Evaluation of ensilication technology for ambient DNA preservation
Bibliographic record
Abstract
Abstract Current nucleic acid preservation relies on ultra-low temperature storage (−20 °C to −80 °C), imposing significant infrastructure, cost, and accessibility barriers that limit genomic medicine worldwide. We present a comprehensive evaluation of ensilication, a silica-based encapsulation method enabling ambient-temperature preservation of DNA without compromising sequencing fidelity. Across clinical, genomic, and biochemical analyses, ensilication maintained complete diagnostic concordance with cryogenic controls, detecting all actionable variants in FFPE tumor samples, even at low variant allele frequencies. Whole-genome sequencing revealed that frozen storage accumulated up to 65% more artifactual C>T mutations than ensilicated samples, underscoring its potential to reduce false-positive calls in oncology. Both linear and circular DNA libraries preserved structural integrity across temperatures from −80 °C to 37 °C. By eliminating cold-chain dependence, ensilication enables decentralized biobanking, point-of-care testing, and equitable access to precision oncology, enabling globally accessible cancer genomics. Its compatibility with emerging sequencing platforms positions ensilication as a foundational technology for next-generation diagnostics and large-scale population studies.
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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) | 0.000 | 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) | 0.001 | 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".