Development of real‐time reverse transcriptase polymerase chain reaction methods for human rabies diagnosis
Bibliographic record
Abstract
To improve timely ante-mortem human rabies diagnosis, methods to detect viral RNA by TaqMan-based quantitative reverse transcriptase polymerase chain reactions (qRT-PCRs) have been developed. Three sets of two primers and one internal dual-labeled probe for each primer set that target distinct conserved regions of the rabies virus N gene were designed and evaluated. Using a collection of 203 isolates representative of the world-wide diversity of rabies virus, all three primers/probe sets were shown to detect a wide range of rabies virus strains with very few detection failures; the RABVD1 set in particular was the most broadly reactive. These qRT-PCR assays were shown to be quantitative over a wide range of viral titer and were 100-1,000 times more sensitive than nested RT-PCR; however, both the standard and real-time PCR methods yielded concordant results when used to test a collection of archived human suspect samples. The qRT-PCR assay was employed to monitor virus load in the saliva of a rabies virus-infected patient undergoing the Milwaukee treatment protocol. However in this case it would appear that reduction of the viral load in the patient's saliva over time did not appear to correlate well with clearance of viral components from the brain.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".