Tanshinone I Ameliorates Psoriasis-Like Dermatitis by Suppressing Inflammation and Regulating Keratinocyte Differentiation
Bibliographic record
Abstract
Background: Psoriasis is an immune-related inflammatory systemic condition characterized by dysregulated keratinocyte proliferation and chronic inflammation. Tanshinone I (Tan-I) has recently been discovered to have immunomodulatory properties, but its role and mechanisms in treating psoriasis remain unclear. Objective: To evaluate the efficacy of Tan-I in the treatment of psoriasis and to determine the mechanisms involved. Methods: An imiquimod (IMQ)-induced psoriasis-like mouse model was treated topically with Tan-I (7.5 mg/kg/d) or a vehicle. Disease severity was evaluated using the Psoriasis Area and Severity Index (PASI), and histological changes were assessed via H&E staining and Ki67 immunofluorescence. TNF-α-stimulated HaCaT keratinocytes were used for in vitro analyses, including apoptosis, cell cycle progression, and inflammatory gene expression via RT-qPCR. RNA sequencing (RNA-seq) was performed to investigate Tan-I's mechanisms in vivo and in vitro, while keratin expression was analyzed by immunofluorescence and Western blot. Results: Tan-I treatment significantly alleviated psoriasis-like lesions in the IMQ mouse model, improving skin pathology and reducing Ki67-positive cells. RNA-seq revealed that Tan-I modulated immune pathways, keratinocyte differentiation, and barrier function. In TNF-α-stimulated HaCaT cells, Tan-I induced G1-phase cell cycle arrest, reduced apoptosis, and suppressed inflammatory gene expression. RNA-seq further showed that Tan-I normalized cell cycle signaling and apoptosis pathways disrupted by TNF-α. Additionally, Tan-I restored keratin expression patterns, increasing K1 and decreasing K6 and K17 levels in both mouse skin and HaCaT cells. Conclusion: Tan-I is a promising therapeutic candidate for psoriasis, effectively mitigating inflammation, normalizing keratinocyte differentiation, and inhibiting abnormal keratinocyte apoptosis.
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 | 0.001 |
| 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".