Geometric classification of scalp hair for valid drug testing, 6 more reliable than 8 hair curl groups

Mkentane, Kwezikazi; Wyk, Jennifer C. Van; Sishi, N.; Gumedze, Freedom; Ngoepe, Malebogo; Davids, Lester M.; Khumalo, Nonhlanhla P.

doi:10.1371/journal.pone.0172834

Cited by 16 publications

(24 citation statements)

References 29 publications

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“…Hair types: straight to very curly; chemically untreated for last 12 months. Curl type classification was done per donor and per fiber (I, straight, to VI, tightly curly), based on a modification (Mkentane et al, 2017) of the segmentation tree analysis method (de la Mettrie et al, 2007). Each experimental step was photographed as digital evidence and traceability of fiber changes.…”

Section: Methodsmentioning

confidence: 99%

“…The segmentation tree analysis method is impractical for a large pool of medium to highly curled fibers because of the time it takes to perform and the variability in costs of human evaluation. In this research, we classified fibers according to a modified segmentation tree analysis method protocol of six curl type groups described in Mkentane et al (2017), in which classes V and VI, as well as VII and VIII, each have been collapsed into single groups. To estimate curvature quantitatively, three curvature descriptors were used: index (C i ), width (C w ), and depth (C d ).…”

Section: Introductionmentioning

confidence: 99%

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Understanding Curly Hair Mechanics: Fiber Strength

Cloete

Khumalo

Ngoepe

2020

Journal of Investigative Dermatology

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The relationship between the geometric and mechanical profiles of hair fibers has been studied, with special focus on curly samples. Incidental observations pointed to a significantly different viscoelastic character with varying curliness. Further investigations confirmed initial observations, showing an initial distinct toe region behavior for curly fibers on the stress-strain plot, which is absent for straight fibers. This behavior suggested a difference in the viscoelastic nature of the curly fiber that is linked to mechanical energy stored in the fiber. Results also suggest that the strength of hair depends on two main components, and further pointed out that de facto methods of tensile testing may erode curly fiber strength during preparation. The main outcome of this study is that the tensile strength (s T) of hair fibers is composed of two (rather than one main) components, namely the toe region (s t) and the elastic region (s ε), so that: s T ¼ s t þ s ε. For noncurly fibers, the greatest part of fiber strength is derived from s ε , while s t z 0. For curly fibers, s t (i.e., springiness) adds significantly to the overall strength, even though s ε remains the major contributor. Although these results require validation in larger studies, they are significant in the current understanding of curly hair. Also, they may represent a fundamental shift from the current understanding of tensile testing of human hair in general.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding Curly Hair Mechanics: Fiber Strength

Cloete

Khumalo

Ngoepe

2020

Journal of Investigative Dermatology

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“…Our novel computational tool streamlines the analysis of curvature and cross-sectional geometry of hair fibers. This tool requires no input from a user (other than the location of the image files), and so removes inter-observer error and subjectivity in assessing curvature 13,17 . It also saves time and improves accuracy and reproducibility because tasks that would have previously required hours of labor can now be executed unsupervised by an automated program that requires no additional cost or training to use.…”

Section: Discussionmentioning

confidence: 99%

High-throughput phenotyping methods for quantifying hair fiber morphology

Lasisi

Zaidi

Webster

et al. 2020

Preprint

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Quantifying the continuous variation in human scalp hair morphology is of interest to anthropologists, geneticists, dermatologists and forensic scientists, but existing methods for studying hair form are time-consuming and not widely used. Here, we present a high-throughput sample preparation protocol for the imaging of both longitudinal (curvature) and cross-sectional scalp hair morphology. Additionally, we describe and validate a new Python package designed to process longitudinal and cross-sectional hair images, segment them, and provide measurements of interest. Lastly, we apply our methods to an admixed African-European sample (n=140), demonstrating the benefit of quantifying hair morphology over qualitative classification or racial categories, and providing evidence against the long-held belief that cross-sectional morphology predicts curvature.

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“…However, a taxonomy based on racial differentiators is subjective and fails to consider diversity that arises from variability within a race, as well as distinctions emanating from genetics, gender, lifestyle, stress-states, aging, nutrition, drugs or disease. Other taxonomies, based on geometric descriptors only, have been proposed but these still fall short of providing an integrated view on hair data for interdisciplinary purposes (de la Mettrie et al, 2007; Loussouarn et al, 2007; Mkentane et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Systems Approach to Human Hair Fibers: Interdependence Between Physical, Mechanical, Biochemical and Geometric Properties of Natural Healthy Hair

et al. 2019

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Contextual interpretation of hair fiber data is often blind to the effects of the dynamic complexity between different fiber properties. This intrinsic complexity requires systems thinking to decipher hair fiber accurately. Hair research, studied by various disciplines, follows a reductionist research approach, where elements of interest are studied from a local context with a certain amount of detachment from other elements or contexts. Following a systems approach, the authors are currently developing a cross-disciplinary taxonomy to provide a holistic view of fiber constituents and their interactions within large-scale dynamics. Based on the development process, this paper presents a review that explores the associated features, interrelationships and interactive complexities between physical, mechanical, biochemical and geometric features of natural, healthy hair fibers. Through the review, the importance of an appropriate taxonomy for interpreting hair fiber data across different disciplines is revealed. The review also demonstrates how seemingly unrelated fiber constituents are indeed interdependent and that these interdependencies may affect the behavior of the fiber. Finally, the review highlights how a non-integrative approach may have a negative impact on the reliability of hair data interpretation.

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Geometric classification of scalp hair for valid drug testing, 6 more reliable than 8 hair curl groups

Cited by 16 publications

References 29 publications

Understanding Curly Hair Mechanics: Fiber Strength

Understanding Curly Hair Mechanics: Fiber Strength

High-throughput phenotyping methods for quantifying hair fiber morphology

Systems Approach to Human Hair Fibers: Interdependence Between Physical, Mechanical, Biochemical and Geometric Properties of Natural Healthy Hair

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