Stability of SARS-CoV-2 IgG in multiple laboratory conditions and blood sample types
Bibliographic record
Abstract
BACKGROUND: COVID-19 seroprevalence studies use serum/plasma samples to detect SARS-CoV-2 IgG. Data supporting alternate specimen types and freeze-thaw antibody stability is lacking. The stability of IgG and other immunoglobulins in multiple blood sample types stored in differing conditions and multiple freeze-thaw cycles (FTCs) was evaluated. MATERIALS AND METHODS: Serum, plasma, and heparinized-plasma samples were collected from COVID-19 recovered individuals. Samples underwent testing for SARS-CoV-2 antibodies upon collection, after each of 10-12 FTCs, and storage at -70°C, -20°C, 4°C, and room-temperature for 10-12 days using four high-throughput commercial assays, two rapid-test cassettes, a manual ELISA, and a surrogate neutralization assay. RESULTS: All three specimen types were collected from 34 COVID-19 recovered seropositive individuals (≥21 days post-symptoms). Using the Architect and Liaison assays, a positive qualitative SARS-CoV-2 IgG result was detected daily up to 12 FTCs and up to 10 days of storage at different temperatures. An additional 25 plasma samples consistently demonstrated detection of SARS-CoV-2 antibodies daily after 12 FTCs and storage at -20°C using two rapid test cassette assays (SD Biosensor and Hangzhou All Test), manual (Beijing Wantai) and surrogate neutralization (GenScript) ELISAs, and two high-throughput assays (Roche Elecsys nucleocapsid and spike). IgM antibodies were less frequently detected by one of the rapid test cassette assays. CONCLUSIONS: Serum, plasma, and heparinized-plasma constitute reliable samples for SARS-CoV-2 antibody detection. In particular, the IgG response was stable and reliably detected after multiple FTCs and storage at common laboratory conditions. IgM detection was variable due to the labile nature of this antibody class.
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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 | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 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".