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Record W4297957100

A review of fibrin and fibrin composites for bone tissue engineering

2017· review· en· W4297957100 on OpenAlexaboutno aff
Abeer Adnan Noori Adnan Noori, Ashrafi SJ, R Vaez-Ghaemi, A Hatamian-Zaremi, Webster TJ

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2017
Typereview
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsnot available
Fundersnot available
KeywordsFibrinComposite materialMaterials scienceBiomedical engineeringMedicine
DOInot available

Abstract

fetched live from OpenAlex

Alireza Noori,1 Seyed Jamal Ashrafi,2 Roza Vaez-Ghaemi,3 Ashraf Hatamian-Zaremi,4 Thomas J Webster5 1Department of Tissue Engineering and Applied Cell Sciences, Faculty of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, 2School of Medicine, Shahroud University of Medical Sciences, Shahroud, Iran; 3Department of Chemical and Biological Engineering, Faculty of Biomedical Engineering, The University of British Columbia, Vancouver, BC, Canada; 4Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran; 5Department of Chemical Engineering, Northeastern University, Boston, MA, USA Abstract: Tissue engineering has emerged as a new treatment approach for bone repair and regeneration seeking to address limitations associated with current therapies, such as autologous bone grafting. While many bone tissue engineering approaches have traditionally focused on synthetic materials (such as polymers or hydrogels), there has been a lot of excitement surrounding the use of natural materials due to their biologically inspired properties. Fibrin is a natural scaffold formed following tissue injury that initiates hemostasis and provides the initial matrix useful for cell adhesion, migration, proliferation, and differentiation. Fibrin has captured the interest of bone tissue engineers due to its excellent biocompatibility, controllable biodegradability, and ability to deliver cells and biomolecules. Fibrin is particularly appealing because its precursors, fibrinogen, and thrombin, which can be derived from the patient’s own blood, enable the fabrication of completely autologous scaffolds. In this article, we highlight the unique properties of fibrin as a scaffolding material to treat bone defects. Moreover, we emphasize its role in bone tissue engineering nanocomposites where approaches further emulate the natural nanostructured features of bone when using fibrin and other nanomaterials. We also review the preparation methods of fibrin glue and then discuss a wide range of fibrin applications in bone tissue engineering. These include the delivery of cells and/or biomolecules to a defect site, distributing cells, and/or growth factors throughout other pre-formed scaffolds and enhancing the physical as well as biological properties of other biomaterials. Thoughts on the future direction of fibrin research for bone tissue engineering are also presented. In the future, the development of fibrin precursors as recombinant proteins will solve problems associated with using multiple or single-donor fibrin glue, and the combination of nanomaterials that allow for the incorporation of biomolecules with fibrin will significantly improve the efficacy of fibrin for numerous bone tissue engineering applications. Keywords: fibrin, fibrinogen, injectable hydrogel, fibrin preparation, fibrin beads, fibrin coating, nanofibrous scaffold, bone repair

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.311
GPT teacher head0.600
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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