Background/Objectives: Carbapenemase-producing Escherichia coli (CP-Ec) has emerged as an important contributor to the global crisis of antimicrobial resistance. Although less prevalent than carbapenemase-producing Klebsiella pneumoniae, CP-Ec exhibits marked genomic plasticity, efficient plasmid-mediated dissemination, and increasing involvement in bloodstream infections. This comprehensive review summarizes the global epidemiology, molecular features, treatment options, clonal structure and transmission dynamics of CP-Ec. Particular attention is given to the expanding repertoire of NDM, OXA-48-like, and KPC carbapenemases and their associated plasmid backbones. Key high-risk clones, including ST410, ST167 and ST131, are highlighted as drivers of international spread. Conclusions and Future Directions: CP-Ec bloodstream infections represent a growing clinical challenge, often associated with severe outcomes and limited therapeutic options, particularly for NDM producers. The emergence of treatment failures with last-resort agents further underscores the need for improved management strategies. Strengthened global surveillance, integration of genomic epidemiology, optimized antimicrobial stewardship, and targeted infection control measures are essential to limit the dissemination of CP-Ec and mitigate its impact on human health.

Background: Antimicrobial resistance (AMR) among ESKAPE pathogens—Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.—represents a major global health threat and accounts for a substantial proportion of healthcare-associated infections. Their genomic plasticity and adaptive regulatory responses facilitate the rapid emergence and dissemination of resistance and virulence determinants. Artificial intelligence (AI) has emerged as a powerful approach for analyzing large-scale biological datasets and identifying molecular signatures associated with antimicrobial resistance and pathogenicity. Objectives: This review examines AI-driven frameworks for predictive target discovery in ESKAPE pathogens, focusing on approaches that leverage genomic and transcriptomic data and extend toward the integration of additional omics layers within network-based and systems-level modeling frameworks. We discuss how AI methods are evolving beyond phenotypic prediction toward more biologically interpretable inference for prioritizing resistance mechanisms, virulence determinants, and candidate antimicrobial targets. Conclusions and Future Directions: Current AI applications exploit genomic, transcriptomic, and network-level data to prioritize resistance and virulence determinants and to support antimicrobial discovery, including small molecules and antimicrobial peptides. However, integrative multi-layer modeling and comprehensive experimental validation remain limited. Future advances will depend on improved integration of complementary biological data, enhanced model interpretability, and robust translational validation frameworks to enable clinically actionable AI-guided novel pathogen-targeted next-generation diagnostics, therapeutic and stewardship strategies against ESKAPE pathogens.

Background/Objectives: Invasive pulmonary aspergillosis (IPA) represents a critical respiratory condition mainly caused by Aspergillus fumigatus and other ubiquitous species such as Aspergillus flavus, Aspergillus niger, and Aspergillus terreus. IPA clinical management has been complicated by diagnostic challenges and therapeutic difficulties due to antifungal intrinsic or secondary resistance episodes. Despite this assumption, few scientific data have been reported about possible antifungal drug combinations. Herein, we propose an experimental evaluation using isavuconazole/amphotericin B and isavuconazole/caspofungin in vitro synergy assays to investigate their combined activity on Aspergillus spp. IPA clinical isolates. Methods: We globally analyzed 55 Aspergillus spp. isolates, practicing the gradient test methods with single and combined antifungal drugs through the MIC Strip test (Liofilchem, Roseto degli Abruzzi, Italy). The collected MIC values were interpreted according to the EUCAST guidelines and classified as synergy, additivity, indifference, and antagonism cases through a FIC index calculation. A statistical analysis on species’ correlation with particular synergy testing results was finally provided. Results: Despite an interesting activity against A. fumigatus, isavuconazole/amphotericin B did not report statistical significance, obtaining a consistent antagonism percentage (43.6%). On the other hand, isavuconazole/caspofungin showed a promising in vitro synergic activity, except for A. flavus isolates. Conclusions: Synergy testing demonstrated a significant species-specific trend. Future studies should be focused on Aspergillus spp. isolates and antifungal in vitro synergy testing, aiming to discourage or recommend any specific antifungal combinations, depending on the isolated species.

Dental caries is a multifactorial chronic infectious disease that impacts healthcare costs globally, caused by alterations of the plaque microbiome and proliferation of cariogenic Streptococcus mutans. Treatments targeting S. mutans, such as alternative strategies using probiotics, might be effective in preventing the development of dental caries. In this study, the probiotic formulation of Lactobacillus reuteri SGL01, vitamin C, and acerola was tested against S. mutans DSM20523. Antimicrobial activity was assessed by deferred antagonism and spot-on-lawn assays for L. reuteri SGL01. MIC and MBC of L. reuteri SGL01 cell-free supernatant (CFS), vitamin C, and acerola were determined with the microdilution method. Time–kill assays determined the bactericidal kinetics for each compound. The checkerboard method was used to evaluate the potential synergistic activity of CFS–vitamin C or CFS–acerola at scalar dilutions from 1 to 8X MIC. Lastly, antibiofilm activity was tested for each compound. Antimicrobial activity of L. reuteri SGL01 was first assessed by classic methods. MIC and MBC values differed for one dilution for all compounds, with values of 25% and 50% for CFS, 9.3 mg/mL and 18.7 mg/mL for vitamin C, and 18.7 mg/mL and 37.5 mg/mL for acerola, respectively. Moreover, time–kill assays confirmed the bactericidal activity at different timepoints: 4 h for CFS, 6 h for vitamin C, and 24 h for acerola. The fractional inhibitory concentration index (FICI) showed indifference for all combinations, and for associations tested at 2, 4, and 8XMIC. S. mutans biofilm production was impaired for all components, with stronger activity by vitamin C and acerola at lower concentrations. The probiotic formulation containing L. reuteri SGl01, vitamin C, and acerola extract exerts a bactericidal effect, especially strong for the CFS, as well as antibiofilm activity. Thus, the combination of these three components could be advantageous for their complementary effects, with use as a novel treatment against the development of dental caries by S. mutans.

Background/Objectives: Antimicrobial resistance (AMR), driven by high-risk bacterial pathogens, is a major healthcare threat. This scoping review mapped artificial intelligence/machine learning (AI/ML) and computational approaches integrated with whole-genome sequencing (WGS), genomic surveillance, rapid typing, epidemiological data, or clinical metadata for early warning of AMR outbreak clones. Methods: Following PRISMA-ScR guidance and the Population–Concept–Context (PCC) framework, PubMed/MEDLINE, Scopus, and Web of Science were searched for English-language studies published between 2010 and 2026. Eligible studies addressed AI/ML or computational approaches for AMR outbreak detection, clone surveillance, transmission analysis, or infection prevention and control (IPC). Results: Thirty-eight studies were grouped into five domains: genomic surveillance; rapid typing; resistance, risk-factor, and lineage prediction; transmission reconstruction; and IPC-oriented genomic epidemiology. AI/ML supported automation, isolate prioritization, typing triage, prediction, transmission modelling, and electronic health record (EHR)-linked route identification. Conclusions: AI/ML may enhance WGS-based AMR surveillance, but validation, dataset dependence, heterogeneity, and limited IPC outcome reporting remain key gaps.