Advancing understanding of microbial biofilms through machine learning-powered studies

Biofilm-mediated infections; novel therapeutic approaches and harnessing artificial intelligence for early detection and treatment of biofilm-associated infections

A biofilm is a group of bacteria that have self-produced a matrix and are grouped together in a dense population. By resisting the host's immune system's phagocytosis process and attacking with anti-microbial chemicals such as reactive oxygen and nitrogen species, a biofilm enables pathogenic bacteria to evade elimination. One of the major problems in managing chronic injuries is treating wounds colonized by biofilms. These days, a major issue is the biofilms, which exacerbate infection pathogenesis and severity. Numerous investigators have already discovered cutting-edge methods for biofilm manipulation. Using phytochemicals is a practical tactic to control and prevent the production of biofilms. Numerous studies conducted in the last few years have demonstrated the antibacterial and antibiofilm qualities of nanoparticles (NPs) against bacteria, fungi, and protozoa. Because hydrogel has antibiofilm properties, it has been employed extensively in wound care recently. It may be removed with ease and without causing trauma. Today, artificial intelligence (AI) is being used to improve these tactics by providing customized treatment alternatives and predictive analytics. Artificial intelligence (AI) algorithms have the capability to examine extensive datasets and detect trends in biofilm formation and resistance mechanisms. This can aid in the creation of more potent antimicrobial drugs. AI models analyze complex datasets, predict biofilm formation, and guide the design of personalized treatment strategies by identifying resistance mechanisms and therapeutic targets with exceptional precision. This review provides an integrative perspective on biofilm formation mechanisms and their role in infections, highlighting the innovative applications of AI in this domain. By integrating data from diverse biological systems, AI accelerates drug discovery, optimizes treatment regimens, and enables real-time monitoring of biofilm dynamics. From predictive analytics to personalized care, we explore how AI enhances biofilm diagnostics and introduces precision medicine in biofilm-associated infections. This approach not only addresses the limitations of traditional methods but also paves the way for revolutionary advancements in infection control, antimicrobial resistance management, and improved patient outcomes.

Artificial Intelligence-Driven Analysis of Antimicrobial-Resistant and Biofilm-Forming Pathogens on Biotic and Abiotic Surfaces

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.

Lactobacillus-derived components for inhibiting biofilm formation in the food industry

Biofilm, a microbial community formed by especially pathogenic and spoilage bacterial species, is a critical problem in the food industries. It is an important cause of continued contamination by foodborne pathogenic bacteria. Therefore, removing biofilm is the key to solving the high pollution caused by foodborne pathogenic bacteria in the food industry. Lactobacillus, a commonly recognized probiotic that is healthy for consumer, have been proven useful for isolating the potential biofilm inhibitors. However, the addition of surface components and metabolites of Lactobacillus is not a current widely adopted biofilm control strategy at present. This review focuses on the effects and preliminary mechanism of action on biofilm inhibition of Lactobacillus-derived components including lipoteichoic acid, exopolysaccharides, bacteriocins, secreted protein, organic acids and some new identified molecules. Further, the review discusses several modern biofilm identification techniques and particularly interesting new technology of biofilm inhibition molecules. These molecules exhibit stronger inhibition of biofilm formation, playing a pivotal role in food preservation and storage. Overall, this review article discusses the application of biofilm inhibitors produced by Lactobacillus, which would greatly aid efforts to eradicate undesirable bacteria from environment in the food industries.