Use of an Internal Positive Control in a Multiplex Reverse Transcription-PCR To Detect West Nile Virus RNA in Mosquito Pools
Bibliographic record
Abstract
We report on the use of West Nile virus Armored RNA as an internal positive control (IPC) for the extraction and reverse transcription-PCR (RT-PCR) of RNA extracted from field-collected mosquitoes and on a multiplex real-time Taqman RT-PCR to simultaneously detect the 3' noncoding region of West Nile virus and the West Nile virus NS5-2 region comprising the IPC. Mosquito pools from the province of British Columbia, Canada (n = 635), were tested in duplicate and found to be negative for West Nile virus and positive for the IPC. Known West Nile virus-positive supernatants from mosquito pools from the provinces of Alberta and Manitoba were tested in duplicate and found to be positive for both regions of the West Nile virus genome. The mean cycle threshold (Ct) value for the IPC in batch extraction controls +/- 2 standard deviations was found to be 36.43 +/- 1.78 cycles. IPCs of 98.4% (624) of West Nile virus-negative pools fell within this range, indicating the reproducibility of RNA extraction and RT-PCR for pools varying in mosquito genus and number. A comparison of mosquito pool genera revealed no significant genus effect on the Ct value of the IPC. The incorporation of West Nile virus Armored RNA as an IPC allows monitoring of RNA extraction and RT-PCR and detection of false-negative results due to failures in these processes or to PCR inhibition, respectively.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| 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 | 0.001 |
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".