In depth characterisation of the proteome of MIS-C and post COVID-19 infection in children reveals inflammatory pathway activation and evidence of tissue damage
Bibliographic record
Abstract
BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) is a rare but severe complication that arises between two and six weeks after initial SARS-CoV-2 infection. The mechanisms underlying why only a subset of children develop this hyperinflammatory response remain unclear. METHODS: We performed an in-depth proteomic analysis of plasma samples from children before and after SARS-CoV-2 infection, including those who developed MIS-C. Proteomic profiling was conducted using high-throughput technologies, and findings were validated using publicly available datasets. RESULTS: Healthy children showed minimal changes in the circulating proteome following SARS-CoV-2 infection, with no evidence of ongoing inflammation. In contrast, children with MIS-C exhibited significant activation of pro-inflammatory pathways and elevated circulating markers of myocardial and vascular injury. CONCLUSIONS: Our data suggest that SARS-CoV-2 infection alone does not cause sustained proteomic alterations in most children. However, MIS-C is associated with a distinct inflammatory and vascular injury signature. Several candidate diagnostic biomarkers for MIS-C were identified and validated in silico, offering promising avenues for future diagnostic and therapeutic strategies.
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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 | 0.001 |
| 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 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".