Evidence map›Paper›PMID 31775349›Full record

ReviewMicromachines2019

Advanced Polymers for Three-Dimensional (3D) Organ Bioprinting.

Xiaohong Wang

Abstract readReview
In one paragraph

Review in Micromachines, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Article
  6. Review
  7. Article
  8. Review
  9. Review
  10. Review
  11. 3D Printing Approaches to Engineer Cardiac Tissue.Current cardiology reports · 2023
    Review
  12. Article
  13. Review
  14. Bioprinting Technologies and Bioinks for Vascular Model Establishment.International journal of molecular sciences · 2023
    Review
  15. Review
  16. Review
  17. Review
  18. Review
  19. Article
  20. 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

1 author.

Xiaohong WangCenter of 3D Printing & Organ Manufacturing, School of Fundamental Sciences, China Medical University (CMU), No. 77 Puhe Road, Shenyang North New Area, Shenyang 110122, China.

Funding

the 2017 Discipline Promotion Project of China Medical University (CMU) 3110117049the 2018 Scientist Partners of China Medical University (CMU) and Shenyang Branch of Chinese Academy of Sciences (CAS) HZHB2018013the Key Research & Development Project of Liaoning Province 2018225082the National Natural Science Foundation of China (NSFC) 81571832 and 81271665
6 · The paper itself

Abstract

Three-dimensional (3D) organ bioprinting is an attractive scientific area with huge commercial profit, which could solve all the serious bottleneck problems for allograft transplantation, high-throughput drug screening, and pathological analysis. Integrating multiple heterogeneous adult cell types and/or stem cells along with other biomaterials (e.g., polymers, bioactive agents, or biomolecules) to make 3D constructs functional is one of the core issues for 3D bioprinting of bioartificial organs. Both natural and synthetic polymers play essential and ubiquitous roles for hierarchical vascular and neural network formation in 3D printed constructs based on their specific physical, chemical, biological, and physiological properties. In this article, several advanced polymers with excellent biocompatibility, biodegradability, 3D printability, and structural stability are reviewed. The challenges and perspectives of polymers for rapid manufacturing of complex organs, such as the liver, heart, kidney, lung, breast, and brain, are outlined.

Indexed as

biomaterialsorgan manufacturingpolymersstem cellsthree-dimensional (3D) printing

Identifiers

PMID31775349
PMCPMC6952999

What OpenQuestion holds

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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.