Bibliographic record
Abstract
B on health regulatory recommendations, the characteristics profile (e.g., identity, safety, potency, consistency, etc.) of a vaccine candidate should be established prior to initiation of a clinical phase study. Genetic instability of the genes or transgenes within a vaccine may lead to inconsistent expression of complementing gene products and altered cellular properties. Therefore, it is important to demonstrate consistent and stable gene copy numbers over the generation length of a production run. We have previously developed a digital PCR (dPCR) assay to monitor the stability of the UL5 and UL29 transgenes in a manufactured cell line (AV529) by comparison of transgene copy numbers in the master cell bank with their copy numbers in the extended cell bank. Our developed approach was able to count the number of UL5 and UL29 transgenes directly rather than relying on a reference standard or endogenous control, quantified the absolute numbers of target molecules and not the relative numbers. Our dPCR based approach was able to overcome some of the issues associated with conventional qPCR. Herein, we developed a digital PCR approach for the determination of the gene copy numbers in not only simple (amplified DNA fragments) but also complex matrices (genomic DNA in Mycoplasma arginine and a candidate HSV type II vaccine (HSV529)). The amount of gene copy numbers in Mycoplasma arginine and viral genomes per unit sample of HSV529 is also compared to other methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".