human Biological Systems: Alignment with STOMP Objectives and Preliminary Feasibility Data
AI summary Read the abstract
This preliminary study described a non-destructive, image-based optical framework that detects microplastics and nanoplastics in intact biofluids such as urine and blood without sample destruction, showing reproducible spatial and temporal signal patterns in spiked matrices. The authors proposed this approach as a pathway toward scalable, high-throughput monitoring of human microplastic exposure aligned with the ARPA-H STOMP program objectives.
Toward Scalable Detection of Microplastics and Nanoplastics in Human Biological Systems: Alignment with STOMP Objectives and Preliminary Feasibility Data
AI summary Read the abstract
This preliminary study described a non-destructive, image-based optical framework that detects microplastics and nanoplastics in intact biofluids such as urine and blood without sample destruction, showing reproducible spatial and temporal signal patterns in spiked matrices. The authors proposed this approach as a pathway toward scalable, high-throughput monitoring of human microplastic exposure aligned with the ARPA-H STOMP program objectives.
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.