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This study found that ATR-FTIR spectroscopy combined with machine learning effectively differentiated alopecia areata patients from healthy controls with an AUC of 0.85, and also showed promise in predicting treatment response, particularly through alterations in the Amide I band.

This study successfully used ATR FT-IR spectroscopy to uniquely identify or differentiate human hairs by detecting specific spectral features associated with individual hair characteristics and cosmetic residues on hair surfaces.

Hair removal methods damage the skin barrier and affect substance penetration.

This study indicates potential differences in the hair spectra of individuals with mood disorders, suggesting a promising, yet preliminary, method for noninvasive prediagnosis.

This study observed that the molecular structure of keratins in hair fibres' discrete transition zone differs in breast cancer patients, with altered lipid ester absorption bands reverting post-cancer removal.