Evidence map›Paper›PMID 38667541›Full record

ReviewJournal of functional biomaterials2024

Biomaterials for Regenerative Cranioplasty: Current State of Clinical Application and Future Challenges.

Lizhe He

Open access · goldAbstract readReview
In one paragraph

Review in Journal of functional biomaterials, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.0field-weighted citation impact, top 9% 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

10 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Review
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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 at 1 institution in 1 country.

Lizhe HeKey Laboratory of 3D Printing Process and Equipment of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, Hangzhou 310028, China.ORCID 0000-0002-6310-868X
Zhejiang University · CN

Funding

Ningbo Science and Technology Bureau 2022Z143
6 · The paper itself

Abstract

Acquired cranial defects are a prevalent condition in neurosurgery and call for cranioplasty, where the missing or defective cranium is replaced by an implant. Nevertheless, the biomaterials in current clinical applications are hardly exempt from long-term safety and comfort concerns. An appealing solution is regenerative cranioplasty, where biomaterials with/without cells and bioactive molecules are applied to induce the regeneration of the cranium and ultimately repair the cranial defects. This review examines the current state of research, development, and translational application of regenerative cranioplasty biomaterials and discusses the efforts required in future research. The first section briefly introduced the regenerative capacity of the cranium, including the spontaneous bone regeneration bioactivities and the presence of pluripotent skeletal stem cells in the cranial suture. Then, three major types of biomaterials for regenerative cranioplasty, namely the calcium phosphate/titanium (CaP/Ti) composites, mineralised collagen, and 3D-printed polycaprolactone (PCL) composites, are reviewed for their composition, material properties, and findings from clinical trials. The third part discusses perspectives on future research and development of regenerative cranioplasty biomaterials, with a considerable portion based on issues identified in clinical trials. This review aims to facilitate the development of biomaterials that ultimately contribute to a safer and more effective healing of cranial defects.

Indexed as

biomaterialsclinical trialscranial defectscranioplastyregenerative medicine

Identifiers

PMID38667541
PMCPMC11050949
OpenAlexW4393253868

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.