Antibiotic resistomes and microbial communities in biosolid fertilizers collected from two Canadian wastewater treatment plants in a 10-years interval-potential risks to food chains?
Bibliographic record
Abstract
Dissemination of microorganisms with antimicrobial resistance genes (ARGs) through the food chain has been recognized as a growing public health concern worldwide. Biosolids, a product of wastewater treatment process, have been used as fertilizers in agriculture globally and have also been considered as a potential source of pathogens and ARGs for horizontal transfer across various environments. This study characterized antibiotic resistomes and microbiota in 24 biosolids samples collected from two Canadian waste water treatment plants in different cities in 2009 and 2019. The ARGs were detected using a qPCR array kit, and microbiota was analyzed using 16S ribosomal RNA gene amplicon sequencing. Furthermore, correlation analysis of ARG abundance and bacterial genera abundance was explored to predict potential hosts of ARGs. Seventy-one of 84 ARGs were detected in at least one or more samples with 12 ARGs being detected in all samples. Antibiotic resistomes did not show a statistically significant distinction between different collection years, sites, or year and site combined in principle coordinate analysis. The microbiota communities were significantly different between samples collected in different years, sites, or year and site combined. In total 34 phyla were detected with 13 genera among the top three phyla were typically related to the human gut microbiota and seven of them showing strong correlation with ARGs related to aminoglycoside and beta-lactam resistance. This study provides valuable baseline information and consistent trend on ARGs and bacterial communities in biosolid fertilizers in Canada, indicating that the biosolid fertilizer could potentially be a source of ARGs in the agricultural soils and may leading to potential contamination of plant-based food chains.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".