17β-Estradiol-Loaded Exosomes for Targeted Drug Delivery in Osteoporosis: A Comparative Study of Two Loading Methods
Bibliographic record
Abstract
Purpose: Exosomes are natural nanoparticles that participate in intercellular communication through molecular transport. Recently, due to their membrane vesicular structure and surface proteins, exosomes have been used extensively in the research field of drug delivery. Osteoporosis is an inflammation in which the cellular balance of bone tissue is disturbed that reduces bone density and making bone prone to abnormal fractures with small amount of force. Utilizing estrogen is one of the main therapeutic strategies for osteoporosis. Despite the positive effects of estrogen on bone tissue, changes in the natural estrogen levels of the body can cause a number of diseases such as different types of cancer. Therefore, designing a therapeutic system which controls more accurate tissue targeting of estrogen seems to be a rational and promising practical approach. Methods: In this study, bone marrow mesenchymal stem cells (BMMSCs)-derived exosomes were loaded by estradiol using two different methods of drug loading, namely incubation and sonication methods and then the survival effects of the drug loaded exosomes on BMMSCs was investigated. Results: Examination of size, shape, and surface factors of exosomes in different states (pure exosomes and drug-loaded exosomes) showed that the round morphology of exosomes was preserved in all conditions. However, the particles size increased significantly when loaded by sonication method. The increased survival of BMMSCs was noted with estradiol-loaded exosomes when compared to the control group. Conclusion: The results suggest that estradiol-loaded exosomes have potential to be used as nano-drug carriers in the treatment of osteoporosis.
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 | 0.001 |
| 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 | 0.001 |
| 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".