Development of PEI- <i>RANK</i> siRNA Complex Loaded PLGA Nanocapsules for the Treatment of Osteoporosis
Bibliographic record
Abstract
Osteoporosis, which is characterized by low bone mineral density and susceptibility to fracture, is caused by increased osteoclastic activity. Receptor activator of nuclear factor kappa B ligand (RANKL)/RANK signaling plays an important role in osteoclast differentiation and activation. The current treatment strategies for osteoporosis do not directly address this underlying cause and generates undesired side effects. This led to emergence of controlled delivery systems to increase drug bioavailability and efficacy specifically at the bone tissue. With better understanding of molecular pathology of bone, the use of small interfering RNA (siRNA) to inhibit translation of abnormal gene expression in cells is becoming a promising approach. In this study, we report a siRNA delivery system consisting of PEI:RANK siRNA complex entrapped in nanosized poly(lactic acid-co-glycolic acid) (PLGA) capsules intended to be used in the treatment of osteoporosis. The nanosize will enable the nanoparticles to be administered by intravenous injection. The RANK siRNA was complexed with polyethylenimine (PEI) and loaded into biodegradable PLGA nanocapsules (NCs). The PEI:RANK siRNA loaded nanocapsules significantly reduced (47%) RANK mRNA levels. The differentiation of osteoclast precursors to mature osteoclasts was significantly suppressed (∼54%). The reduction in the osteoclastic activity of the differentiated osteoclasts (55%) was found to be statistically significant. The siRNA delivery system developed in the study is planned to be tested i.v. in mouse and has the potential to be used as a novel alternative approach for the systemic treatment of osteoporosis.
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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".