Airways tissue expression of type I interferons and their stimulated genes is higher in children than adults
Bibliographic record
Abstract
There is emerging evidence that age-dependent differences in susceptibility to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) correlate with stronger innate immune response in the upper respiratory tract in children compared to adults. The efficient induction of interferon (IFN) alpha and beta (α and β) signaling, and interferon-stimulated genes (ISGs) is fundamental to the host antiviral response. In-silico transcriptomic analyses was conducted to determine the expression levels of IFN α/β pathway genes as well as 524 human ISGs in upper and lower airways of children and adults at baseline and post respiratory infections including coronavirus disease 2019 (COVID-19). To validate our in-silico analysis, we conducted qRT-PCR to measure ISGs levels in children and adult's nasal epithelial samples. At baseline, children had significantly higher levels of IFN α/β and ISGs genes compared to adults. More distinction was also seen in bronchial compared to nasal basal levels. Children nasal epithelial cells exhibited superior antiviral IFN α/β and associated ISGs response following ex-vivo poly (I:C) treatment model, and in clinical samples of SARS-CoV-2 infected patients. This was also confirmed in nasal epithelial samples using qRT-PCR validation. No gender-based difference in type I IFN levels across both age groups were observed. Understanding the biological basis for children resistance against severe COVID-19 is a challenge that has substantial clinical importance. More mechanistic studies are needed to carefully quantify how much of early IFN levels is needed to bypass the viral evasion mechanism and prevent its further replication and dissemination to lower airways and the rest of the body.
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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 | 0.000 | 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 | 0.001 |
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".