{"doi":"10.1021/acsami.1c11787","title":"Personalized and Defect-Specific Antibiotic-Laden Scaffolds for Periodontal Infection Ablation","abstract":"Periodontitis compromises the integrity and function of tooth-supporting structures. Although therapeutic approaches have been offered, predictable regeneration of periodontal tissues remains intangible, particularly in anatomically complex defects. In this work, personalized and defect-specific antibiotic-laden polymeric scaffolds containing metronidazole (MET), tetracycline (TCH), or their combination (MET/TCH) were created via electrospinning. An initial screening of the synthesized fibers comprising chemo-morphological analyses, cytocompatibility assessment, and antimicrobial validation against periodontopathogens was accomplished to determine the cell-friendly and anti-infective nature of the scaffolds. According to the cytocompatibility and antimicrobial data, the 1:3 MET/TCH formulation was used to obtain three-dimensional defect-specific scaffolds to treat periodontally compromised three-wall osseous defects in rats. Inflammatory cell response and new bone formation were assessed by histology. Micro-computerized tomography was performed to assess bone loss in the furcation area at 2 and 6 weeks post implantation. Chemo-morphological and cell compatibility analyses confirmed the synthesis of cytocompatible antibiotic-laden fibers with antimicrobial action. Importantly, the 1:3 MET/TCH defect-specific scaffolds led to increased new bone formation, lower bone loss, and reduced inflammatory response when compared to antibiotic-free scaffolds. Altogether, our results suggest that the fabrication of defect-specific antibiotic-laden scaffolds holds great potential toward the development of personalized (i.e., patient-specific medication) scaffolds to ablate infection while affording regenerative properties.","journal":"ACS Applied Materials & Interfaces","year":2021,"id":167543,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":32,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9521,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":695058,"name":"Karla Zanini Kantorski","orcid":"0000-0002-6219-0255","position":1,"is_corresponding":false},{"id":260951,"name":"Nileshkumar Dubey","orcid":null,"position":2,"is_corresponding":false},{"id":260952,"name":"Arwa Daghrery","orcid":null,"position":3,"is_corresponding":false},{"id":411538,"name":"J. Christopher Fenno","orcid":"0000-0002-7073-7855","position":4,"is_corresponding":false},{"id":75986,"name":"Yuji Mishina","orcid":"0000-0002-6268-4204","position":5,"is_corresponding":false},{"id":644790,"name":"Hsun‐Liang Chan","orcid":"0000-0001-5952-0447","position":6,"is_corresponding":false},{"id":695059,"name":"Gustavo Mendonça","orcid":"0000-0003-2290-4046","position":7,"is_corresponding":false},{"id":258010,"name":"Marco C. Bottino","orcid":"0000-0001-8740-2464","position":8,"is_corresponding":false},{"id":258006,"name":"Jéssica A. Ferreira","orcid":"0000-0002-9669-4339","position":0,"is_corresponding":true}],"reference_count":82,"raw_metadata":null,"created_at":"2026-07-18T23:45:58.359801Z","pmid":"34637255","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}