Construction of Multi‐Module RNA Nanoparticles Harboring miRNA, AIE, and CH6 Aptamer for Bone Targeting and Bone Anabolic Therapy
Bibliographic record
Abstract
Abstract Bone healing remains a major challenge in the treatment of osteoporosis. Effective strategies that simultaneously promote bone formation and inhibit bone resorption are crucial for the treatment of osteoporosis and associated bone defects. MicroRNA (miRNA)‐based approaches aim at simultaneously promoting bone formation and suppressing bone resorption and have therapeutic potential. However, the toxicity of cationic carriers and off‐target effects are two major challenges associated with miRNA delivery. This study establishes a bone‐targeting miRNA delivery system (CH6‐SNA‐26a) that integrates aggregation‐induced emission (AIE), a CH6 aptamer, and miRNAs into a single nanoplatform without any cationic carrier. In this system, an AIE molecule is coupled to miR‐26a to form a core–shell spherical nucleic acid (SNA‐26a). The CH6 aptamer is co‐assembled with the SNA to achieve specific miR‐26a delivery to the bone surface. This aptamer‐functionalized, non‐cationic miR‐26a delivery strategy (CH6‐SNA‐26a) enables both bone‐targeted delivery and high transfection efficiency, ultimately optimizing bone remodeling and calvarial bone healing in an osteoporosis mouse model while limiting adverse effects in non‐skeletal tissues. Mechanistically, the overexpression of CH6‐SNA‐26a in skeletal tissues promotes bone anabolic action by functionally targeting glycogen synthase kinase 3 beta in bone marrow mesenchymal stem cells and cellular communication network 2 in osteoclasts.
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".