Folate-mediated Transport of Nanoparticles Across the Placenta.
Pharmaceutical nanotechnology(2024)
Department of Obstetrics and Gynecology | Institute of Molecular Medicine
Abstract
BACKGROUND:In this study, a prototype of a targeted nanocarrier for drug delivery for prenatal therapy of the developing fetus was developed and examined in vitro and ex vivo. The folate transport mechanism in the human placenta was utilized as a possible pathway for the transplacental delivery of targeted nanoparticles.METHODS:Several types of folic acid-decorated polymeric nanoparticles were synthesized and characterized. During transport studies of targeted and non-targeted fluorescent nanoparticles across the placental barrier, the apparent permeability values, uptake, transfer indices, and distribution in placental tissue were determined.RESULTS:The nanoparticles had no effect on BeWo b30 cell viability. In vitro, studies showed significantly higher apparent permeability of the targeted nanoparticles across the cell monolayers as compared to the nontargeted nanoparticles (Pe = 5.92 ± 1.44 ×10-6 cm/s for PLGA-PEG-FA vs. 1.26 ± 0.31 ×10-6 cm/s for PLGA-PEG, P < 0.05), and the transport of the targeted nanoparticles was significantly inhibited by excess folate. Ex vivo placental perfusion showed significantly greater accumulation of the targeted nanoparticles in the placental tissue (4.31 ± 0.91%/g for PLGA-PEG-FA vs. 2.07 ± 0.26%/g for PLGA-PEG).CONCLUSION:The data obtained suggested different mechanisms for the uptake and transplacental transfer of targeted versus nontargeted nanoparticles. This targeted nanoformulation may be a promising strategy for fetal drug therapy.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest