Integrated environmental and clinical surveillance for the prevention of acute respiratory infections in closed settings and vulnerable communities: school, prison and nursing home (Stell-ARI Project)
Bibliographic record
Abstract
Theme: Importance of a global wastewater surveillance system for public health Background: Water borne pollutants are a known link to increasing levels of antimicrobial resistance (AMR). An important route for AMR into the environment is via sewerage networks where potential key control points are located in wastewater treatment plants (WWTPs) ¹. Presently there is no mainstream strategy to influence the emerging persistent antibiotic and heavy metal contamination linking resistance gene contamination in wastewater (WW) and WWTP biofilms. Methods: Qualitative and quantitative analysis of WW and WWTP biofilms from four different sampling points (in triplicate) throughout the WWTP over a yearlong sampling campaign. Flame Atomic Absorption Spectrophotometry (FAAS) for heavy metal (HM) quantification (Cr, Cu, Fe, Pb, Mg, Mn, Ni, Ag and Zn). High-Pressure Liquid Chromatography Mass Spectrometry/MS (HPLC-MS/MS) for antibiotic detection and quantification (amoxicillin, azithromycin, ciprofloxacin, clarithromycin, erythromycin, flucloxacillin, metronidazole, ofloxacin, sulfamethoxazole and trimethoprim). High-Throughput real-time chip PCR (qPCR) for quantification of AMR genes (qepA, sul1_2, blandM, blaCTX-M, blaTEM_1, tetX, mcr1, nimE, ermF_1, acc(6’)-Ib_2 and dfrA1_1). Results: A detection was made of significant seasonal variations of pharmaceuticals and heavy metals in conjunction with the identification of associated AMR genes in wastewater and biofilms. Several of the antibiotics were detected over the predicted no-effect concentration (PNEC)² in both the wastewater and in the biofilms with one producing a bio-concentration factor of 3.1, which classifies it (according to EU guidelines) as bio-accumulative in the WWTP. This accumulation may be significantly influencing which resistance genes transfer into the receiving environmental waters. Conclusion and Future Work: The bioaccumulation of heavy metals and antibiotics by wastewater biofilms may influence the quantity of resistance genes found in the surrounding aquatic environment. This research will guide wastewater management to reduce AMR. It will also provide concentrations for the next stage of this study into the phycoremediation of antibiotics and heavy metals using the micro-algae Chlamydomonas acidophila. A photo-bioreactor culturing these algae in WW may reduce the bioavailability of antibiotics and heavy metals throughout the WWTP and reduce AMR in the environment.
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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 | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".