Early Humoral Response Correlates with Disease Severity and Outcomes in COVID-19 Patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Coronavirus Disease 2019 (COVID-19), caused by SARS-CoV-2, continues to spread globally with significantly high morbidity and mortality rates. Antigen-specific responses are of unquestionable value for clinical management of COVID-19 patients. Here, we investigated the kinetics of IgM, IgG against the spike (S) and nucleoproteins (N) proteins and their neutralizing capabilities in hospitalized COVID-19 patients with different disease presentations (i.e., mild, moderate or severe), need for intensive care units (ICU) admission or outcomes (i.e., survival vs death). We 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. Interestingly, significantly higher levels of nAbs as well as anti-S1 and -N IgG and IgM antibodies were found in patients with more severe symptoms, patients requiring admission to ICU or those with fatal outcomes. More importantly, early after symptoms onset, we found that the levels of anti-N antibodies correlated strongly with disease severity. Collectively, these findings provide new insights into the kinetics of antibody responses in COVID-19 patients with different disease severity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 it