Epithelial Ion Channel Function Altered by Influenza A Induced Cytokine Production
Bibliographic record
Abstract
Influenza A infection of respiratory airway epithelium produces cytokines that aid in fighting the infection. However, the excessive increase in cytokine production during severe influenza infections could contribute to altered airway ion channel activity that disrupts the airway fluid balance leading to pulmonary edema. Using qRT‐PCR and Bio‐Plex array we measured cytokines in Calu‐3 cells infected with influenza A. We simultaneously measured channel function by means of short‐circuit current produced by a Calu‐3 monolayer in response to agonists and channel blockers. Viral protein production and initial increases in cytokines were found at 24 hours post infection. Interestingly, no change in short‐circuit current response was found. However, further increases in proinflammatory cytokines at 48 hours post infection, such as IL‐8 and IL‐6, did correlate with a change in the agonist induced short‐circuit current response. However, the observed decreases in cAMP induced short‐circuit current, did not correlate with the significant increases in CFTR mRNA. In conclusion, influenza infection either directly or in concert with cytokines can induce transcriptional expression of ion channels but simultaneously inhibit their function. This study helps understand the pathophysiology which drives fluid into the lungs during an influenza A infection. Support: SHRF, EHRF, CAHF, CFI and NSERC
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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 | 0.000 |
| 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 | 0.001 |
| 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".