Current and Future Developments in the Treatment of Virus-Induced Hypercytokinemia
Bibliographic record
Abstract
Emerging pathogenic viruses such as Ebola and Middle Eastern Respiratory Syndrome coronavirus (MERS-CoV) can cause acute infections through the evasion of the host's antiviral immune responses and by inducing the upregulation of inflammatory cytokines. This immune dysregulation, termed a cytokine storm or hypercytokinemia, is potentially fatal and is a significant underlying factor in increased mortality of infected patients. The prevalence of global outbreaks in recent years has offered opportunities to study the progression of various viral infections and have provided an improved understanding of hypercytokinemia associated with these diseases. However, despite this increased knowledge and the study of the infections caused by a range of emerging viruses, the therapeutic options still remain limited. This review aims to explore alternative experimental strategies for treating hypercytokinemia induced by the Ebola, avian influenza and Dengue viruses; outlining their modes of action, summarizing their preclinical assessments and potential clinical applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".