FieldNA: a 3D printed vertical microfluidic device for portable nucleic acid isolation from olive oil samples
Bibliographic record
Abstract
Isolation and purification of nucleic acid is an essential step in molecular assays for several application areas including healthcare, food safety and security, environmental monitoring, forensic science, etc. Nucleic acid extraction is a critical bottleneck towards field deployable nucleic acid-based assays, limiting them to laboratory setups and bench-top configurations. In addition, this lack of portability leads to longer timelines for sample processing and time-to-results, and higher testing costs, limiting access to this highly sensitivity assay tool in many instances. Several efforts have explored the creation of portable nucleic acid extraction systems to complement recent innovations in reducing the footprint and overhead of the nucleic acid test assays; however, most solutions are dependent on supporting systems such as power supply, and peripheral laboratory equipment (centrifuges, incubators). In this work, we present a novel 3D printed and fully disposable device, FieldNA, which minimizes specialized reagents and laboratory equipment requirements for nucleic acid extraction. The device relies on gravity driven vertical flow, and a magnet assisted bead washing and solid-liquid separation phase. Its functionality is demonstrated through DNA extraction from olive oil samples, and its performance is compared to three widely used extraction methods: CTAB combined with phenol chloroform (PCl), and two commercial filter column-based DNA extraction kits. The optimized FieldNA device prototype repeatably produced nucleic acid yield and quality comparable to the above lab-based olive oil DNA extraction techniques. The 3D printed device's performance in isolating olive DNA from different batches of olive oil samples indicates its suitability for handling complex agricultural products, and the viable scalability for implementation in a wide spectrum of applications ranging from food to health sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".