A review on lipid and polymeric nano-based 17-β-estradiol delivery systems: advances and challenges
Bibliographic record
Abstract
17β-estradiol (E2) is an endogenous steroid hormone pivotal for the development of female secondary sexual characteristics and the maintenance of the female reproductive system. Its roles extend beyond these physiological functions, as E2 is employed in hormone replacement therapy to alleviate symptoms associated with menopause. Furthermore, E2 exhibits therapeutic potential in the management of osteoporosis, breast cancer, and various neurological and cardiovascular conditions, partly due to its anti-inflammatory effects via modulation of the MAPK/NFκB signaling pathway. Notwithstanding, the hydrophobic nature of E2 significantly hinders the formulation of efficacious delivery systems for its clinical deployment. Recent advances have highlighted nano-based delivery systems for E2 as a promising solution to this solubility challenge. This review critically examines contemporary nano-delivery strategies for E2, particularly emphasizing lipid and polymeric nanoparticle-based systems. These nanostructures are designed to enhance stability, biocompatibility, controlled release, and targeted delivery of E2, yet the selectivity of E2 delivery for therapeutic purposes remains an ongoing challenge. The novelty of this review lies in its focus on the advances in nano-based E2 delivery systems over the past decade, a topic not extensively covered in prior literature. We present a comprehensive analysis of the encapsulation of E2 within polymeric and lipid nanoparticles, underscoring the untapped potential of these strategies. This review identifies a significant research gap, advocating for intensified experimental investigations that could pave the way for the translation of nano-based E2 therapies from bench to bedside.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".