Face masks are used to protect the wearer from harmful external air and to prevent transmission of viruses from air exhaled by potentially infected wearers to the surrounding people. In this study, we examined the potential utility of masks for collecting viruses contained in exhaled breath and detected the collected viruses via various molecular tests. Using KF94 masks, the inner electrostatic filter was selected for virus collection, and an RNA extraction protocol was developed for the face mask. Virus detection in worn mask samples was performed using PCR and rolling circle amplification (RCA) tests and four different target genes (N, E, RdRp, and ORF1ab genes). The present study confirmed that the mask sample tests showed positive SARS-CoV-2 results, similar to the PCR tests using nasopharyngeal swab samples. In addition, the quantity of nucleic acid collected in the masks linearly increased with wearing time. These results suggest that samples for SARS-CoV-2 tests can be collected in a noninvasive, quick, and easy method by simply submitting worn masks from subjects, which can significantly reduce the hassle of waiting at airports or public places and concerns about cross-infection. In addition, it is expected that miniaturization technology will integrate PCR assays on face masks in the near future, and mask-based self-diagnosis would play a significant role in resolving the pandemic situation.

In this paper, we propose a color image segmentation method based on gravitational clustering using neighbor relations in the spatial domain and distance information in RGB space among pixels. Most clustering-based segmentation algorithms use only color space distances after pixels are mapped from the spatial domain to color space, ignoring their neighbor relations; but we use both information. We use gravitational clustering, which imitates the Law of Gravity, and the gravitational force is applied only to neighboring clusters. The results show that the proposed method is effective in finding exact boundaries of regions.