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.

Facial microbiota dysbiosis is an important factor in causing acne vulgaris. The present study aimed to analyze the effect of 2% Supramolecular Salicylic Acid (SSA) on acne-associated facial bacteria. In the current study, 30 acne vulgaris patients (treated with 2% SSA for eight weeks) and ten volunteers with no facial acne were selected. Samples from acne patients (before and after treatment) and volunteers (not treated) were analyzed via high throughput sequencing, Deblur algorithm, and R microbiome package. After treatment with 2% SSA, the total lesion count and global acne grading system (GAGS) score reduced significantly (p < 0.001). Metagenomic sequencing analysis revealed that the pre-treated acne group had low α and deviated β diversity compared to the control and post-treated acne groups. Due to the treatment with 2% SSA, α diversity index was increased and β diversity was stabilized significantly (p < 0.001). The relative abundance of bacterial genera in the pre-treated acne group was uneven and had a high proportion of Staphylococcus, Ralstonia, and Streptococcus. The proportion of these three genera was significantly decreased in the post-treated group, and overall bacteria genera distribution tends toward the healthy individual. It is concluded that 2% SSA normalizes the microbial communities associated with the skin.

Introduction: Multidrug-resistant urinary tract pathogens, primarily Klebsiella pneumoniae and Pseudomonas aeruginosa , represent a significant and growing public health challenge. The overuse of antibiotics has accelerated the development of resistance, creating an urgent need for alternative antimicrobial strategies. This study aimed to evaluate the antibacterial efficacy of a peppermint oil nanoemulsion (PEONE) against clinical multidrug-resistant (MDR) isolates of K. pneumoniae and P. aeruginosa , with a specific focus on its potential for resistance modulation and its mechanism of action. Methods: Clinical isolates were obtained from patients with urinary tract infections and their antibiotic susceptibility profiles were determined. The PEONE was prepared using ultrasonic emulsification and characterized, revealing a droplet size of 190.21 ± 0.5 nm and a polydispersity index (PDI) of 0.15 ± 0.021. Antibacterial activity was assessed by determining the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). Membrane integrity was evaluated using DNA and protein leakage assays, and bacterial killing over time was measured with time-kill kinetics. Furthermore, molecular docking and 100 ns molecular dynamic simulations were performed against β-lactamase enzymes (PDB: 4EXY from K. pneumoniae , 6R73 from P. aeruginosa ) to identify key bioactive components within the nanoemulsion. Results: The bacterial isolates were resistant to Levofloxacin, Penicillin G, Ceftazidime, and amoxicillin-clavulanic acid (AMC). PEONE demonstrated potent antibacterial activity, with an MIC of 0.1% v/v and an MBC of 0.14% v/v. DNA and protein leakage increased significantly ( p < 0.05) with higher PEONE concentrations, indicating bacterial membrane disruption. Time-kill assays showed a sustained reduction in bacterial viability over 72 hours, with significant differences emerging after 12 hours of exposure. Molecular docking revealed that caryophyllene, a major component of peppermint oil, had the highest binding affinity against both β-lactamase targets (−9.2 kcal/mol for 4EXY; −7.1 kcal/mol for 6R73). The stability of this binding was confirmed through molecular dynamics simulations. Discussion: The findings indicate that PEONE is effective at inhibiting and killing MDR K. pneumoniae and P. aeruginosa . The observed leakage of DNA and proteins suggests that the mechanism of action likely involves disruption of the bacterial membrane, leading to the loss of intracellular contents. This is further supported by the computational data, which identified caryophyllene as a key component with stable binding to resistance-associated β-lactamase enzymes. These results position PEONE as a promising, plant-based alternative for combating antibiotic-resistant urinary tract infections. Further in vivo studies are warranted to explore its clinical applicability and safety profile.