Skin pH, Atopic Dermatitis, and Filaggrin Mutations
Bibliographic record
Abstract
BACKGROUND: The acidic pH of the skin plays a role in antimicrobial defense by regulating the bacterial skin flora and aspects of barrier. Filaggrin is a co-factor in maintaining a low skin pH because of its degradation into acidic amino acids. Accordingly, lack of filaggrin due to filaggrin mutations may influence skin pH. OBJECTIVE: We aimed to determine the epidermal pH in different groups stratified by filaggrin mutations and atopic dermatitis. Further, we investigated the changes in pH according to severity of mutational status among patients with dermatitis, irrespective of skin condition. METHODS: pH was measured with a multiprobe system pH probe (PH 905), and the study population was composed of 67 individuals, who had all been genotyped for 3 filaggrin mutations (R501X, 2282del4, R2447X). RESULTS: We found no clear pattern in relation to filaggrin mutation carrier status. Individuals with wild-type filaggrin displayed both the most acidic and most alkaline values independent of concomitant skin disease; however, no statistical differences between the groups were found. CONCLUSIONS: The lack of significant diversity in skin pH in relation to filaggrin mutation carrier status suggests that the effect of filaggrin mutations on skin pH is not pronounced.
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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 | 0.001 | 0.001 |
| 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) | 0.004 | 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".