Expression of chemokines and cytokines in influenza A and B patients have a significant correlation to the levels of serum miRNAs
Bibliographic record
Abstract
Abstract Several host cytokines display differential expression in blood plasma and serum levels during influenza virus infection. Influenza may induce a T helper 2 polarized immune response leading to the activation of cytotoxic CD8+ T cells followed by expression of interleukins (IL). MiRNAs are remarkably stable and are key regulators of mRNA transcripts for proteins. MiRNAs are involved in the regulation of influenza virus replication in many cell types. During the 2015–16 and 2016–17 influenza season, we collected blood samples from patients infected with influenza to identify the role of serum miRNAs and cytokines. In influenza B patients, 76 miRNAs were differentially expressed compared to healthy subjects (p < 0.05), while 26 exosome miRNAs were differentially expressed in influenza A patients (p < 0.05). Of these miRNAs, 11 were expressed in both influenza A and B patients. The miRNAs that were differentially regulated between influenza A and B patients included miRNA-21, miR-191-5p, miR-324-5p and miR-199a-5p. MiR-21 is reported to target IP-10. Multiplex analysis of chemokines and cytokines showed that expression of IP-10 was highest in both A and B patients. In addition, influenza A patients showed increased expression of IFNα-2, GM-CSF, IL-13, IL-17, IL-1β, IL-6 and TNF-α, while influenza B patients showed only increase in IL-1α. MiR-150-5p expression showed a high correlation with MCP-1. MiR-133a-3p potentially targets both IL-17A and IL-1b and IL-15 potentially is targeted by miR-885 and miR-122, indicating that some cytokines were targeted by more than one miRNA. Our study shows that specific circulating miRNAs may be expressed by infected lung cells and that these miRNAs may be involved in the inflammatory responses to influenza.
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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 | 0.000 | 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".