Developing erythromycin resistance gene by heavy metals, Pb, Zn, and Co, in aquatic ecosystems
Bibliographic record
Abstract
Industrial development is the main cause of environmental pollution with various substances such as antibiotics and heavy metals. Many heavy metals with antimicrobial properties could contribute to antibiotic resistance and the emergence of antibiotic resistance genes due to the co-selection phenomenon. The aim of this study was to investigate the concurrent presence and correlation between several heavy metals and the erythromycin resistance genes in six aquatic ecosystems of Iran. Distribution and assessment of 11 erythromycin resistance genes were investigated using specific primers and online enrichment and triple-quadrupole LC-MS/MS. The concentration of heavy metals was measured using inductively coupled plasma atomic emission spectroscopy by Thermo electron corporation. Principal component analysis was performed to globally compare and to determine the similarities and differences among different aquatic ecosystems in different parts of the world in terms of the concentration of zinc and lead in their water. The results of the simple logistic regression analysis for the correlation between erythromycin resistance genes and heavy metals concentrations revealed the most significant correlation between erythromycin resistance genes and Pb concentration, followed by Co and Zn concentrations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".