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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".