A Bayesian approach to modeling antimicrobial multidrug resistance

The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance

Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes to address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, and epidemiological data for predicting AMR outbreaks. Although AI/ML are relatively new fields, numerous case studies offer substantial evidence of their successful application in predicting AMR outbreaks with greater accuracy. These models can provide insights into the discovery of novel antimicrobials, the repurposing of existing drugs, and combination therapy through the analysis of their molecular structures. In addition, AI-based clinical decision support systems in real-time guide healthcare professionals to improve prescribing of antibiotics. The review also outlines how can AI improve AMR surveillance, analyze resistance trends, and enable early outbreak identification. Challenges, such as ethical considerations, data privacy, and model biases exist, however, the continuous development of novel methodologies enables AI/ML to play a significant role in combating AMR.

Bayesian Calibration to Address the Challenge of Antimicrobial Resistance: A Review

Antimicrobial resistance (AMR) emerges when disease-causing microorganisms develop the ability to withstand the effects of antimicrobial therapy. This phenomenon is often fueled by the human-to-human transmission of pathogens and the overuse of antibiotics. Over the past 50 years, increased computational power has facilitated the application of Bayesian inference algorithms. In this comprehensive review, the basic theory of Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) methods are explained. These inference algorithms are instrumental in calibrating complex statistical models to the vast amounts of AMR-related data. Popular statistical models include hierarchical and mixture models as well as discrete and stochastic epidemiological compartmental and agent based models. Studies encompassed multi-drug resistance, economic implications of vaccines, and modeling AMR in vitro as well as within specific populations. We describe how combining these topics in a coherent framework can result in an effective antimicrobial stewardship. We also outline recent advancements in the methodology of Bayesian inference algorithms and provide insights into their prospective applicability for modeling AMR in the future.

Understanding the Role of SARS-CoV-2 ORF3a in Viral Pathogenesis and COVID-19

The ongoing SARS-CoV-2 pandemic has shocked the world due to its persistence, COVID-19-related morbidity and mortality, and the high mutability of the virus. One of the major concerns is the emergence of new viral variants that may increase viral transmission and disease severity. In addition to mutations of spike protein, mutations of viral proteins that affect virulence, such as ORF3a, also must be considered. The purpose of this article is to review the current literature on ORF3a, to summarize the molecular actions of SARS-CoV-2 ORF3a, and its role in viral pathogenesis and COVID-19. ORF3a is a polymorphic, multifunctional viral protein that is specific to SARS-CoV/SARS-CoV-2. It was acquired from β-CoV lineage and likely originated from bats through viral evolution. SARS-CoV-2 ORF3a is a viroporin that interferes with ion channel activities in host plasma and endomembranes. It is likely a virion-associated protein that exerts its effect on the viral life cycle during viral entry through endocytosis, endomembrane-associated viral transcription and replication, and viral release through exocytosis. ORF3a induces cellular innate and pro-inflammatory immune responses that can trigger a cytokine storm, especially under hypoxic conditions, by activating NLRP3 inflammasomes, HMGB1, and HIF-1α to promote the production of pro-inflammatory cytokines and chemokines. ORF3a induces cell death through apoptosis, necrosis, and pyroptosis, which leads to tissue damage that affects the severity of COVID-19. ORF3a continues to evolve along with spike and other viral proteins to adapt in the human cellular environment. How the emerging ORF3a mutations alter the function of SARS-CoV-2 ORF3a and its role in viral pathogenesis and COVID-19 is largely unknown. This review provides an in-depth analysis of ORF3a protein's structure, origin, evolution, and mutant variants, and how these characteristics affect its functional role in viral pathogenesis and COVID-19.