Exploring the Impact of Skin Care Routines on the Skin Microbiome and Possible Skin Disease Risk—A Pilot Study
Bibliographic record
Abstract
Background/Objectives: Unceasing exposure of skin and its microbiome to various external and internal factors influences its health. Any imbalance ensuing may result in dysbiosis and consequently skin diseases. Thus, it becomes critical to identify and recognize the influence of several factors on the skin microbiome and various disorders associated with it. Methods: In the current investigation, we studied the skin microbiomes of 37 subjects using the next-generation shotgun sequencing method and compared them with 48 healthy subjects from the Human Microbiome Project (HMP). This work focused on the analysis of the impact of different skin care routines—use of sunscreen and moisturizers—on the skin microbiome and related skin diseases. Results: Differences were observed between the microbiomes of subjects who were using only moisturizer (p = 3.1 × 10−5) or moisturizer with sunscreen (p = 3.2 × 10−13) and those who did not use these products at all. We also observed differences in the skin microbiomes of males vs. females with respect to the use of moisturizer. The composition of the skin microbiomes of female participants showed a higher difference in diversity in comparison to males. The current investigation also found that usage of sunscreen might help in retaining skin-protecting species in the skin microbiome. Conclusions: This work allowed us to understand the impact of moisturizer and sunscreen on skin health. The present evaluation shall pave the way for personalized skin care product development.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".