Implications of preexisting asthma on COVID-19 pathogenesis
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic spreading at an alarming rate has taken a heavy toll on the public healthcare systems and economies worldwide. An abnormal and overactivated inflammatory response is occasionally elicited by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and this hyperinflammation is associated with worse prognosis of COVID-19. Theoretically, one would expect patients with asthma to be at a greater risk of SARS-CoV-2 infection considering their increased susceptibility to common respiratory virus-associated exacerbations. Surprisingly, current data do not consistently suggest an increased prevalence of asthma among patients with COVID-19. Considering the high global prevalence of asthma, the characteristics of the disease and/or their conventional therapy might play a role in their potential defense against COVID-19. This may be attributed to the T helper type 2 immune response predominantly seen in patients with asthma. Likewise, asthma therapeutics, including corticosteroids and biologics, may in fact benefit the patients with asthma by alleviating the development of hyperinflammation. On the other hand, elevated IL-17 levels are characteristically seen in a subset of asthma patients with severe disease as well as in patients with COVID-19. Targeting the IL-17 pathway as a treatment strategy could plausibly alleviate acute respiratory distress syndrome (ARDS) in patients with COVID-19 and asthma demonstrating a predominant T helper type 17 response. A clinical trial including a drug targeting this pathway may thus, constitute a logical addition to the global pursuit for effective therapeutics against COVID-19. The complex interplay between the asthma endotypes and COVID-19 is not very well understood and will be discussed in this mini-review.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".