Extraction of nucleic acids from blood: unveiling the potential of active pneumatic pumping in centrifugal microfluidics for integration and automation of sample preparation processes
Bibliographic record
Abstract
This paper describes the development of an on-chip nucleic acid (NA) extraction assay from whole blood using a centrifugal microfluidic platform that allows for pneumatic actuation of liquids during rotation. The combination of pneumatic and centrifugal forces makes it possible to perform fluidic operations without the need for integrating active control elements on the microfluidic cartridge. The cartridge is fabricated from thermoplastic polymers (e.g., Zeonor 1060R) and features a simple design that is compatible with injection molding. In addition, the cartridge is interfaced with two external vials for off-chip storage of the blood sample and retrieval of the eluted NA solution, respectively. On-chip capture of NAs is performed using an embedded solid-phase extraction matrix composed of commercial glass microfiber filters (Whatman GF/D and GF/F). The yield of the automated, on-chip extraction protocol, determined by measuring absorbance at 260 nm, is comparable to some of the best manually operated kits (e.g., Qiagen QIAamp DNA Mini Kit) while providing low assay-to-assay variability due to the high level of control provided by the platform for each processing step. The A260/A280 and A260/A230 ratios of the absorbance spectra also reveal that protein contamination of the sample is negligible. The capability of the pneumatic platform to circulate air flux through the microfluidic conduit was used to dry leftover ethanol residues retained in the capture matrix during washing. This method, applied in combination with localized heating, proved effective for reducing ethanol contamination in eluted samples from ∼12% to 1% (v/v).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".