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Metabolites in biofluids can serve as biomarkers for diagnosing diseases and monitoring body conditions. Among the available biofluids, interstitial fluid (ISF) in the skin has garnered considerable attention owing to its advantages, which include inability to clot, easy access to the skin, and possibility of incorporating wearable devices. However, the scientific understanding of skin ISF composition is limited. In this study, we aimed to compare metabolites between skin dialysate containing metabolites from the skin ISF and venous blood (plasma) samples, both collected under resting states. We collected forearm skin dialysate using intradermal microdialysis alongside venous blood (plasma) samples from 12 healthy young adults. We analyzed these samples using capillary electrophoresis–fourier transform mass spectrometry–based metabolomics (CE–FTMS). Significant positive correlations were observed in 39 metabolites between the skin dialysate and plasma, including creatine (a mitochondrial disease biomarker), 1-methyladenosine (an early detection of cancer biomarker), and trimethylamine N-oxide (a posterior predictor of heart failure biomarker). Based on the Human Metabolome Technologies database, we identified 12 metabolites unique to forearm skin dialysate including nucleic acids, benzoate acids, fatty acids, amino acids, ascorbic acid, 3-methoxy-4-hydroxyphenylethyleneglycol (an Alzheimer's disease biomarker), and cysteic acid (an acute myocardial infarction biomarker). We show that some venous blood biomarkers may be predicted from skin dialysate or skin ISF, and that these fluids may serve as diagnostic and monitoring tools for health and clinical conditions. • Skin interstitial fluid offers non-clotting, easy access from the skin surface. • Identified 12 key metabolites in skin dialysate by using CE–FTMS–based metabolomics. • Skin dialysate positively correlates with plasma in 39 metabolites. [ABSTRACT FROM AUTHOR]

Background: Videomicroscopy can be used to observe skin capillaries easily and non‐invasively. In this study, we develop an algorithm that can handle skin regions by combining color component separation methods as pre‐processing based on blood vessel extraction filtering. Materials and Methods: Images of skin on the inner upper arm were acquired using videomicroscopy. An algorithm using independent component analysis (ICA) and the Frangi filter was constructed, and capillary regions were extracted. The capillary blood vessel extraction images were compared with ground truth to verify accuracy. An evaluation of the physiological responses of skin exposed to carbon dioxide (CO2) water, local heating, and methyl nicotinate was performed to test blood vessels with different mechanisms of action and layer depth. Results: Based on a comparison with ground truth images, a Dice coefficient of 0.82 was calculated. In examining physiological responses to stimuli on the skin, it was found that exposure to CO2 for 2 minutes significantly increased the capillary area compared with the steady state. Conclusion: An algorithm to extract capillaries from skin images using ICA and the Frangi filter method was proposed. Results suggest that this algorithm can quantitatively analyze physiological changes in capillaries on the skin surface. [ABSTRACT FROM AUTHOR]

The cover image is based on the Original Article Skin capillary extraction technique based on independent component analysis and Frangi filter using videomicroscopy by Akihiko Oharazawa et al., https://doi.org/10.1111/srt.12850. [ABSTRACT FROM AUTHOR]