Rapid Filtration Separation-Based Sample Preparation Method for Bacillus Spores in Powdery and Environmental Matrices
Bibliographic record
Abstract
Authorities frequently need to analyze suspicious powders and other samples for biothreat agents in order to assess environmental safety. Numerous nucleic acid detection technologies have been developed to detect and identify biowarfare agents in a timely fashion. The extraction of microbial nucleic acids from a wide variety of powdery and environmental samples to obtain a quality level adequate for these technologies still remains a technical challenge. We aimed to develop a rapid and versatile method of separating bacteria from these samples and then extracting their microbial DNA. Bacillus atrophaeus subsp. globigii was used as a simulant of Bacillus anthracis. We studied the effects of a broad variety of powdery and environmental samples on PCR detection and the steps required to alleviate their interference. With a benchmark DNA extraction procedure, 17 of the 23 samples investigated interfered with bacterial lysis and/or PCR-based detection. Therefore, we developed the dual-filter method for applied recovery of microbial particles from environmental and powdery samples (DARE). The DARE procedure allows the separation of bacteria from contaminating matrices that interfere with PCR detection. This procedure required only 2 min, while the DNA extraction process lasted 7 min, for a total of <10 min. This sample preparation procedure allowed the recovery of cleaned bacterial spores and relieved detection interference caused by a wide variety of samples. Our procedure was easily completed in a laboratory facility and is amenable to field application and automation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".