Molecular Insights into Long COVID: Plasma Proteomics Reveals Oxidative Stress, Coagulation Cascade Activation, and Glycolytic Imbalance
Bibliographic record
Abstract
Abstract Persistent symptoms following SARS-CoV-2 infection are the hallmark of post-COVID condition (PCC), also referred to as long COVID. However, our knowledge is limited on the underlying molecular mechanisms. In this study, we performed data-independent acquisition mass spectrometry plasma proteomics (DIA-MS) to identify molecular alterations associated with PCC. DIA-MS proteomic analysis revealed a few proteins linked to oxidative stress that had altered expression. Notably, PCC samples exhibited downregulation of the antioxidant protein peroxiredoxin 6 (PRDX6) and upregulation of oxidative stress-associated proteins particularly vanin-1 (VNN1) and paraoxonase-3 (PON3). Additionally, the PCC group showed significantly higher levels of six proteins (PCSK9, CST3, C1Q, CPB2, KNG1 GAPDH), which were linked to pathways involving glycolysis, complement and coagulation cascades, and inflammation. Oxidative stress analysis confirmed that PCC samples had significantly higher levels of DNA damage (8-OHdG) than the convalescent group, whereas antioxidant markers, such as reduced and oxidized glutathione (GSH and GSSG), were significantly lower in PCC samples than in uninfected controls. Our observations point towards ongoing oxidative and inflammatory processes in PCC and suggest potential targets for biomarker development and therapeutic intervention.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".