Anti-Inflammatory and Antifungal Effects of Steroidal Compounds from Thymus vulgaris Against Malassezia-Induced Inflammation in Keratinocytes
Bibliographic record
Abstract
Malassezia-induced inflammation is common in chronic skin diseases, including seborrheic dermatitis.The current treatment methods primarily rely on antifungal and anti-inflammatory medications, which often have side effects associated with them.This study investigates the use of steroidal substances obtained from Thymus vulgaris (thyme) in terms of their joint anti-inflammatory and antimicrobial properties.The goal was to determine the effects of T. vulgaris steroids on the inflammatory gene expression and pathogen growth in keratinocytes stimulated by Malassezia, especially in relation to the NF-κB signaling pathway.HaCaT keratinocytes were cultured in vitro, and Malassezia was treated, followed by application of the aqueous extract of T. vulgaris at concentrations of 10-100 μM. mRNA expression of the inflammatory markers (IL 1b, TNF a, IL 6, and COX-2) was quantified by quantitative real-time PCR, whereas protein levels of NF-kB were shown by Western blot.The antibacterial activities of the compounds were determined through minimum inhibitory concentration (MIC) tests and live/dead staining.The compounds derived from T. vulgaris demonstrated significant suppression of inflammatory gene expression, and the mRNA levels measured were reduced by approximately 65% at concentrations above 50 µM (p < 0.05).The reduction in IκBα phosphorylation was a sign of the NF-κB pathway being blocked.The tests for antimicrobial activity revealed that the growth of M. globosa and M. restricta was totally stopped at the concentration of 100 µM, while the MIC values were 75 µM and 100 µM, in that order.The steroid compounds from T. vulgaris have shown to possess both antiinflammatory and antifungal activities, thus suggests the potential for developing novel therapeutic agents for skin problems caused by Malassezia.The mode of action of these natural products is basically through NF-κB pathway suppression accompanied by fungal growth inhibition, which is a more integrative and safer therapeutic way with less side effects compared to conventional treatments.
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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) | 0.000 | 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) | 0.001 | 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".