Selective and Quantitative Detection of Influenza Virus Proteins in Commercial Vaccines Using Two-Dimensional High-Performance Liquid Chromatography and Fluorescence Detection
Bibliographic record
Abstract
In this work, we report on the applicability of two-dimensional high-performance liquid chromatography (2D-HPLC) for the comprehensive characterization of inactivated influenza vaccine proteins. This novel procedure features minimal sample treatment and combines the on-line coupling of size exclusion HPLC to reversed-phase HPLC. A comparative analysis of commercial vaccines from three different manufacturers showed the method to be highly selective by providing characteristic reproducible chromatographic profiles for each vaccine. In addition, the method provided enhanced sensitivity for most constituents as a result of the use of native fluorescence detection in the reversed-phase HPLC step. The limits of detection (at a signal-to-noise ratio of >3) for hemagglutinin (HA) antigens were 105 and 172 ng/mL for influenza A/New Caledonia/20/99 and B/Jiangsu/10/2003 strains, respectively. The potential of this 2D-HPLC procedure in terms of quantitative antigen analysis was assessed by determination of the HA content of commercial vaccines. Results provided very good correlation with nominal HA values. The reproducibility (RSD) of the whole procedure was also evaluated and was found to be better than 2 and 3% for calculated antigen concentrations expressed as micrograms of HA per milliliter in commercial vaccines for samples of the same lot (n = 5) or different lots (n = 3), respectively. In addition, it allowed the selective detection of several influenza constituents including nucleoproteins from type A and B viruses and the highly hydrophobic matrix protein 1 from both virus strains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".