5‐α reductase inhibition by <i>Epilobioum fleischeri</i> extract modulates facial microbiota structure
Bibliographic record
Abstract
BACKGROUND: Facial skin is a particularly complex environment made of different skin types such as sebaceous (forehead) and dry (cheeks). The skin microbiota composition on different facial sites has not yet been addressed. METHODS: We conducted a 4-week-long, single-centre, randomized and placebo-controlled clinical study involving 23 Caucasian females. We assessed both bacterial composition on five different facial areas and the microbiome modulatory effects resulting from the topical application of a plant extract (Epilobium fleischeri). Skin microbiome samples were collected before and after 4 weeks of product application. Microbiota profiling was performed via 16S rRNA gene sequencing, and relative abundance data were used to calculate differentials via a multinomial regression model. RESULTS: Via 'reference frames', we observed shifts in microbial composition after 4 weeks of twice-daily product application and identify certain microbiota species, which were positively associated with the application of the product containing the Epilobium fleischeri extract. Staphylococcus hominis, Staphylococcus epidermidis, and Micrococcus yunnanensis appeared to be significantly enriched in the final microbiota composition of the active treatment group. CONCLUSION: Facial skin was found to be colonized by an heterogenous microbiota, and the Epilobium fleischeri extract had a modulatory effect on commensal bacteria on the different facial sites.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".