Early Humoral Response Correlates with Disease Severity and Outcomes in COVID-19 Patients
Bibliographic record
Abstract
Abstract The Coronavirus Disease 2019 (COVID-19), caused by the novel SARS-CoV-2, continues to spread globally with significantly high morbidity and mortality rates. Immunological surrogate markers, in particular antigen-specific responses, are of unquestionable value for clinical management of patients with COVID-19. Here, we investigated the kinetics of IgM, IgG against the spike (S) and nucleoproteins (N) proteins and their neutralizing capabilities in hospitalized patients with RT-PCR confirmed COVID-19 infection. Our data show that SARS-CoV-2 specific IgG, IgM and neutralizing antibodies (nAbs) were readily detectable in almost all COVID-19 patients with various clinical presentations. Notably, anti-S and -N IgG, peaked 20-40 day after disease onset, and were still detectable for at least up to 70 days, with nAbs observed during the same time period. Moreover, nAbs titers were strongly correlated with IgG antibodies. Significantly higher levels of nAbs as well as anti-S1 and N IgG and IgM antibodies were found in patients with more severe clinical presentations, patients requiring admission to intensive care units (ICU) or those with fatal outcomes. Interestingly, lower levels of antibodies, particularly anti-N IgG and IgM in the first 15 days after symptoms onset, were found in survivors and those with mild clinical presentations. Collectively, these findings provide new insights into the characteristics and kinetics of antibody responses in COVID-19 patients with different disease severity.
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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 | 0.003 |
| 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 | 0.001 |
| 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".