Whole blood transcriptome in long-COVID patients reveals association with lung function and immune response
Bibliographic record
Abstract
BACKGROUND: Months after infection with severe acute respiratory syndrome coronavirus 2, at least 10% of patients still experience complaints. Long-COVID (coronavirus disease 2019) is a heterogeneous disease, and clustering efforts revealed multiple phenotypes on a clinical level. However, the molecular pathways underlying long-COVID phenotypes are still poorly understood. OBJECTIVES: We sought to cluster patients according to their blood transcriptomes and uncover the pathways underlying their disease. METHODS: Blood was collected from 77 patients with long-COVID from the Precision Medicine for more Oxygen (P4O2) COVID-19 study. Unsupervised hierarchical clustering was performed on the whole blood transcriptome. These clusters were analyzed for differences in clinical features, pulmonary function tests, and gene ontology term enrichment. RESULTS: /forced vital capacity (72% vs 81%), and lower diffusion capacity of the lung for carbon monoxide (68% vs 85% predicted). Gene ontology term enrichment analysis revealed upregulation of genes involved in the antiviral innate immune response in cluster 1, whereas genes involved with the adaptive immune response were upregulated in cluster 2. CONCLUSIONS: This study provides a start in uncovering the pathophysiological mechanisms underlying long-COVID. Further research is required to unravel why the immune response is different in these clusters, and to identify potential therapeutic targets to create an optimized treatment or monitoring strategy for the individual long-COVID patient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".