Impact of Heavy Metal and Resistance Genes on Antimicrobial Resistance: Ecological and Public Health Implications
Bibliographic record
Abstract
Heavy metals (HMs) are widespread pollutants that can exert selection pressure on microbial populations due to their toxicity and persistence, leading to the evolution of heavy metal resistance genes (HMRGs). These genes are part of the resistome, and their spread often occurs via mobile genetic elements that allow co-selection with antibiotic and biocide resistance genes. Such processes have an impact on microbial biodiversity, biogeochemical cycling and public health in agriculture, industry and urban areas. The selection pressure exerted by HM promotes the spread of multidrug-resistant strains and thus increases ecological and health risks. This review discusses the interaction between HMRGs and genetic determinants such as virulence genes that influence biofilm formation, cellular homeostasis and oxidative stress. It also discusses the dual role of HMRGs in promoting ecological functions such as bioremediation while potentially limiting them by reducing microbial diversity. Understanding such interactions contributes significantly to targeting different systems to overcome the challenges associated with antimicrobial resistance (AMR).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".