Quantification of Reverse Transcriptase Activity by Real-Time PCR as a Fast and Accurate Method for Titration of HIV, Lenti- and Retroviral Vectors
Bibliographic record
Abstract
Quantification of retroviruses in cell culture supernatants and other biological preparations is required in a diverse spectrum of laboratories and applications. Methods based on antigen detection, such as p24 for HIV, or on genome detection are virus specific and sometimes suffer from a limited dynamic range of detection. In contrast, measurement of reverse transcriptase (RT) activity is a generic method which can be adapted for higher sensitivity using real-time PCR quantification (qPCR-based product-enhanced RT (PERT) assay). We present an evaluation of a modified SYBR Green I-based PERT assay (SG-PERT), using commercially available reagents such as MS2 RNA and ready-to-use qPCR mixes. This assay has a dynamic range of 7 logs, a sensitivity of 10 nU HIV-1 RT and outperforms p24 ELISA for HIV titer determination by lower inter-run variation, lower cost and higher linear range. The SG-PERT values correlate with transducing and infectious units in HIV-based viral vector and replication-competent HIV-1 preparations respectively. This assay can furthermore quantify Moloney Murine Leukemia Virus-derived vectors and can be performed on different instruments, such as Roche Lightcycler® 480 and Applied Biosystems ABI 7300. We consider this test to be an accurate, fast and relatively cheap method for retroviral quantification that is easily implemented for use in routine and research laboratories.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 0.004 |
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".