Metagenomic analysis after selective culture enrichment of wastewater demonstrates increased burden of antibiotic resistant genes in hospitals relative to the community
Bibliographic record
Abstract
Abstract Antimicrobial resistance (AMR) is an ever-increasing threat to global health. Wastewater-based surveillance is an emerging methodology that objectively enables an inclusive and comprehensive assessment of population AMR in an observed sewershed. Here we compared the resistome of two tertiary-care hospitals with two separate neighborhoods, using complimentary targeted qPCR and metagenomics of wastewater before and after selective culture enrichment for clinically important Gram negatives. In total 26 ARG-type (1225 ARG-subtypes) were found across all samples, in which β-lactam ARG was the richest (the number of different ARG-subtypes found) followed by multidrug, fluoroquinolone, macrolide-lincosamide-streptogramin (MLS) and aminoglycoside. The composition of ARGs in wastewater differed between raw wastewater pellets and culture-enriched wastewater samples and the resistomes clustered based on the type of location (Hospitals vs neighborhoods). Hospital wastewater was found to have higher diversity and greater abundance of ARGs compared to neighborhood wastewater when the composition profiles of ARGs in both raw and culture-enriched wastewater pellets. Clinically relevant ARG (i.e., VIM, NDM metallo-ß-lactamases) were detected in culture enrichment samples that were not identified in raw samples, despite a lower targeted sequencing depth. Wastewater-based surveillance is an effective, and potentially extremely important and powerful tool that could be developed to augment hospital-based infection control and antimicrobial stewardship programs, creating a safer space for those receiving care.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| 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".