Functional mass spectrometry indicates anti-protease and complement activity increase with COVID-19 severity
Bibliographic record
Abstract
Investigations on some innate immunity proteins can yield misleading information, as investigators often rely on static measurements and assume a direct correlation to function. As protein function is often not directly proportional to protein abundance, and mechanistic pathways are interconnected and under constant feedback regulatory control, functional analysis is required. In this study, we used functional mass spectrometry to measure anti-protease and complement activity in plasma obtained from coronavirus disease 2019 (COVID-19) patients. Our data suggests that within 48 h of hospital admission, COVID-19 patients undergo a protease storm with significantly elevated neutrophil elastase (p < 0.001) and lymphocyte granzyme B (p < 0.01), while, anti-protease activity is significantly increased, including alpha-1 antitrypsin (AAT; p < 0.001) and alpha-1-antichymotrypsin (ACT; p < 0.001). Concurrently, the ratio of C3a to C3beta activity significantly decreased with increasing COVID-19 severity, suggesting more complement activation (Mild COVID-19 p < 0.05; Severe COVID-19 p < 0.001). Activity levels of AAT, ACT and C3a/C3beta remained unchanged over 10 hospital days. Our data suggests that COVID-19 is associated with both a protease storm and complement activation, with the former somewhat balanced with increased anti-protease activity. Evaluation of the AAT/ACT ratio and C3a/C3beta ratio indicated that COVID-19 severity is associated with both neutrophil elastase neutralization and complement activation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".