Polymer-based microfluidic chip for rapid and efficient immunomagnetic capture and release of Listeria monocytogenes
Bibliographic record
Abstract
Infections caused by foodborne pathogens such as Listeria monocytogenes pose a threat to public health while timely detection is challenging due to pathogen low numbers. The development of robust and efficient sample preparation techniques is crucial to improve detection sensitivity and workflow. Immunomagnetic separation using magnetic nanoparticles (MNPs) is attractive, as it can efficiently capture target cells. For food safety applications, a platform is needed to rapidly process large sample volumes, allowing capture and release of target bacteria conjugated to immunomagnetic nanoparticles (IMNPs). Herein, we demonstrate a method for magnetic capture and release of bacteria-IMNPs complex based on a 3D magnetic trap integrated on a polymeric microfluidic device. The 3D magnetic capture region consist of a dense array of high-aspect ratio (3 : 1) cylindrical pillars embossed in thermoplastic polymer and coated with soft ferromagnetic nickel by an electroless deposition technique. This allows the generation of strong and switchable magnetic capture regions due to the very low remanence of the nickel shell. We propose and validate an optimized configuration of capture regions for efficient localized capture and rapid release of MNPs and IMNPs conjugated to L. monocytogenes. A maximum recovery rate for MNPs corresponded to 91% while a maximum capture efficiency of 30% was obtained for live bacteria, with a minimum detectable sample concentration of ~10 cfu ml(-1) in 1 ml volume using plate-culture method. We believe that the flexible design and low-cost fabrication process of the proposed system will allow rapid sample preparation for applications beyond food and water safety, including point-of-care diagnosis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".