Spectroscopic Characterization and Differentiation of SARS-CoV-2 Virus-like Particles
Bibliographic record
Abstract
Virus-like particles (VLPs) are recombinant, noninfectious, self-assembled structures that are made up of the viral structural proteins that mimic the morphology of viruses but lack genomic material. VLPs have been used to develop vaccines against viruses and cancer, leading to a surge of industry interest in exploring VLP vaccines. There are strict quality controls as a part of downstream processing in the production of nonreplicating VLPs. We characterized SARS-CoV-2 VLPs of the Beta and Omicron BA.5 subvariants, which differ in 43 amino acids in the spike protein. By comparing the Raman spectra of these particles with those of SARS-CoV-2 virions and purified RNA isolated from yeast, we confirmed the absence of genomic material in the VLPs, a crucial requirement for validating manufactured VLP vaccines. Principal component analysis (PCA) was applied to UV–visible spectra between 240 and 300 nm wavelength and Raman spectra in the range of 3200–800 cm –1 . The PCA score plots showed a clear separation between Beta and Omicron BA.5 VLPs. This study shows that spectroscopic techniques, combined with chemometric tools, can be used for rapid, label-free analysis with minimal sample preparation for the characterization of the VLPs. Thus, Raman spectroscopy can serve as a valuable tool for ensuring the structural integrity and quality control of VLPs for vaccine production.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".