Host–Pathogen Interaction 3.0

Burkovski, Andreas

doi:10.3390/ijms232112811

Cited by 2 publications

(1 citation statement)

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“…Microorganisms have surrounded us and interacted with plants, animals and humans, causing diseases [Burkovski, 2022;Altermann and Kazmierczak, 2003;Cavalier-Smith, 2006;Schopf, 2006]. Microorganisms have surrounded us for a long time.…”

Section: Introductionmentioning

confidence: 99%

Brief History of Thermodynamic Research on Microorganisms

Popovic¹

2023

Preprint

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Pathogen-host interaction (virus-host, bacteria-host and fungi-host) is analyzed from the perspective of biothermodynamics. A mechanistic model of pathogen-host interactions, which was earlier applied to virus-host interactions, was applied for the first time to bacteria-host and fungi-host interactions. Through a mechanistic model suggested in this research, it is possible to explain the phenomenon of tropism of microorganisms to develop infections in certain tissues. Gibbs energy of biosynthesis represents the driving force for growth of bacteria inside host organisms. A growing bacterial colony represents a thermodynamic system, while the host organism is its surroundings. The permissiveness coefficient determines the possibility of growth of bacteria in an appropriate environment (tissue).

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Section: Introductionmentioning

confidence: 99%

Brief History of Thermodynamic Research on Microorganisms

Popovic¹

2023

Preprint

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Artificial intelligence applications in the diagnosis and treatment of bacterial infections

Zhang,

Zhang

et al. 2024

Front. Microbiol.

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The diagnosis and treatment of bacterial infections in the medical and public health field in the 21st century remain significantly challenging. Artificial Intelligence (AI) has emerged as a powerful new tool in diagnosing and treating bacterial infections. AI is rapidly revolutionizing epidemiological studies of infectious diseases, providing effective early warning, prevention, and control of outbreaks. Machine learning models provide a highly flexible way to simulate and predict the complex mechanisms of pathogen-host interactions, which is crucial for a comprehensive understanding of the nature of diseases. Machine learning-based pathogen identification technology and antimicrobial drug susceptibility testing break through the limitations of traditional methods, significantly shorten the time from sample collection to the determination of result, and greatly improve the speed and accuracy of laboratory testing. In addition, AI technology application in treating bacterial infections, particularly in the research and development of drugs and vaccines, and the application of innovative therapies such as bacteriophage, provides new strategies for improving therapy and curbing bacterial resistance. Although AI has a broad application prospect in diagnosing and treating bacterial infections, significant challenges remain in data quality and quantity, model interpretability, clinical integration, and patient privacy protection. To overcome these challenges and, realize widespread application in clinical practice, interdisciplinary cooperation, technology innovation, and policy support are essential components of the joint efforts required. In summary, with continuous advancements and in-depth application of AI technology, AI will enable doctors to more effectivelyaddress the challenge of bacterial infection, promoting the development of medical practice toward precision, efficiency, and personalization; optimizing the best nursing and treatment plans for patients; and providing strong support for public health safety.

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Host–Pathogen Interaction 3.0

Abstract: Microorganisms can interact with plants, animals and humans in many different ways, e [...]

Cited by 2 publications

References 47 publications

Brief History of Thermodynamic Research on Microorganisms

Brief History of Thermodynamic Research on Microorganisms

Artificial intelligence applications in the diagnosis and treatment of bacterial infections

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