Evaluation of a Reverse Transcription-Quantitative Polymerase Chain Reaction (RT-qPCR)-Based Microneutralization Assay for Assessing Clinical Human Cytomegalovirus-Neutralizing Antibody Activity
Bibliographic record
Abstract
Development of a vaccine for human cytomegalovirus (hCMV) is critical because of the severe consequences of infection in congenitally infected newborns and immunocompromised patients. The assessment of hCMV-neutralizing antibody activity is crucial for vaccine development. This study evaluated a RT-qPCR assay targeting the immediate-early gene transcript of hCMV for determining microneutralizing antibody activity. The assay was evaluated for sensitivity, specificity, and precision using endotheliotropic clinical isolate VR1814 that infects fibroblasts, epithelial, and endothelial cells. The RT-qPCR-based neutralization assay was compared with an immunostaining-based neutralization assay using virions present in hCMV-positive urine, saliva, and breast-milk samples. Our results showed that hCMV replication was detectable at 20 h post-infection with a limit of detection of 1 infectious units (IU)/reaction. The RT-qPCR assay had a dynamic range of 1 to 1.0 × 104 IU/reaction, with coefficients of variation ranging from 0.94% to 15.08%. The RT-qPCR results were in high agreement with the immunostaining assay for hCMV-antibody neutralization assessment. Overall, the RT-qPCR neutralization assay is a reliable, rapid, efficient, and sensitive alternative method for evaluating hCMV-neutralizing activity in vitro.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".