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Regenerative endodontic procedures (REP) represent an innovative approach aimed at replacing altered pulp tissue with healthy tissue to restore the normal functionality of the pulp-dentin complex. The efficacy of REP may be influenced by various factors, including stem cells, biomaterial scaffolds, and bioactive signaling molecules. Scaffolds for pulp regeneration must possess several essential characteristics, including ease of manipulation, bioactivity, biodegradability, adequate porosity, sufficient physical and mechanical strength, minimal immunogenicity, and the ability to promote vascularization. This review presents data-based research to comprehensively address the study question: “what types of scaffolds are applicable for the regeneration of the dentin-pulp complex?”. An electronic search of English literature referenced in databases such as the Cochrane Library, PubMed, and EMBASE prior to 2024 was conducted. The search terms “pulp-dentin regeneration AND scaffold” and “pulp-dentin complex AND regeneration” were utilized to locate research papers about tissue engineering methods. Furthermore, reference mining of the selected publications was conducted to identify further studies. Particular scaffolds for pulp-dentin complex regeneration that have been documented in in vitro and in vivo animal investigations were chosen and examined. This review concludes that each scaffold possesses distinct advantages and disadvantages. To determine the optimal treatment plan and materials, further investigations, particularly additional clinical trials, should be conducted to identify the appropriate regenerative endodontic treatment procedures and materials.
Comments:
The idea is good, and the methodology is scientifically sound, with electronic search of English literature referenced in databases such as the Cochrane Library, PubMed, and EMBASE prior to 2024. The search terms “pulp-dentin regeneration AND scaffold” and “pulp-dentin complex AND regeneration” may be too narrow, and using of “OR” maybe more appropriate to capture more publications. However, reference mining of the selected publications was conducted to identify further studies, which added values to this systematic review of the topic.
Unluckily, the full text of the article is not available, and the paper seems to have been withdrawn by the authors themselves.
The author declares that they have no competing interests.
The author declares that they did not use generative AI to come up with new ideas for their review.
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