Validation of a centrifugal microfluidic sample lysis and homogenization platform for nucleic acidextraction with clinical samples
Bibliographic record
Abstract
The applications of microfluidic technologies in medical diagnostics continue to increase, particularly in the field of nucleic acid diagnostics. While much attention has been focused on the development of nucleic acid amplification and detection platforms, sample preparation is often taken for granted or ignored all together. Specifically, little or no consideration is paid to the development of microfluidic systems that efficiently extract nucleic acids from biological samples. Here, a centrifugal microfluidic platform for mechanical sample lysis and homogenization is presented. The system performs sample lysis through a magnetically actuated bead-beating system followed by a centrifugal clarification step. The supernatant is then transferred for extraction using a unique siphon. Several other new microfluidic functions are implemented on this centrifugal platform as well, including sample distribution, a unique hydraulic capillary valve, and self-venting. Additionally, the improved system has features with a small footprint designed specifically for integration with further downstream processing steps. Biological validation of the platform is performed using Bacillus subtilis spores and clinical samples (nasopharyngeal aspirates) for respiratory virus detection. The platform was found to be as efficient as in-tube bead-beating lysis and homogenization for nucleic acid extraction, and capable of processing 4 samples in batch to near PCR-ready products in under 6 min.
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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 | 0.004 | 0.004 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".