Biomaterial-based combinatorial approach of aescin-comprised zein-coated gelatin nanoparticles alleviates synovial inflammation in experimental inflammatory arthritis
Bibliographic record
Abstract
Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease that mostly affects joints. Although RA therapy has made significant progress, difficulties including extensive medication metabolism and its quick clearance result in its inadequate bioavailability. The anti-inflammatory effect of zein was reported with other medications, but it has certain limitations. There are reports on the anti-oxidant and anti-inflammatory effect of aescin, which exhibits low bioavailability for the treatment of rheumatoid arthritis. Also, the combinatorial effect of zein with other effective drug delivery systems is still under investigation for the treatment of experimental collagen-induced rheumatoid arthritis. The focus of this study was to formulate and define the characteristics of zein-coated gelatin nanoparticles encapsulated with aescin (Ze@Aes-GNPs) and to assess and contrast the therapeutic effectiveness of Ze@Aes-GNPs towards collagen-induced RA in Wistar rats. Nanoprecipitation and the layer-by-layer coating process were used to fabricate Ze@Aes-GNPs and their hydrodynamic diameter was determined to be 182 nm. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were used to further validate the size, shape, and surface morphology of Ze@Aes-GNPs. When tested against foreskin fibroblasts (BJ), these nanoparticles demonstrated significantly high cytocompatibility. Both Aes and Ze@Aes-GNPs were effective in treating arthritis, as shown by the decreased edoema, erythema, and swelling of the joints, between which Ze@Aes-GNPs were more effective. Further, it was demonstrated that Aes and Ze@Aes-GNPs reduced the levels of oxidative stress (articular elastase, lipid peroxidation, catalase, superoxide dismutase and nitric oxide) and inflammatory indicators (TNF-α, IL-1β and myeloperoxidase). The histopathology findings further demonstrated that Ze@Aes-GNPs considerably reduced the infiltration of inflammatory cells at the ankle joint cartilage compared to Aes. Additionally, immunohistochemistry examination showed that treatment with Ze@Aes-GNPs suppressed the expression of pro-inflammatory markers (COX-2 and IL-6) while increasing the expression of SOD1. In summary, the experiments indicated that Aes and Ze@Aes-GNPs lowered the severity of arthritis, and critically, Ze@Aes-GNPs showed better effectiveness in comparison to Aes. This suppression of oxidative stress and inflammation was likely driven by Aes and Ze@Aes-GNPs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".