Bone-protective Effects of Nonviral Gene Therapy With Folate–Chitosan DNA Nanoparticle Containing Interleukin-1 Receptor Antagonist Gene in Rats With Adjuvant-induced Arthritis
Bibliographic record
Abstract
Interleukin-1 receptor antagonist (IL-1Ra), is a natural blocker of the inflammatory cytokine interleukin-1. Using a rat adjuvant-induced arthritis (AIA) model of rheumatoid arthritis (RA), we examined the protective effects of IL-1Ra in bone metabolism in vivo after folate-mediated nonviral gene delivery. We detected secreted human IL-1Ra protein in serum and cultured primary osteoblasts of rats that were treated with chitosan-IL-1Ra and folate-IL-1Ra-chitosan nanoparticles, respectively. In vivo, IL-1Ra gene delivery significantly reverted alterations in bone turnover observed in arthritic animals by modulating the level of osteocalcin (OC) as well as the activities of alkaline phosphatase and tartrate-resistant acid phosphatase. The protective effects of these nanoparticles were evident from the decrease in the expression levels of interleukine-1beta and prostaglandin E(2) as well as osteoclast number and other histopathological findings. Compared to naked DNA and chitosan-DNA, folate-chitosan-DNA nanoparticles were less cytotoxic and enhanced IL-1Ra protein synthesis in vitro and offered a better protection against inflammation and abnormal bone metabolism in vivo. Nonviral gene therapy with folate-chitosan-DNA nanoparticles containing the IL-1 Ra gene seemed to protect against bone damage and inflammation in rat adjuvant-induced arthritis model.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".