Evidence map›Paper›PMID 31387201›Full record

ArticleInternational journal of molecular sciences2019

Hepatic Cellular Distribution of Silica Nanoparticles by Surface Energy Modification.

A-Rang Lee, Kibeom Nam, Byeong Jun Lee, Seoung-Woo Lee, Su-Min Baek, Jun-Sun Bang, Seong-Kyoon Choi, Sang-Joon Park, Tae-Hwan Kim, Kyu-Shik Jeong and 2 more

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.7field-weighted citation impact, top 35% of its field
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

9 citing papers in PubMed, 16 citations in OpenAlex.

  1. Review
  2. Review
  3. Emerging nanomedicine for liver diseases treatment.Journal of nanobiotechnology · 2025
    Review
  4. Article
  5. Free radical detection in precision-cut mouse liver slices with diamond-based quantum sensing.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  6. Article
  7. Review
  8. Isolation and Characterization of Compounds fromInternational journal of molecular sciences · 2021
    Article
  9. Review
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

12 authors at 2 institutions in 1 country.

A-Rang LeeDepartment of Veterinary Pathology, College of Veterinary Medicine, Kyungpook National University, Daegu 41566, Korea.
Kibeom NamDepartment of Polymer Science and Engineering, Kyungpook National University, Daegu 41566, Korea.
Byeong Jun LeeDepartment of Polymer Science and Engineering, Kyungpook National University, Daegu 41566, Korea.
Seoung-Woo LeeDepartment of Veterinary Pathology, College of Veterinary Medicine, Kyungpook National University, Daegu 41566, Korea.
Su-Min BaekDepartment of Veterinary Pathology, College of Veterinary Medicine, Kyungpook National University, Daegu 41566, Korea.
Jun-Sun BangDepartment of Veterinary Pathology, College of Veterinary Medicine, Kyungpook National University, Daegu 41566, Korea.
Seong-Kyoon ChoiCore Protein Resources Center, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 42988, Korea.
Sang-Joon ParkLaboratory of Veterinary Histology, College of Veterinary Medicine, Kyungpook National University, Daegu 41566, Korea.
Tae-Hwan KimDepartment of Veterinary Pathology, College of Veterinary Medicine, Kyungpook National University, Daegu 41566, Korea.
Kyu-Shik JeongDepartment of Veterinary Pathology, College of Veterinary Medicine, Kyungpook National University, Daegu 41566, Korea.ORCID 0000-0003-1725-638X
Dong Yun LeeDepartment of Polymer Science and Engineering, Kyungpook National University, Daegu 41566, Korea. dongyunlee@knu.ac.kr.ORCID 0000-0003-4998-2116
Jin-Kyu ParkDepartment of Veterinary Pathology, College of Veterinary Medicine, Kyungpook National University, Daegu 41566, Korea. jinkyu820@knu.ac.kr.
Kyungpook National University · KRDaegu Gyeongbuk Institute of Science and Technology · KR

Funding

National Research Foundation of Korea 2017R1C1B5015716National Research Foundation of Korea 2019R1A2C1009874
6 · The paper itself

Abstract

The cellular distribution of silica nanoparticles (NPs) in the liver is not well understood. Targeting specific cells is one of the most important issues in NP-based drug delivery to improve delivery efficacy. In this context, the present study analyzed the relative cellular distribution pattern of silica NPs in the liver, and the effect of surface energy modification on NPs. Hydrophobic NP surface modification enhanced NP delivery to the liver and liver sinusoid fFendothelial cells (LSECs). Conversely, hydrophilic NP surface modification was commensurate with targeting hepatic stellate cells (HSCs) rather than other cell types. There was no notable difference in NP delivery to Kupffer cells or hepatocytes, regardless of hydrophilic or hydrophobic NP surface modification, suggesting that both the targeting of hepatocytes and evasion of phagocytosis by Kupffer cells are not associated with surface energy modification of silica NPs. This study provides useful information to target specific cell types using silica NPs, as well as to understand the relationship between NP surface energy and the NP distribution pattern in the liver, thereby helping to establish strategies for cell targeting using various NPs.

Indexed as

Drug CarriersDrug Delivery SystemsNanoparticlesSilicon DioxideEndothelial CellsHepatic Stellate CellsHepatocytesHydrophobic and Hydrophilic InteractionsKupffer CellsLiverSurface PropertiesTissue DistributionDrug CarriersSilicon DioxideNP-based drug deliverysilica nanoparticlessurface energy modification

Identifiers

PMID31387201
PMCPMC6696118
OpenAlexW2965410333

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.