A low-cost homogenizing device for in-field and remote DNA and RNA extraction
Bibliographic record
Abstract
Environmental monitoring of microorganisms is critical for the protection and enhancement of human and ecosystem health. Even though these molecular methods have overtaken traditional culture-based methods and become more accessible, these techniques still require expensive equipment and dedicated facilities to process samples which in the context of a global pandemic, remote sampling areas or low-income countries can be extremely challenging. Sample preparation and sample homogenisation are critical steps for molecular-based techniques, especially for the extraction of DNA and RNA. This study developed a low-cost, open-source, freely available 3D printed homogenizer for the processing of DNA and RNA extraction. The BoSL Beater 3D is a portable device that allows researcher to perform bead-beating steps commonly required for environmental sample extraction protocols in the field and without access to main's power. The BoSL Beater 3D was tested on filtered wastewater samples and passive samplers exposed to wastewater over a 24-hour period and showed similar or better performance to the traditional laboratory bead beater for both the extraction of DNA and RNA. The cost of this 3D homogeniser is roughly $18 AUD ($296 AUD with the jigsaw, which is roughly 57 times cheaper than a traditional bead beater) and has the added usability of being portable and easily adaptable to any type of jigsaw. In combination to newly developed field extraction kits as well as portable PCR machines, this 3D homogeniser could provide the tool necessary to enable access to molecular testing in remote setting as well as developing countries, which may not have access to fully equipped laboratories, but also allow for timely reporting. In addition, the BoSL Beater 3D, in combination with field extraction kit, can allow more flexibility to researchers while sampling, shipping, and processing DNA and RNA samples, whilst maintaining quality of these samples.
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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 | 0.001 |
| 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) | 0.006 | 0.007 |
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".