Baseline sebum IL‐1α is higher than expected in afro‐textured hair: a risk factor for hair loss?*
Bibliographic record
Abstract
OBJECTIVE: To investigate changes in sebum cytokines in response to hair cosmetics. Design and setting A prospective study at a University hospital. METHODS: We used a novel method for scalp surface sebum collection (Sebutape(®)) on three visits, sequentially a week apart, to investigate changes in six cytokines in 36 healthy women before and after shampoo and compared various chemical treatments (ammonium thioglycolate, "lye" sodium hydroxide and "no-lye" guanidine hydroxide relaxers) performed by a professional hairdresser. RESULTS: Significant levels detected were IL-1 alpha (IL-1α) and IL-1 receptor antagonist (IL-1ra), which were higher in untreated scalp vs. forehead: P < 0.001. Baseline levels of scalp sebum IL-1α were 18 times higher than IL-1ra. The levels of IL-1α decreased uniformly after shampoo (visit 1) and various chemical treatments (both crown and vertex all P < 0.001 - visit 2) but increased on follow-up at visit 3. Decreases in IL-1ra mimicked IL-1α at the vertex [after shampoo (P = 0.018) and visit 3 (P = 0.014)], but not on the crown, a finding which may suggest site-specific scalp predisposition to inflammation. The ratio of IL-1ra/IL-1α increased in all groups after all chemical treatments and on follow-up (all P < 0.001) but was surprisingly not significantly different from natural hair that underwent shampoo. LIMITATIONS: A wider cytokine panel may reveal response differences in treatment groups. CONCLUSIONS: Baseline inflammatory scalp cytokines are higher than expected and reduce with shampooing. Scrutiny of the influence of hair moisturizer formulations and shampoo intervals and studies investigating pro-fibrotic cytokines are required. This may elucidate the predilection of afro-textured hair to scarring alopecia.
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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 | 0.001 |
| 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) | 0.002 | 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".