Tracking Age-Linked Antibiotic Resistance Patterns through Building-Level Wastewater Analysis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Antimicrobial resistance (AMR) is a global health challenge, and monitoring different demographic populations can improve our understanding of its spread and prevalence in urban settlements. This study applies building-level wastewater-based epidemiology (WBE) to analyze the resistome and mobilome of age-segregated populations from an elementary school (School), a university residence (UnivRes), and an elderly care facility (ElderlyRes) all located in Girona (Catalonia, Spain). Metagenomic analyses were subsequently conducted to investigate differences in bacterial communities, antibiotic resistance genes (ARGs), and mobile genetic elements (MGEs). The results revealed age-linked variations in the relative abundance and diversity of ARGs. The wastewater collected at the School exhibited the highest abundance of ARGs, while the ElderlyRes showed the highest diversity. Furthermore, sequences affiliated with bacterial pathogens were more prevalent in samples from both the School and the ElderlyRes, emphasizing potential public health implications. Among the 12 bacterial genera most strongly correlated with ARGs (Pearson R > 0.7), 11 were identified as members of the gut microbiota, underscoring their predominant role as reservoirs of resistance compared to bacteria of environmental origin. By integrating localized wastewater sampling with metagenomics, our study uncovers demographic-specific resistome patterns, delivering actionable evidence to strengthen AMR surveillance and intervention strategies in urban populations.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 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".