Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

Gestal, Mónica Cartelle; Dedloff, Margaret R.; Torres-Sangiao, Eva

doi:10.3390/app9122486

Cited by 16 publications

(7 citation statements)

References 136 publications

(158 reference statements)

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“…As part of their research, Getsal et al used a combination of tools to undertake antibiotic susceptibility—namely, screening flow cytometer antimicrobial susceptibility testing and assisted machine learning were used to improve current AST methods [ 53 ]. This type of artificial intelligence technology produces a dependable output in less than 3 h. A fully developed IR-spectrometer approach has also emerged in recent years that integrates infrared (IR) spectroscopy with artificial neural networks to minimize the amount of time required to perform AST from 24 h to 30 min [ 54 ].…”

Section: Strategies To Overcome Antibiotic Resistancementioning

confidence: 99%

“…Additional obstacles may include time constraints for otherwise no reimbursable tasks. Clinicians are unlikely to dedicate time to implementing an ASP if it does not generate revenue or could incur additional costs for their practice [ 53 ]. A typical ASP program may involve pharmacists, ID physicians, educational programs for providers and patients, and mechanisms in place for interventions, tracking, and reporting data.…”

Section: Artificial Intelligence Vs Antibiotic Stewardship Programmentioning

confidence: 99%

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Application of Artificial Intelligence in Combating High Antimicrobial Resistance Rates

et al. 2022

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Artificial intelligence (AI) is a branch of science and engineering that focuses on the computational understanding of intelligent behavior. Many human professions, including clinical diagnosis and prognosis, are greatly useful from AI. Antimicrobial resistance (AMR) is among the most critical challenges facing Pakistan and the rest of the world. The rising incidence of AMR has become a significant issue, and authorities must take measures to combat the overuse and incorrect use of antibiotics in order to combat rising resistance rates. The widespread use of antibiotics in clinical practice has not only resulted in drug resistance but has also increased the threat of super-resistant bacteria emergence. As AMR rises, clinicians find it more difficult to treat many bacterial infections in a timely manner, and therapy becomes prohibitively costly for patients. To combat the rise in AMR rates, it is critical to implement an institutional antibiotic stewardship program that monitors correct antibiotic use, controls antibiotics, and generates antibiograms. Furthermore, these types of tools may aid in the treatment of patients in the event of a medical emergency in which a physician is unable to wait for bacterial culture results. AI’s applications in healthcare might be unlimited, reducing the time it takes to discover new antimicrobial drugs, improving diagnostic and treatment accuracy, and lowering expenses at the same time. The majority of suggested AI solutions for AMR are meant to supplement rather than replace a doctor’s prescription or opinion, but rather to serve as a valuable tool for making their work easier. When it comes to infectious diseases, AI has the potential to be a game-changer in the battle against antibiotic resistance. Finally, when selecting antibiotic therapy for infections, data from local antibiotic stewardship programs are critical to ensuring that these bacteria are treated quickly and effectively. Furthermore, organizations such as the World Health Organization (WHO) have underlined the necessity of selecting the appropriate antibiotic and treating for the shortest time feasible to minimize the spread of resistant and invasive resistant bacterial strains.

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Section: Strategies To Overcome Antibiotic Resistancementioning

confidence: 99%

Section: Artificial Intelligence Vs Antibiotic Stewardship Programmentioning

confidence: 99%

Application of Artificial Intelligence in Combating High Antimicrobial Resistance Rates

et al. 2022

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“…The diverse domain arrangement and high sequence variability of the M proteins enable S. pyogenes to form protein interactions with various human proteins, revealing a dense and highly organized protein interaction network ( 99 ). To determine the stoichiometric relationship between pathogen, surface proteins, and interacting host proteins, the Malmström group developed a dynamic model to study the relationship between the bacterial surface and its adhered host proteins ( 100 ) by a surface adsorption plasma approach in combination with MS ( 99 , 101 ). The same Malmström group determined the Fc-binding interface, demonstrating a specific site in the IgG CH3 domain (essential for binding to FcγR receptor).…”

Section: The Dynamic Host-pathogen Interactions During Infectionmentioning

confidence: 99%

Proteomic Approaches to Unravel Mechanisms of Antibiotic Resistance and Immune Evasion of Bacterial Pathogens

et al. 2022

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The profound effects of and distress caused by the global COVID-19 pandemic highlighted what has been known in the health sciences a long time ago: that bacteria, fungi, viruses, and parasites continue to present a major threat to human health. Infectious diseases remain the leading cause of death worldwide, with antibiotic resistance increasing exponentially due to a lack of new treatments. In addition to this, many pathogens share the common trait of having the ability to modulate, and escape from, the host immune response. The challenge in medical microbiology is to develop and apply new experimental approaches that allow for the identification of both the microbe and its drug susceptibility profile in a time-sensitive manner, as well as to elucidate their molecular mechanisms of survival and immunomodulation. Over the last three decades, proteomics has contributed to a better understanding of the underlying molecular mechanisms responsible for microbial drug resistance and pathogenicity. Proteomics has gained new momentum as a result of recent advances in mass spectrometry. Indeed, mass spectrometry-based biomedical research has been made possible thanks to technological advances in instrumentation capability and the continuous improvement of sample processing and workflows. For example, high-throughput applications such as SWATH or Trapped ion mobility enable the identification of thousands of proteins in a matter of minutes. This type of rapid, in-depth analysis, combined with other advanced, supportive applications such as data processing and artificial intelligence, presents a unique opportunity to translate knowledge-based findings into measurable impacts like new antimicrobial biomarkers and drug targets. In relation to the Research Topic “Proteomic Approaches to Unravel Mechanisms of Resistance and Immune Evasion of Bacterial Pathogens,” this review specifically seeks to highlight the synergies between the powerful fields of modern proteomics and microbiology, as well as bridging translational opportunities from biomedical research to clinical practice.

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“…Today, the use of in silico experiments (research conducted by means of computer modeling or computer simulation) jointly with computational and machine learning offer an in depth understanding of systems biology, allowing us to use this knowledge for the prevention, prediction, and control of infectious disease. An in-depth knowledge of host–pathogen–protein interactions, combined with a better understanding of a host's immune response and bacterial fitness, is key determinants for halting infectious diseases and antimicrobial resistance dissemination [ 77 ]. IoTs (Internet of Things) are providing a platform that allows public-health agencies access to data for monitoring the COVID-19 pandemic.…”

Section: Brief Research Summaries On Infectious Diseases and Covid-19mentioning

confidence: 99%

Immunology

Catania

2022

The Paradox of the Immune System

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Computational Health Engineering Applied to Model Infectious Diseases and Antimicrobial Resistance Spread

Cited by 16 publications

References 136 publications

Application of Artificial Intelligence in Combating High Antimicrobial Resistance Rates

Application of Artificial Intelligence in Combating High Antimicrobial Resistance Rates

Proteomic Approaches to Unravel Mechanisms of Antibiotic Resistance and Immune Evasion of Bacterial Pathogens

Immunology

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