Using network analysis and large-language models to obtain a landscape of the literature on dressing materials for wound healing: The predominance of chitosan and other biomacromolecules: A review

Autor(es)

Klarak, Jaromir ; Brito, Ana Caroline M. ; Moreira, Luan F. ; Silva, Filipi N. ; Amancio, Diego R. ; Andok, Robert ; Oliveira, Maria Cristina F. ; Bardosova, Maria ; Oliveira Jr, Osvaldo N.

• Tipo de documento: Artigo Científico
• Fonte: International Journal of Biological Macromolecules; v. 306, p. 13-pg., 2025-03-05.

Resumo

We present an overview of the literature on dressing materials for wound healing, combining network analysis and natural language processing using large language models. Contributions to this field come from a variety of research areas and journals, so we employed multiple strategies for searching the OpenAlex database to ensure that the most relevant papers were covered, while also focusing on the specific topic of interest. Citation networks were created from the retrieved papers, identifying clusters that represent major topics. Starting with broad searches on 'wound' and 'wound healing' we refined the focus to dressing materials by incorporating expert knowledge into the analysis. This approach also allowed for a comparison with fully automated analyses. The resulting landscape shows significant growth in this area in recent years, with most contributions coming from the Northern Hemisphere, particularly China and the USA. The most commonly used materials include gauze, hydrocolloids, chitosan-based hydrogels, foams, alginates, hydrofibers (e.g., those containing nanomaterials such as silver nanoparticles), composites, biomaterials, and skin substitutes. Research primarily focuses on the antibacterial properties of these materials and their application in treating burn-related wounds, which, along with diabetes, are common causes of chronic wounds. (AU)

• Processo FAPESP: 18/22214-6 - Rumo à convergência de tecnologias: de sensores e biossensores à visualização de informação e aprendizado de máquina para análise de dados em diagnóstico clínico
• Beneficiário: Osvaldo Novais de Oliveira Junior
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 20/14817-2 - Usando redes complexas e processamento de línguas naturais para caracterização e previsão de sucesso na Ciência
• Beneficiário: Ana Caroline Medeiros Brito
• Modalidade de apoio: Bolsas no Brasil - Doutorado