Exploring the human hair follicle microbiome*
Bibliographic record
Abstract
Human hair follicles (HFs) carry complex microbial communities that differ from the skin surface microbiota. This likely reflects that the HF epithelium differs from the epidermal barrier in that it provides a moist, less acidic, and relatively ultraviolet light-protected environment, part of which is immune-privileged, thus facilitating microbial survival. Here we review the current understanding of the human HF microbiome and its potential physiological and pathological functions, including in folliculitis, acne vulgaris, hidradenitis suppurativa, alopecia areata and cicatricial alopecias. While reviewing the main human HF bacteria (such as Propionibacteria, Corynebacteria, Staphylococci and Streptococci), viruses, fungi and parasites as human HF microbiome constituents, we advocate a broad view of the HF as an integral part of the human holobiont. Specifically, we explore how the human HF may manage its microbiome via the regulated production of antimicrobial peptides (such as cathelicidin, psoriasin, RNAse7 and dermcidin) by HF keratinocytes, how the microbiome may impact on cytokine and chemokine release from the HF, and examine hair growth-modulatory effects of antibiotics, and ask whether the microbiome affects hair growth in turn. We highlight major open questions and potential novel approaches to the management of hair diseases by targeting the HF microbiome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".