Pre-existing asthma as a comorbidity does not modify cytokine responses and severity of COVID-19
Bibliographic record
Abstract
BACKGROUND: A significant portion of COVID-19 sufferers have asthma. The impacts of asthma on COVID-19 progression are still unclear but a modifying effect is plausible as respiratory viruses are acknowledged to be an important trigger for asthma exacerbations and a different, potentially type-2 biased, immune response might occur. In this study, we compared the blood circulating cytokine response to COVID-19 infection in patients with and without asthma. METHODS: Plasma samples and clinical information were collected from 80 patients with mild (25), severe (36) or critical (19) COVID-19 and 29 healthy subjects at the John Radcliffe Hospital, Oxford, UK. The concentrations of 51 circulating proteins in the plasma samples were measured with Luminex and compared between groups. RESULTS: Total 16 pre-existing asthma patients were found (3 in mild, 10 in severe, and 3 in critical COVID-19). The prevalence of asthma in COVID-19 severity groups did not suggest a clear correlation between asthma and COVID-19 severity. Within the same COVID-19 severity group, no differences were observed between patients with or without asthma on oxygen saturation, CRP, neutrophil counts, and length of hospital stay. The mortality in the COVID-19 patients with asthma (12.5%) was not higher than that in patients without asthma (17.2%). No significant difference was found between asthmatic and non-asthmatic in circulating cytokine response in different COVID-19 severity groups, including the cytokines strongly implicated in COVID-19 such as CXCL10, IL-6, CCL2, and IL-8. CONCLUSIONS: Pre-existing asthma was not associated with an enhanced cytokine response after COVID-19 infection, disease severity or mortality.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".