Multi-omics landscapes reveal heterogeneity in long COVID patients characterized with enhanced neutrophil activity
Bibliographic record
Abstract
BACKGROUND: Omicron variant impacts populations with its rapid contagiousness, and part of patients suffered from persistent symptoms termed as long COVID. The molecular and immune mechanisms of this currently dominant global variant leading to long COVID remain unclear, due to long COVID heterogeneity across populations. METHODS: We recruited 66 participants in total, 22 out of 66 were healthy control without COVID-19 infection history, and 22 complaining about long COVID symptoms 6 months after first infection of Omicron, referred as long COVID (LC) Group. The left ones were defined as non-long COVID (NLC) Group. We profiled them via plasma neutralizing antibody titer, SARS-CoV-2 viral load, transcriptomic and proteomics screening, and machine learning. RESULTS: No serum residual SARS-CoV-2 was observed in the participants 6 months post COVID-19 infection. No significant difference in neutralizing antibody titers was found between the long COVID (LC) Group and the non-long COVID (NLC) Group. Transcriptomic and proteomic profiling allow the stratification of long COVID into neutrophil function upregulated (NU-LC) and downregulated types (ND-LC). The NU-LC, identifiable through a refined set of 5 blood gene markers (ABCA13, CEACAM6, CRISP3, CTSG and BPI), displays evidence of relatively higher neutrophil counts and function of degranulation than the ND-LC at 6 months after infection, while recovered at 12 months post COVID-19. CONCLUSION: The transcriptomic and proteomic profiling revealed heterogeneity among long COVID patients. We discovered a subgroup of long COVID population characterized by neutrophil activation, which might associate with the development of psychiatric symptoms and indicate a higher inflammatory state. Meanwhile, a cluster of 5 genes was manually curated as the most potent discriminators of NU-LC from long COVID population. This study can serve as a foundational exploration of the heterogeneity in the pathogenesis of long COVID and assist in therapeutic targeting and detailed epidemiological investigation of long COVID.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".