A review of sebum in mammals in relation to skin diseases, skin function, and the skin microbiome
Bibliographic record
Abstract
Diseases vary among and within species but the causes of this variation can be unclear. Immune responses are an important driver of disease variation, but mechanisms on how the body resists pathogen establishment before activation of immune responses are understudied. Skin surfaces of mammals are the first line of defense against abiotic stressors and pathogens, and skin attributes such as pH, microbiomes, and lipids influence disease outcomes. Sebaceous glands produce sebum composed of multiple types of lipids with species-specific compositions. Sebum affects skin barrier function by contributing to minimizing water loss, supporting thermoregulation, protecting against pathogens, and preventing UV-induced damage. Sebum also affects skin microbiome composition both via its antimicrobial properties, and by providing potential nutrient sources. Intra- and interspecific variation in sebum composition influences skin disease outcomes in humans and domestic mammal species but is not well-characterized in wildlife. We synthesized knowledge on sebum function in mammals in relation to skin diseases and the skin microbiome. We found that sebum composition was described for only 29 live, wild mammalian species. Sebum is important in dermatophilosis, various forms of dermatitis, demodicosis, and potentially white-nose syndrome. Sebum composition likely affects disease susceptibility, as lipid components can have antimicrobial functions against specific pathogens. It is unclear why sebum composition is species-specific, but both phylogeny and environmental effects may drive differences. Our review illustrates the role of mammal sebum function and influence on skin microbes in the context of skin diseases, providing a baseline for future studies to elucidate mechanisms of disease resistance beyond immune responses.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".