Extracellular acyl-CoA-binding protein as an independent biomarker of COVID-19 disease severity
Bibliographic record
Abstract
Background: Factors leading to severe COVID-19 remain partially known. New biomarkers predicting COVID-19 severity that are also causally involved in disease pathogenesis could improve patient management and contribute to the development of innovative therapies. Autophagy, a cytosolic structure degradation pathway is involved in the maintenance of cellular homeostasis, degradation of intracellular pathogens and generation of energy for immune responses. Acyl-CoA binding protein (ACBP) is a key regulator of autophagy in the context of diabetes, obesity and anorexia. The objective of our work was to assess whether circulating ACBP levels are associated with COVID-19 severity, using proteomics data from the plasma of 903 COVID-19 patients. Methods: Somalogic proteomic analysis was used to detect 5000 proteins in plasma samples collected between March 2020 and August 2021 from hospitalized participants in the province of Quebec, Canada. Plasma samples from 903 COVID-19 patients collected during their admission during acute phase of COVID-19 and 295 hospitalized controls were assessed leading to 1198 interpretable proteomic profiles. Levels of anti-SARS-CoV-2 IgG were measured by ELISA and a cell-binding assay. Results: The median age of the participants was 59 years, 46% were female, 65% had comorbidities. Plasma ACBP levels correlated with COVID-19 severity, in association with inflammation and anti-SARS-CoV-2 antibody levels, independently of sex or the presence of comorbidities. Samples collected during the second COVID-19 wave in Quebec had higher levels of plasma ACBP than during the first wave. Plasma ACBP levels were negatively correlated with biomarkers of T and NK cell responses interferon-γ, tumor necrosis factor-α and interleukin-21, independently of age, sex, and severity. Conclusions: Circulating ACBP levels can be considered a biomarker of COVID-19 severity linked to inflammation. The contribution of extracellular ACBP to immunometabolic responses during viral infection should be further studied.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".