Preclinical Testing of Dendritic Core–Multishell Nanoparticles in Inflammatory Skin Equivalents
Bibliographic record
Abstract
Human skin equivalents emerged as novel tools in preclinical dermatological research. It is being claimed that they may bridge the translational gap between preclinical and clinical research, yet only a few studies have investigated their suitability for preclinical drug testing so far. Therefore, we investigated if inflammatory skin equivalents, which emulate hallmarks of atopic dermatitis (AD), are suitable to assess the anti-inflammatory effects of dexamethasone (DXM) in a cream formulation or loaded onto dendritic core-multishell nanoparticles. Topical DXM application resulted in significantly decreased expression of the proinflammatory cytokine TSLP, increased expression of the skin barrier protein involucrin, and facilitated glucocorticoid receptor translocation in a dose-dependent manner. Further, DXM treatment inhibited gene expression of extracellular matrix components, potentially indicative of the known skin atrophy-inducing side effects of glucocorticoids. Overall, we were able to successfully assess the anti-inflammatory effects of DXM and the superiority of the nanoparticle formulation. Nevertheless the identification of robust readout parameters proved challenging and requires careful study design.
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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 | 0.001 | 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) | 0.001 | 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".