Back

Bactogram: Spatial Analysis of Bacterial Colonization in Epidermal Wounds

Wallblom, K.; Forsberg, F.; Lundgren, S.; Fisher, J.; Cardoso, J.; Petruk, G.; Strömdahl, A.-C.; Saleh, K.; Puthia, M.; Schmidtchen, A.

2024-04-19 dermatology
10.1101/2024.04.18.24305961 medRxiv
Show abstract

Skin barrier damage and subsequent development of harmful microbiota contribute to conditions such as wound infections, atopic dermatitis, and chronic wounds, which impact millions of people globally and pose a significant economic burden on healthcare systems. Established microbial sampling methods, such as swabs and tissue biopsies, provide limited information on the spatial distribution of bacteria. We here describe a new method that produces a visual map of the distribution of cultivable bacteria, denoted "Bactogram", across the whole wound and surrounding skin, suitable for image-based quantification. As part of an exploratory endpoint in a clinical trial (NCT05378997) we applied the Bactogram method to 48 suction blister wounds in 24 healthy volunteers. Bacteria developed in all wounds, predominantly on the skin under the dressing and near wound edges. Two quantification methods, based on visual scoring and image analysis, demonstrated high inter-, and intra-rater agreement and were used to characterize bacterial re-colonization during epidermal wound healing. We also demonstrated proof of concept that the method can be used with chromogenic agar to enable spatial identification of pathogenic bacterial species, such as Staphylococcus aureus. In conclusion, this study introduces a simple method for sampling bacteria over large areas and generating a bacterial map that can identify spatial variations in bacterial composition and abundance in skin and wound conditions.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.