Minimum Ciprofloxacin Concentration Affecting the Microbial Community and Antimicrobial Resistance Gene Compositions of Anaerobic Digestion Processes
Bibliographic record
Abstract
Anaerobic digestion (AD) is a well-established process employed for the reduction of wastewater treatment sludge residuals and the recovery of energy in the form of biogas.It plays a crucial role in enhancing the microbiological quality of residual biosolids, making them suitable for use as fertilizers in agricultural settings.However, concerns have arisen regarding the potential dissemination of antimicrobial resistance genes (ARGs) from wastewater to agricultural environments and subsequently entering the food production chain.The presence of antimicrobials in AD systems can exert selective pressure, promoting the proliferation of ARGs and facilitating their horizontal gene transfer (HGT) between microbial species.Ciprofloxacin (CIP), known for its hydrophobic properties, frequently accumulates in AD processes, making it one of the most prevalent antimicrobials in these environments.This study aimed to investigate the impact of CIP at concentrations observed in full-scale wastewater treatment systems on the performance of AD processes, microbial community composition, and ARG abundance.Triplicate reactors were operated using primary or secondary IV CIP égales ou supérieures à 1 µg/mL, ce qui indique une influence du CIP sur la structure et la composition des communautés microbiennes dans les systèmes de digestion anaérobie.Au-delà de l'analyse des communautés microbiennes, l'analyse quantitative par matrice de PCRq (qPCR) array de 95 ARG a indiqué une légère augmentation de leurs abondances relatives à une concentration de CIP de 10 µg/mL de CIP lorsque les boues primaires alimentaient les réacteurs.Inversement, lorsque des boues secondaires étaient traitées, l'ajout de CIP a entraîné une diminution de l'abondance relative des ARG.Ces résultats mettent en évidence les interactions complexes entre la présence d'antimicrobiens, les communautés microbiennes et la dynamique des ARG dans les systèmes de DA.Il est essentiel de comprendre les implications de la présence d'antimicrobiens dans les processus de DA pour gérer efficacement la dissémination des ARG pendant le traitement des eaux usées et atténuer ainsi les risques associés pour les environnements agricoles et la sécurité alimentaire.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".