COVID‐19 plasma proteome reveals novel temporal and cell‐specific signatures for disease severity and high‐precision disease management
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is a systemic inflammatory condition with high mortality that may benefit from personalized medicine and high-precision approaches. COVID-19 patient plasma was analysed with targeted proteomics of 1161 proteins. Patients were monitored from Days 1 to 10 of their intensive care unit (ICU) stay. Age- and gender-matched COVID-19-negative sepsis ICU patients and healthy subjects were examined as controls. Proteomic data were resolved using both cell-specific annotation and deep-analysis for functional enrichment. COVID-19 caused extensive remodelling of the plasma microenvironment associated with a relative immunosuppressive milieu between ICU Days 3-7, and characterized by extensive organ damage. COVID-19 resulted in (1) reduced antigen presentation and B/T-cell function, (2) increased repurposed neutrophils and M1-type macrophages, (3) relatively immature or disrupted endothelia and fibroblasts with a defined secretome, and (4) reactive myeloid lines. Extracellular matrix changes identified in COVID-19 plasma could represent impaired immune cell homing and programmed cell death. The major functional modules disrupted in COVID-19 were exaggerated in patients with fatal outcome. Taken together, these findings provide systems-level insight into the mechanisms of COVID-19 inflammation and identify potential prognostic biomarkers. Therapeutic strategies could be tailored to the immune response of severely ill patients.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| 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 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".