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Artificial Intelligence-Driven Analysis of Antimicrobial-Resistant and Biofilm-Forming Pathogens on Biotic and Abiotic Surfaces.
Mishra, Akanksha; Tabassum, Nazia; Aggarwal, Ashish; Kim, Young-Mog; Khan, Fazlurrahman.
Afiliação
  • Mishra A; School of Bioengineering and Biosciences, Lovely Professional University, Phagwara 144001, Punjab, India.
  • Tabassum N; Marine Integrated Biomedical Technology Center, The National Key Research Institutes in Universities, Pukyong National University, Busan 48513, Republic of Korea.
  • Aggarwal A; Research Center for Marine Integrated Bionics Technology, Pukyong National University, Busan 48513, Republic of Korea.
  • Kim YM; School of Bioengineering and Biosciences, Lovely Professional University, Phagwara 144001, Punjab, India.
  • Khan F; Marine Integrated Biomedical Technology Center, The National Key Research Institutes in Universities, Pukyong National University, Busan 48513, Republic of Korea.
Antibiotics (Basel) ; 13(8)2024 Aug 22.
Article em En | MEDLINE | ID: mdl-39200087
ABSTRACT
The growing threat of antimicrobial-resistant (AMR) pathogens to human health worldwide emphasizes the need for more effective infection control strategies. Bacterial and fungal biofilms pose a major challenge in treating AMR pathogen infections. Biofilms are formed by pathogenic microbes encased in extracellular polymeric substances to confer protection from antimicrobials and the host immune system. Biofilms also promote the growth of antibiotic-resistant mutants and latent persister cells and thus complicate therapeutic approaches. Biofilms are ubiquitous and cause serious health risks due to their ability to colonize various surfaces, including human tissues, medical devices, and food-processing equipment. Detection and characterization of biofilms are crucial for prompt intervention and infection control. To this end, traditional approaches are often effective, yet they fail to identify the microbial species inside biofilms. Recent advances in artificial intelligence (AI) have provided new avenues to improve biofilm identification. Machine-learning algorithms and image-processing techniques have shown promise for the accurate and efficient detection of biofilm-forming microorganisms on biotic and abiotic surfaces. These advancements have the potential to transform biofilm research and clinical practice by allowing faster diagnosis and more tailored therapy. This comprehensive review focuses on the application of AI techniques for the identification of biofilm-forming pathogens in various industries, including healthcare, food safety, and agriculture. The review discusses the existing approaches, challenges, and potential applications of AI in biofilm research, with a particular focus on the role of AI in improving diagnostic capacities and guiding preventative actions. The synthesis of the current knowledge and future directions, as described in this review, will guide future research and development efforts in combating biofilm-associated infections.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Antibiotics (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Índia País de publicação: Suíça

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Antibiotics (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Índia País de publicação: Suíça