Pseudopterosin A-D Modulates Dendritic Cell Activation in Skin Sensitization
Bibliographic record
Abstract
This study investigates the anti-inflammatory effects of the marine diterpene glycosides pseudopterosin A-D (PsA-D) in mitigating nickel sulfate (NiSO4)-induced skin sensitization. In dermal dendritic cell (DDC) surrogates, PsA-D pre-treatment significantly reduced NiSO4-induced upregulation of key activation surface markers, cluster of differentiation (CD)54 (~1.2-fold), and CD86 (~1.6-fold). Additionally, PsA-D inhibited the NiSO4-induced activation of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) pathway by suppressing inhibitor of kappa B alpha (IκBα) degradation. Furthermore, PsA-D suppressed inflammatory responses by inhibiting the NiSO4-induced secretion of pro-inflammatory cytokines, including interleukin (IL)-8 (~6.8-fold), IL-6 (~2.2-fold), and IL-1β (~5.3-fold). In a full-thickness human skin model incorporating DDC surrogates, topical application of PsA-D effectively attenuated NiSO4-induced mRNA expression of IL-8 (~2.1-fold), IL-6 (~2.6-fold), and IL-1β (~2.2-fold), along with the key inflammatory mediators cyclooxygenase-2 (COX-2) (~3.5-fold) and NOD-like receptor family pyrin domain-containing 3 (NLRP3) (~2.1-fold). Overall, PsA-D demonstrated comparable efficacy to dexamethasone, a benchmark corticosteroid, providing a promising therapeutic alternative to corticosteroids for the treatment of skin sensitization and allergic contact dermatitis. However, to maximize PsA-D’s therapeutic potential, future studies on optimizing the bioavailability and formulation of PsA-D are required.
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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".