A novel, magnetic bead-based extraction method for the isolation of antimicrobial resistance genes with a case study in river water in Malawi
Bibliographic record
Abstract
AIMS: The environment is increasingly recognized as an important reservoir of antimicrobial resistance genes (ARGs), which can be identified using molecular platforms. Yet, environmental surveillance remains an underutilised tool as there is no agreement on the best strategy for sample processing. We aim to develop a low-cost extraction method independent to commercial kits or reagents. METHODS AND RESULTS: We present a novel, magnetic bead-based method for the isolation of ARGs from river water named MagnaExtract. We present this with analytic limit of detection as well as a case study in Southern Malawi. Here we compare the DNA yield from MagnaExtract with commercially available QIAGEN kits and the crude boil and spin method, using a high-resolution melt analysis PCR panel designed for the detection of third-generation cephalosporin and carbapenem-resistant genes from 98 water samples. CONCLUSION: The MagnaExtract method is comparable, and in some instance's superior to commercially available kits for the isolation of ARGs from river water samples. SIGNIFICANCE AND IMPACT OF THE STUDY: The MagnaExtract approach offers a simple, affordable, high yielding extraction method that could be used for the detection of ARGs from river water samples in surveillance campaigns in East Africa.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".