Effect of shampoo, conditioner and permanent waving on the molecular structure of human hair
Bibliographic record
Abstract
The hair is a filamentous biomaterial consisting of the cuticle, the cortex and the medulla, all held together by the cell membrane complex. The cortex mostly consists of helical keratin proteins that spiral together to form coiled-coil dimers, intermediate filaments, micro-fibrils and macro-fibrils. We used X-ray diffraction to study hair structure on the molecular level, at length scales between ∼3-90 Å, in hopes of developing a diagnostic method for diseases affecting hair structure allowing for fast and noninvasive screening. However, such an approach can only be successful if common hair treatments do not affect molecular hair structure. We found that a single use of shampoo and conditioner has no effect on packing of keratin molecules, structure of the intermediate filaments or internal lipid composition of the membrane complex. Permanent waving treatments are known to break and reform disulfide linkages in the hair. Single application of a perming product was found to deeply penetrate the hair and reduce the number of keratin coiled-coils and change the structure of the intermediate filaments. Signals related to the coiled-coil structure of the α-keratin molecules at 5 and 9.5 Å were found to be decreased while a signal associated with the organization of the intermediate filaments at 47 Å was significantly elevated in permed hair. Both these observations are related to breaking of the bonds between two coiled-coil keratin dimers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".