Puerarin: A Potential Therapeutic for SARS-CoV-2 and Hantavirus Co-Infection
Bibliographic record
Abstract
Patients with Hantavirus-caused epidemic hemorrhagic fever (EHF) are at risk of contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there is currently no validated EHF/SARS-CoV-2 strategy. Several studies have recently shown Puerarin, a natural product, has potent antiviral properties. The goal of present study was to determine the mechanism of puerarin in patients with EHF/COVID-19. We use network pharmacology and bioinformatics to investigate the possible pharmacological targets, bioactivities, and molecular mechanisms of puerarin in the treatment of patients with EHF/SARS-CoV-2. The study investigated the pathogenesis of COVID-19 and EHF and the signaling pathway impacted by puerarin. 68 common genes linked to puerarin and EHF/SARS-CoV-2 were discovered during the investigation. By using protein-protein interaction (PPI) network, we identified RELA, JUN, NF-B1, NF-B2, and FOS as potential therapeutic targets. The bioactivity and signaling pathways of puerarin have also been demonstrated in the treatment of EHF and COVID-19. According to present study, puerarin could reduce excessive immune responses and inflammation through the NF-B, TNF, and HIF-1 signaling pathways. This study explored the potential therapeutic targets and mechanisms of Puerarin in the treatment of EHF/COVID-19.
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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 | 0.001 |
| 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) | 0.001 | 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".