Toward co-design of an AI solution for detection of diarrheal pathogens in drinking water within resource-constrained contexts.

Despite successes on the Sustainable Development Goals for access to improved water sources and sanitation, many low and middle-income countries (LMICs) continue to struggle with high rates of diarrheal disease. In Guatemala, 98% of water sources are estimated to have E. coli contamination. This project moves toward a novel low-cost approach to bridge the gap between the microbiologic identification of E. coli and the vast impact that this pathogen has on human health within marginalized communities using co-designed community-based tools, low-cost technology, and AI. An agile co-design process was followed with water quality stakeholders, community staff, and local graphic design artists to develop a community water quality education mobile app. A series of alpha- and beta-testers completed interactive demonstration, feedback, and in-depth interview sessions. A microbiology lab in Guatemala developed and piloted field protocols with lay community workers to collect and process water samples. A preliminary artificial intelligence (AI) algorithm was developed to detect the presence of E. coli in images generated from community-derived water samples. The mobile app emerged as a pictorial and audio-driven community-facing tool. The field protocol for water sampling and testing was successfully implemented by lay community workers. Feedback from the community workers indicated both desire and ability to conduct the water sampling and testing protocol under field conditions. However, images derived from the low-cost $2 microscope in field conditions were not of a suitable quality for AI object detection of E. coli, and additional low-cost technologies are being considered. The preliminary AI object detection algorithm from lab-derived images performed at 94% accuracy in identifying E. coli in comparison to the Chromocult gold-standard.

No genome-wide DNA methylation changes found associated with medium-term reduced graphene oxide exposure in human lung epithelial cells.

The presence of nanomaterials in our everyday life is ever increasing, and so too are concerns about the possible health consequences of exposure to them. While evidence of their biological activity is growing, there is still scant knowledge of the epigenetic mechanisms that could be at play in these processes. Moreover, the great variability in the chemical and physical structures of these compounds handicaps the study of their possible health risks. Here we have synthesized reduced graphene oxide (rGO) through the thermal exfoliation/reduction of graphite oxide, and characterized the resulting material. We have then made use of Illumina's MethylationEPIC arrays and bisulphite pyrosequencing to analyse the genome-wide and global DNA methylation dynamics associated with the medium-term exposure of human lung epithelial cells to rGO at concentrations of 1 and 10 µg/mL. The results show no genome-wide or global DNA methylation changes associated with either condition. Our observations thus suggest that medium-term rGO exposure does not have significant effects on the DNA methylation patterns of human lung epithelial cells.