In vivo therapeutic efficacy of TNFα silencing by folate-PEG-chitosan-DEAE/siRNA nanoparticles in arthritic mice
Bibliographic record
Abstract
Background: Tumor necrosis factor-alpha (TNFα), a pro-inflammatory cytokine, has been shown to play a role in the pathophysiology of rheumatoid arthritis. Silencing TNFα expression with small interfering RNA (siRNA) is a promising approach to treatment of the condition. Methods: Towards this end, our team has developed a modified chitosan (CH) nanocarrier, deploying folic acid, diethylethylamine (DEAE) and polyethylene glycol (PEG) (folate-PEG-CH-DEAE 15 ). The gene carrier protects siRNA against nuclease destruction, its ligands facilitate siRNA uptake via cell surface receptors, and it provides improved solubility at neutral pH with transport of its load into target cells. In the present study, nanoparticles were prepared with siRNA-TNFα, DEAE, and folic acid-CH derivative. Nanoparticle size and zeta potential were verified by dynamic light scattering. Their TNFα-knockdown effects were tested in a murine collagen antibody-induced arthritis model. TNFα expression was examined along with measurements of various cartilage and bone turnover markers by performing histology and microcomputed tomography analysis. Results: We demonstrated that folate-PEG-CH-DEAE 15 /siRNA nanoparticles did not alter cell viability, and significantly decreased inflammation, as demonstrated by improved clinical scores and lower TNFα protein concentrations in target tissues. This siRNA nanocarrier also decreased articular cartilage destruction and bone loss. Conclusion: The results indicate that folate-PEG-CH-DEAE 15 nanoparticles are a safe and effective platform for nonviral gene delivery of siRNA, and their potential clinical applications warrant further investigation. Keywords: arthritis, inflammation, siRNA, TNFα, nanoparticles, chitosan
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".