ReviewFrontiers in genetics2020
Current Trends in Experimental and Computational Approaches to Combat Antimicrobial Resistance.
Review in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.Briefings in bioinformatics · 2026Pooled it
- Probiotic-Plant Bioactive Synergy in Gut Health: Mechanisms, Antimicrobial Activity, and Translational Challenges.Nutrients · 2026Review
- Advances in Ciprofloxacin Derivatives: Emerging Strategies to Combat Antimicrobial Resistance.Topics in current chemistry (Cham) · 2026Review
- Review
- Molecular mechanisms of antibiotic resistance in Gram-positive and Gram-negative bacteria - a narrative review.Frontiers in cellular and infection microbiology · 2026Review
- Fungal metabolite Ochratoxin A inhibits MrkD1P of multidrug-resistant Klebsiella pneumoniae: Integrated computational and in vitro validation.Journal of computer-aided molecular design · 2025Article
- AMRColab - a user-friendly antimicrobial resistance detection and visualization tool.Microbial genomics · 2024Article
- Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence.Microorganisms · 2024Review
- Bioinformatics Approaches in the Development of Antifungal Therapeutics and Vaccines.Current genomics · 2024Review
- Predicting over-the-counter antibiotic use in rural Pune, India, using machine learning methods.Epidemiology and health · 2024Article
- Spectroscopic Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups and Special Machine Learning Algorithm.Antibiotics (Basel, Switzerland) · 2023Article
- Discovery and Characterization of Polymyxin-Resistance GenesMicrobiology spectrum · 2023Article
- Recent Advances in Histidine Kinase-Targeted Antimicrobial Agents.Frontiers in chemistry · 2022Review
- Metagenomic-based surveillance systems for antibiotic resistance in non-clinical settings.Frontiers in microbiology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A multitude of factors, such as drug misuse, lack of strong regulatory measures, improper sewage disposal, and low-quality medicine and medications, have been attributed to the emergence of drug resistant microbes. The emergence and outbreaks of multidrug resistance to last-line antibiotics has become quite common. This is further fueled by the slow rate of drug development and the lack of effective resistome surveillance systems. In this review, we provide insights into the recent advances made in computational approaches for the surveillance of antibiotic resistomes, as well as experimental formulation of combinatorial drugs. We explore the multiple roles of antibiotics in nature and the current status of combinatorial and adjuvant-based antibiotic treatments with nanoparticles, phytochemical, and other non-antibiotics based on synergetic effects. Furthermore, advancements in machine learning algorithms could also be applied to combat the spread of antibiotic resistance. Development of resistance to new antibiotics is quite rapid. Hence, we review the recent literature on discoveries of novel antibiotic resistant genes though shotgun and expression-based metagenomics. To decelerate the spread of antibiotic resistant genes, surveillance of the resistome is of utmost importance. Therefore, we discuss integrative applications of whole-genome sequencing and metagenomics together with machine learning models as a means for state-of-the-art surveillance of the antibiotic resistome. We further explore the interactions and negative effects between antibiotics and microbiomes upon drug administration.
Indexed as
Identifiers
What 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.