ReviewArchives of microbiology2025
Frontiers in superbug management: innovating approaches to combat antimicrobial resistance.
Review in Archives of microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Mapping the Convergence of Frontier Technologies for Major Environmental Challenges: A Chemical and Molecular Perspective on the Use of AI for Climate Action and Antimicrobial Resistance.Molecules (Basel, Switzerland) · 2026Review
- Metalloids: Semi as Metals yet Full of Antimicrobial Potential.ChemMedChem · 2026Review
- Disinfectant-induced bacterial resistance and antibiotic cross-resistance-mechanisms and clinical relevance.Clinical and experimental medicine · 2025Review
- Human Antimicrobial Use in Bangladesh: Five-Year Trend Analysis Including COVID-19 Pandemic Era.Antibiotics (Basel, Switzerland) · 2025Article
- Vaccines as Potential Frontliners Against Antimicrobial Resistance (AMR): A Focused Review.Infection and drug resistance · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anti-microbial resistance (AMR) is a global health issue causing significant mortality and economic burden. Pharmaceutical companies' discontinuation of research hinders new agents, while MDR pathogens or "superbugs" worsen the problem. Superbugs pose a threat to common infections and medical procedures, exacerbated by limited antibiotic development and rapid antibiotic resistance. The rising tide of antimicrobial resistance threatens to undermine progress in controlling infectious diseases. This review examines the global proliferation of AMR, its underlying mechanisms, and contributing factors. The study explores various methodologies, emphasizing the significance of precise and timely identification of resistant strains. We discuss recent advancements in CRISPR/Cas9, nanoparticle technology, light-based techniques, and AI-powered antibiogram analysis for combating AMR. Traditional methods often fail to effectively combat multidrug-resistant bacteria, as CRISPR-Cas9 technology offers a more effective approach by cutting specific DNA sequences, precision targeting and genome editing. AI-based smartphone applications for antibiogram analysis in resource-limited settings face challenges like internet connectivity, device compatibility, data quality, energy consumption, and algorithmic limitations. Additionally, light-based antimicrobial techniques are increasingly being used to effectively kill antibiotic-resistant microbial species and treat localized infections. This review provides an in-depth overview of AMR covering epidemiology, evolution, mechanisms, infection prevention, control measures, antibiotic access, stewardship, surveillance, challenges and emerging non-antibiotic therapeutic approaches.
Indexed as
Identifiers
39953143What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.