Scaled-Up Paper Dipsticks for Nucleic Acid Extraction from Soil Samples
Bibliographic record
Abstract
Nucleic acid extraction from soil samples holds paramount importance in various scientific domains, particularly in environmental microbiology, molecular ecology, and agricultural sciences. This process serves as a foundational step for numerous downstream applications, enabling a deeper understanding of soil microbial communities and their functions. Paper-based rapid nucleic acid extraction is the most cost-effective and easily accessible method available for nucleic acid extraction. In contrast to previous attempts at developing paper-based dipsticks for nucleic acid extraction, which could only analyze samples of volume < 10 μL, we report a method that enables the extraction of nucleic acids from samples 50 times larger in volume (50–650 μL). Our new design involves the use of paper-based dipsticks with corrugated edges and a pointed tip, which can be further joined together at the handle to create stacked dipsticks, thereby increasing the surface area of the dipstick in contact with the sample, and the volume of the sample from which nucleic acid can be extracted. The extracted DNA has later been quantified using a benchtop UV–vis spectroscopy-based DNA quantification device to calculate the extraction efficiency (%) of the samples under study. The application of our paper-based nucleic acid extraction dipstick has been demonstrated by conducting controlled experiments to extract nucleic acid from garden soil samples. The highest extraction yield (%) obtained was found to be approximately 52% for a soil sample spiked with DNA with a concentration of 1000 nM using a 10-stack dipstick.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".