<i>Cis</i> -aconitate therapy protects against influenza mortality by dual targeting of viral polymerase and ERK/AKT/NF-κB signaling
Bibliographic record
Abstract
ABSTRACT Influenza virus poses a significant global health challenge, causing approximately 500,000 deaths annually. Its ability to evade antiviral treatments and vaccine-induced immunity underscores the need for novel therapeutic approaches. Our study identifies cis -aconitate ( cis -aco), a mitochondria-derived metabolite, as a potent dual-action agent against influenza, independently of its metabolic derivative, itaconate. Cis -aco impairs viral polymerase activity, suppressing viral mRNA expression and protein synthesis to inhibit replication across a range of influenza subtypes. This antiviral efficacy is confirmed in ex vivo human airway and lung organotypic models. Beyond its antiviral properties, cis-aco exhibits potent anti-inflammatory effects, disrupting key inflammatory cascades and reducing the secretion of inflammatory mediators. In a mouse model of influenza pneumonia, cis -aco mitigates viral replication, inflammation, and immune cell activation, significantly improving survival. Notably, its efficacy persists even when administered at later stages of infection, when oseltamivir/Tamiflu® is no longer effective. These findings position cis -aco as a promising influenza treatment, combining antiviral and anti-inflammatory benefits within a clinically relevant timeframe.
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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) | 0.004 | 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".