Optimization of Viral DNA Extraction from Vaginal Swabs
Bibliographic record
Abstract
The vaginal virome remains under-characterized in healthy individuals and even less so in people with gynecologic conditions, despite its role in the maintenance of microbial homeostasis and potential for clinical application. In conditions such as vulvovaginal candidiasis (VVC), the bacterial composition of the vagina is altered in an attempt to provide defense; however, the bacterial viruses, or bacteriophages (phages), present in this environment have yet to be identified. Thus, this project aimed to establish a protocol for the extraction of viral DNA from vaginal swabs to allow for the further characterization of the vaginal virome. Initially, we applied the established virome extraction protocol using the QIAGEN QIAamp MinElute Virus Spin Kit, optimized for fecal samples. This approach had limited success due to the low biomass swabs used as input. A modified TRIzol protocol with post-purification human DNA depletion using the NEBNext Microbiome DNA Enrichment Kit yielded sequenceable DNA libraries, although with significant human DNA contamination. Our final optimized protocol begins with lysis of host cells prior to virion DNA extraction with the Cytiva Virus Pathogen kit. Our optimized protocol proved to be successful in the extraction of DNA from vaginal swabs, with minimal human DNA contamination and successful enrichment of viral reads in the final dataset. Future work using this protocol will focus on the characterization of bacteriophages present in the vaginal environment and determine how they differ in health and disease, such as in the context of VVC.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".