ReviewCurrent microbiology2024
Heavy Metals as Catalysts in the Evolution of Antimicrobial Resistance and the Mechanisms Underpinning Co-selection.
Review in Current microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 19 citations in OpenAlex.
- From Food Systems to Gut Microbiota: Dietary Substrates, Microbial Exposure and One Health.Microorganisms · 2026Review
- Heavy Metal and Petroleum Hydrocarbon Contaminants Promote Resistance and Biofilm Formation inMicroorganisms · 2025Article
- Genomic Characterization of Multidrug-Resistant and ESBL-ProducingMicroorganisms · 2025Article
- Environmental fate of antibiotic resistance genes in livestock farming.Archives of microbiology · 2025Review
- Sludge amended soil induced multidrug and heavy metal resistance in endophytic Exiguobacterium sp. E21L: genomics evidences.World journal of microbiology & biotechnology · 2025Article
- An eco-evolutionary perspective on antimicrobial resistance in the context of One Health.iScience · 2025Review
- Antimicrobial activity of copper-nickel coated door handles: a blinded, randomized controlled study in a clinical setting.GMS hygiene and infection control · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The menace caused by antibiotic resistance in bacteria is acknowledged on a global scale. Concerns over the same are increasing because of the selection pressure exerted by a huge number of different antimicrobial agents, including heavy metals. Heavy metals are non-metabolizable and recalcitrant to degradation, therefore the bacteria can expel the pollutants out of the system and make it less harmful via different mechanisms. The selection of antibiotic-resistant bacteria may be influenced by heavy metals present in environmental reservoirs. Through co-resistance and cross-resistance processes, the presence of heavy metals in the environment can act as co-selecting agents, hence increasing resistance to both heavy metals and antibiotics. The horizontal gene transfer or mutation assists in the selection of mutant bacteria resistant to the polluted environment. Hence, bioremediation and biodegradation are sustainable methods for the natural clean-up of pollutants. This review sheds light on the occurrence of metal and antibiotic resistance in the environment via the co-resistance and cross-resistance mechanisms underpinning co-selection emphasizing the dearth of studies that specifically examine the method of co-selection in clinical settings. Furthermore, it is advised that future research incorporate both culture- and molecular-based methodologies to further our comprehension of the mechanisms underlying bacterial co- and cross-resistance to antibiotics and heavy metals.
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.