Chrome Extension
WeChat Mini Program
Use on ChatGLM

Interaction Effects Between Macromolecules and Photosensitizer on the Ability of AlPc and InPc-loaded PHB Magnetic Nanoparticles in Photooxidatizing Simple Biomolecules

International Journal of Biological Macromolecules(2022)

Graduate Program in Biochemistry and Pharmacology | Univ Fed Espirito Santo | Fed Inst Espirito Santo

Cited 0|Views16
Abstract
The parameters used in the preparation of polymeric nanoparticles can influence its ability to photooxidate biomolecules. This work evaluated the effects of four parameter to prepare Poly(3-hydroxybutyrate) (PHB) nanoparticle loaded with aluminum and indium phthalocyanine (AlPc and InPc), together with iron oxide nanoparticles, assessing their influence on the size, the entrapment efficiency, and the nanoparticles recovery efficacy. The capability of free, and encapsulated, AlPc and InPc in photooxidating the bovine serum albumin (BSA) and tryptophan (Trp) was monitored by fluorescence. The AlPc-loaded nanoparticles had a larger size and a greater entrapment efficiency than that obtained by InPc-loaded nanoparticles. The free InPc was more efficient than the free AlPc to photooxidize the BSA and Trp; whereas the encapsulated AlPc was more efficient than encapsulated InPc to photooxidize the biomolecules. The higher hydrophobicity of the AlPc, combined with the greater aggregation state and the major interaction with the BSA, quenching the capacity of the free AlPc to photooxidate the biomolecules; whereas the greater interaction of the AlPc with PHB reduce the aggregation effect on the free molecules in the aqueous phase and increase the entrapment efficiency, resulting in an improving of the photodynamic efficiency and an increase of the photooxidation rate constant.
More
Translated text
Key words
Nanoparticle,Polyhydroxybutyrate,Phthalocyanine
求助PDF
上传PDF
Bibtex
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
Related Papers
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