Immunomodulatory therapy for severe influenza
Bibliographic record
Abstract
Influenza A virus is a significant cause of morbidity and mortality worldwide. Severe influenza is recognized as a clinical syndrome, characterized by hyperinduction of proinflammatory cytokine production, otherwise known as hypercytokinemia or a 'cytokine storm'. Research focused on therapeutics to modulate influenza virus-induced inflammation is currently underway. In this review, we discuss the limitations of current antiviral drug treatment strategies, describe the influenza viral and host pathogenicity determinants, and present the evidence supporting the use of immunomodulatory therapy to target the host inflammatory response as a means to improve clinical outcome in severe influenza. We then review the experimental data on investigational immunomodulatory agents targeting the host inflammatory response in severe influenza, including anti-TNF therapy, statins, glucocorticoids, cyclooxygenase-2 inhibitors, macrolides, peroxisome proliferator-activated receptor agonists, AMP-activated protein kinase agonists and high mobility group box 1 antagonists. We then conclude with a rationale for the use of mesenchymal stromal (stem) cells and angiopoietin-1 therapy against deleterious influenza-induced host responses that mediate end-organ injury and dysfunction.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".