Comparative performance of serum and plasma samples in SARS-CoV-2 serology and neutralization assays
Bibliographic record
Abstract
The SARS-CoV-2 pandemic catalyzed the rapid development and deployment of serological assays, which have been pivotal for monitoring antibody responses to infection and vaccination, guiding vaccine design, and shaping public health strategies. Historically, serum and plasma samples have been considered largely interchangeable in serological testing. However, the precise extent of their similarity or potential differences in antibody detection and quantification remains not fully characterized. This distinction is critical as the choice of sample type carries practical and economic implications, particularly in large-scale seroprevalence studies. To address this, we evaluated IgG, IgM, and IgA antibodies targeting key SARS-CoV-2 antigens (spike, RBD, and N) and assessed neutralization efficiency in 124 paired serum and plasma samples collected simultaneously. Using both manual and automated serological and neutralization assays, we demonstrated that while serum and plasma differ in recovered volume, this difference does not affect antibody concentration or functional neutralization. Our findings confirm that serum and plasma are effectively interchangeable for SARS-CoV-2 serological studies, providing robust evidence to support streamlined, flexible, and cost-effective study designs without compromising data accuracy.
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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".