7D, a small molecule inhibits dengue infection by increasing interferons and neutralizing-antibodies via CXCL4:CXCR3:p38:IRF3 and Sirt1:STAT3 axes respectively
Bibliographic record
Abstract
Abstract There are a limited number of effective vaccines against dengue virus (DENV) and significant efforts are being made to develop potent anti-virals. Previously, we described that platelet-chemokine CXCL4 negatively regulates interferon (IFN)-α/β synthesis and promotes DENV2 replication. An antagonist to CXCR3 (CXCL4 receptor) reversed it and inhibited viral replication. In a concurrent search, we identified CXCR3-antagonist from our compound library, namely 7D, which inhibited all serotypes of DENV in vitro. With a half-life of ~2.85 h in plasma and no significant toxicity, 7D supplementation (8 mg/kg-body-weight) to DENV2-infected IFNα/β/γR −/− AG129 or wild-type C57BL6 mice increased synthesis of IFN-α/β and IFN-λ, and rescued disease symptoms like thrombocytopenia, leukopenia and vascular-leakage, with improved survival. 7D, having the property to inhibit Sirt-1 deacetylase, promoted acetylation and phosphorylation of STAT3, which in-turn increased plasmablast proliferation, germinal-center maturation and synthesis of neutralizing-antibodies against DENV2 in mice. A STAT3-inhibitor successfully inhibited these effects of 7D. Together, these observations identify compound 7D as a stimulator of IFN-α/β/λ synthesis via CXCL4:CXCR3:p38:IRF3 signaling, and a booster for neutralizing-antibody generation by promoting STAT3-acetylation in plasmablasts, capable of protecting dengue infection.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".