Human scalp hair: geometry, biochemistry, growth parameters and mechanical characteristics
Scalp hair is increasingly being used as a testing substrate for toxins and monitoring treatment adherence. The biochemistry of human hair is assumed to be similar; however, a recent study reported higher concentration of lipids in African hair. The effect of hair curvature, if any, on drug incorporation (e.g. lipid soluble drugs) is unknown. Racial description of hair morphology is unscientific. A geometric classification of hair into 8 groups (I-straight to VIII-tightly curly) was recently proposed, however its reliability has not been confirmed. The aim of this thesis was to investigate the reliability of the geometric classification (and to assess whether it could be improved) and investigate relationships between morphology and other hair characteristics. Virgin hair was collected from 128 volunteers using a standardized protocol. Geometric measurements of hair using published templates were conducted for classification. Reliability was assessed using Kappa statistics. Characteristics assessed included mechanical properties (miniature tensile tester), growth rate and hair density (TrichoScan® trichogram), biochemistry (Vanillin assay for lipids and Fourier Transform Infrared adsorption) and imaging (Electron and Fluorescent Light Microscopy). Inter-observer agreement was poor for 8-groups (k=0.418) but improved for 6-groups (k=0.671). The intra-observer agreement also improved [ranges: k=0.444 to 0.648 (8-groups) and k=0.599 to 0.836 (6-groups)]. The yield strength of all hair groups was higher than reported for racially grouped samples. Curly hair groups had lower growth rates and tensile strengths. The TrichoScan based growth rate was for fastest for the straightest (0.72±0.3 cm/month) and slowest for the curliest (0.39 ± 0.2 cm/month) hair. No correlation with biochemistry was detected for either the 8 or 6-group classification, although a trend toward higher absorption of lipid (C-H) bands was noted for curly hair. A supervised statistical approach applied to 4 hair groups using the FTIR data improved classification success to 79% (range: 69% - 88%), which needs confirmation but would be more objective than using race for hair testing in Medicine and Forensic Science. This thesis supports a geometric classification with fewer groups (6, based kappa statistics and 4, based on biochemistry); it is also the first to report correlations between hair geometry, biochemistry and physical properties.
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