Evidence map›Paper›PMID 34578701›Full record

ReviewNanomaterials (Basel, Switzerland)2021

Analysis of Nanotoxicity with Integrated Omics and Mechanobiology.

Tae Hwan Shin, Saraswathy Nithiyanandam, Da Yeon Lee, Do Hyeon Kwon, Ji Su Hwang, Seok Gi Kim, Yong Eun Jang, Shaherin Basith, Sungsu Park, Jung-Soon Mo and 1 more

Abstract readReview
In one paragraph

Review in Nanomaterials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. Article
  8. Multi-omics reveals the mechanisms underlyingAnimal nutrition (Zhongguo xu mu shou yi xue hui) · 2023
    Article
  9. Review
  10. Review
  11. Review
  12. Article
  13. Article
  14. Review
  15. Review
  16. Review
  17. Diesel-derived PMFrontiers in endocrinology · 2022
    Article
  18. 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

11 authors.

Tae Hwan ShinDepartment of Physiology, Ajou University School of Medicine, Suwon 16499, Korea.ORCID 0000-0001-9686-4556
Saraswathy NithiyanandamDepartment of Molecular Science and Technology, Ajou University, Suwon 16499, Korea.
Da Yeon LeeDepartment of Physiology, Ajou University School of Medicine, Suwon 16499, Korea.
Do Hyeon KwonDepartment of Molecular Science and Technology, Ajou University, Suwon 16499, Korea.
Ji Su HwangDepartment of Molecular Science and Technology, Ajou University, Suwon 16499, Korea.
Seok Gi KimDepartment of Molecular Science and Technology, Ajou University, Suwon 16499, Korea.
Yong Eun JangDepartment of Biological Sciences, Ajou University, Suwon 16499, Korea.
Shaherin BasithDepartment of Physiology, Ajou University School of Medicine, Suwon 16499, Korea.
Sungsu ParkSchool of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Korea.ORCID 0000-0003-3062-1302
Jung-Soon MoInstitute for Medical Sciences, Ajou University School of Medicine, Suwon 16499, Korea.
Gwang LeeDepartment of Physiology, Ajou University School of Medicine, Suwon 16499, Korea.ORCID 0000-0002-1299-9478

Funding

National Research Foundation of Korea 2020M3E5D9080661National Research Foundation of Korea 2020R1A4A4079722National Research Foundation of Korea 2020R1C1C1008366National Research Foundation of Korea NRF-2020R1A2C2011016
6 · The paper itself

Abstract

Nanoparticles (NPs) in biomedical applications have benefits owing to their small size. However, their intricate and sensitive nature makes an evaluation of the adverse effects of NPs on health necessary and challenging. Since there are limitations to conventional toxicological methods and omics analyses provide a more comprehensive molecular profiling of multifactorial biological systems, omics approaches are necessary to evaluate nanotoxicity. Compared to a single omics layer, integrated omics across multiple omics layers provides more sensitive and comprehensive details on NP-induced toxicity based on network integration analysis. As multi-omics data are heterogeneous and massive, computational methods such as machine learning (ML) have been applied for investigating correlation among each omics. This integration of omics and ML approaches will be helpful for analyzing nanotoxicity. To that end, mechanobiology has been applied for evaluating the biophysical changes in NPs by measuring the traction force and rigidity sensing in NP-treated cells using a sub-elastomeric pillar. Therefore, integrated omics approaches are suitable for elucidating mechanobiological effects exerted by NPs. These technologies will be valuable for expanding the safety evaluations of NPs. Here, we review the integration of omics, ML, and mechanobiology for evaluating nanotoxicity.

Indexed as

integrated omicsmachine learningmechanobiologynanoparticlenanotoxicity

Identifiers

PMID34578701
PMCPMC8470953

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.