<b>CO-SELECTION OF ANTIMICROBIAL RESISTANCE GENES BY HEAVY METAL RESISTANCE IN </b><b><i>Staphylococcus aureus</i></b><b>: </b><b>PUBLIC HEALTH IMPLICATIONS</b>
Bibliographic record
Abstract
The main cause of significant illness and mortality is Staphylococcus aureus, particularly methicillin-resistant S. aureus (MRSA), which makes antimicrobial resistance (AMR) a major global health concern. The co-selection of antibiotic resistance genes (ARGs) and heavy metal resistance genes (HMRGs) in S. aureus exacerbates this issue since heavy metals in environments such as livestock farms, hospitals, and wastewater treatment plants (WWTPs) promote resistant strains. This review looks at the co-selection mechanisms of co-resistance, cross-resistance, and horizontal gene transfer (HGT) and their public health consequences. It examines how metals like zinc, copper, and cadmium affect ARG selection, particularly in livestock-associated MRSA (LA-MRSA), as well as the function of mobile genetic elements (MGEs). Between 2020 and 2025, case studies and meta-analyses are used to illustrate co-selection dynamics. As information gaps, mitigation measures, and clinical and environmental reservoirs are investigated, a One Health strategy is highlighted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".