Evidence map›Paper›PMID 39157812›Full record

ArticleBiomaterials research2024

Natural Gastrointestinal Stable Pea Albumin Nanomicelles for Capsaicin Delivery and Their Effects for Enhanced Mucus Permeability at Small Intestine.

Yiming Li, Mengqi Mao, Xin Yuan, Jiajia Zhao, Lingjun Ma, Fang Chen, Xiaojun Liao, Xiaosong Hu, Junfu Ji

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomaterials research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Yiming LiCollege of Food Science and Nutritional Engineering, National Engineering Research Center for Fruit and Vegetable Processing, China Agricultural University, Key Lab of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing 100083, China.
Mengqi MaoCollege of Food Science and Nutritional Engineering, National Engineering Research Center for Fruit and Vegetable Processing, China Agricultural University, Key Lab of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing 100083, China.
Xin YuanCollege of Food Science and Nutritional Engineering, National Engineering Research Center for Fruit and Vegetable Processing, China Agricultural University, Key Lab of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing 100083, China.
Jiajia ZhaoCollege of Food Science and Nutritional Engineering, National Engineering Research Center for Fruit and Vegetable Processing, China Agricultural University, Key Lab of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing 100083, China.
Lingjun MaCollege of Food Science and Nutritional Engineering, National Engineering Research Center for Fruit and Vegetable Processing, China Agricultural University, Key Lab of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing 100083, China.
Fang ChenCollege of Food Science and Nutritional Engineering, National Engineering Research Center for Fruit and Vegetable Processing, China Agricultural University, Key Lab of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing 100083, China.
Xiaojun LiaoCollege of Food Science and Nutritional Engineering, National Engineering Research Center for Fruit and Vegetable Processing, China Agricultural University, Key Lab of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing 100083, China.
Xiaosong HuCollege of Food Science and Nutritional Engineering, National Engineering Research Center for Fruit and Vegetable Processing, China Agricultural University, Key Lab of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing 100083, China.
Junfu JiCollege of Food Science and Nutritional Engineering, National Engineering Research Center for Fruit and Vegetable Processing, China Agricultural University, Key Lab of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing 100083, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Natural nanodelivery systems are highly desirable owing to their biocompatibility and biodegradability. However, these delivery systems face challenges from potential degradation in the harsh gastrointestinal environment and limitations imposed by the intestinal mucus barrier, reducing their oral delivery efficacy. Here, gastrointestinal stable and mucus-permeable pea albumin nanomicelles (PANs) with a small particle size (36.42 nm) are successfully fabricated via pre-enzymatic hydrolysis of pea albumin isolate (PAI) using trypsin. Capsaicin (CAP) is used as a hydrophobic drug model and loaded in PAN with a loading capacity of 20.02 μg/mg. PAN exhibits superior intestinal stability, with a 40% higher CAP retention compared to PAI in simulated intestinal digestion. Moreover, PAN displays unrestricted movement in intestinal mucus and can effectively penetrate it, since it increases the mucus permeability of CAP by 2.5 times, indicating an excellent ability to overcome the mucus barrier. Additionally, PAN enhances the cellular uptake and transcellular transport of CAP with endoplasmic reticulum/Golgi and Golgi/plasma membrane pathways involved in the transcytosis and exocytosis. This study suggests that partially enzymatically formed PAN may be a promising oral drug delivery system, effectively overcoming the harsh gastrointestinal environment and mucus barrier to improve intestinal absorption and bioavailability of hydrophobic bioactive substances.

Identifiers

PMID39157812
PMCPMC11327615

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.