The gut-skin axis: a bi-directional, microbiota-driven relationship with therapeutic potential
Bibliographic record
Abstract
This review explores the emerging term "gut-skin axis" (GSA), describing the bidirectional signaling that occurs between the skin and the gastrointestinal tract under both homeostatic and disease conditions. Central to GSA communication are the gut and skin microbiota, the microbial communities that colonize these barrier surfaces. By influencing diverse host pathways, including innate immune, vitamin D receptor, and Aryl hydrocarbon receptor signaling, a balanced microbiota contributes to both tissue homeostasis and host defense. In contrast, microbiota imbalance, or dysbiosis at one site, can lead to local barrier dysfunction, resulting in the activation of signaling pathways that can disrupt tissue homeostasis at the other site, potentially leading to inflammatory skin conditions such as atopic dermatitis and psoriasis, or gut diseases like Inflammatory Bowel Disease. To date, most research on the GSA has examined the impact of the gut microbiota and diet on skin health, but recent studies show that exposing the skin to ultraviolet B-light can beneficially modulate both the gut microbiome and intestinal health. Thus, despite the traditional focus of clinicians and researchers on these organ systems as distinct, the GSA offers new opportunities to better understand the pathogenesis of cutaneous and gastrointestinal diseases and promote health at both sites.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".