Construction of a PLGA based, targeted siRNA delivery system for treatment of osteoporosis
Bibliographic record
Abstract
Osteoporosis, a systemic skeletal disorder, occurs when bone turnover balance is disrupted. With the identification of the genes involved in the pathogenesis of the disease, studies on development of new treatments has intensified. Short interfering RNA (siRNA) is used to knockdown disease related gene expressions. Targeting siRNA in vivo is challenging. The maintenance of therapeutic plasma level is hampered by clearance of siRNA from the body. Targeted systems are useful in increasing the drug concentration at the target site and decreasing side effects. Aim of the present study was to develop an injectable siRNA delivery system to protect siRNA during systemic distribution and target the siRNA to bone tissue using a thermoresponsive, genetically engineered, elastin-like recombinamer (ELR), designed to interact with the mineral component of bone. The delivery system consisted of DNAoligo as a siRNA substitute complexed with the cationic polymer, polyethyleneimine (PEI), at N/P ratio of 20. The complex was encapsulated in poly(lactic acid-co-glycolic acid) (PLGA) nanocapsules. PLGA capsules were characterized by SEM, TEM and XPS. FTIR was used to show the preferential attachment of ELR to HAp. Encapsulation efficiency of the complex in PLGA nanocapsules was 48%. The release kinetics of the complex fits the Higuchi release kinetics.
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) | 0.000 | 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".