Integrated multi-omics characterization across clinically relevant subgroups of long COVID
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When SARS-CoV-2 became regional epidemics, a substantial number of patients suffered from post-acute sequelae of COVID-19 (PASC, aka long COVID). Exploring the pathogenesis and especially the heterogenicity features of long COVID subgroups is of paramount importance for understanding its etiology. In this study, through integrative multi-omics analyses encompassing transcriptomics, proteomics, and metabolomics, long COVID patients exhibited overall elevated MAPK pathway activation, while patients who have recovered from long COVID showed down-regulation of this response. Long COVID heterogenicity is described by multi-omics distinct signatures for each subgroup. The Multisystemic (MULTI) symptom subgroup is characterized by enhanced glycerophospholipid and ether lipid metabolism, Neurological (NEU) by augmented glycoprotein synthesis metabolism, Cardio cerebral (CACRB) by increased pyruvate metabolism and suppressed macrophage polarization, Musculoskeletal + Systemic (MSK + SYST) by elevated glycerophospholipid metabolism, and Cardiopulmonary (CAPM) by inhibited NF-κB signaling pathways. ABHD17A, CSNK1D, PSME4 and SYVN1 were general long COVID combination biomarkers, while CRH (MULTI), FPGT (NEU), CBX6 (CACRB) and RBBP4 (CAPM) were selected as serum-specific subgroup proteins. Our study provides a commonly shared and distinct pathophysiological explanation underpinning PASC, paving the way for future diagnosis and therapeutic interventions.
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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 | 0.002 |
| 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 it