Microbe Interactions within the Skin Microbiome
Bibliographic record
Abstract
The skin is the largest human organ and is responsible for many important functions, such as temperature regulation, water transport, and protection from external insults. It is colonized by several microorganisms that interact with each other and with the host, shaping the microbial structure and community dynamics. Through these interactions, the skin microbiota can inhibit pathogens through several mechanisms such as the production of bacteriocins, proteases, phenol soluble modulins (PSMs), and fermentation. Furthermore, these commensals can produce molecules with antivirulence activity, reducing the potential of these pathogens to adhere to and invade human tissues. Microorganisms of the skin microbiota are also able to sense molecules from the environment and shape their behavior in response to these signals through the modulation of gene expression. Additionally, microbiota-derived compounds can affect pathogen gene expression, including the expression of virulence determinants. Although most studies related to microbial interactions in the skin have been directed towards elucidating competition mechanisms, microorganisms can also use the products of other species to their benefit. In this review, we will discuss several mechanisms through which microorganisms interact in the skin and the biotechnological applications of products originating from the skin microbiota that have already been reported in the literature.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".