Purified <i>Astragalus</i> Polysaccharide Combined with Inactivated Vaccine Markedly Prevents Infectious Haematopoietic Necrosis Virus Infection in Rainbow Trout (<i>Oncorhynchus mykiss</i>)
Bibliographic record
Abstract
Rainbow trout ( Oncorhynchus mykiss ) is experiencing a catastrophic pandemic. In recent years, infectious hematopoietic necrosis virus (IHNV) has spread nationwide, resulting in significant mortality. Currently, there are no available treatments or vaccines for IHNV in China. Here, the Astragalus extract was purified and characterized. Then, we developed an inactivated IHNV vaccine with purified Astragalus polysaccharide (P-APS) as an adjuvant. Safety assays showed that IHNV was successfully inactivated. After a serious IHNV challenge, the cumulative mortality rates were 76.0, 38.0, and 22.1% in control, vaccine, and P-APS + vaccine groups, respectively. P-APS + vaccine was effective at reducing head kidney damage and apoptosis after IHNV challenge by histopathological and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) analyses. The P-APS + vaccine group showed better results in enhancing specific antibodies (IgM) and immune enzyme activities (C3, LZM, GOT, and GPT). RNA-seq revealed that many immune-related pathways were significantly enriched. TLR2, TLR7, C3, IFN-γ, IgM, MHC1, MHC2, MX1, and VIG1 were identified as core genes based on RNA-seq and PPI networks. Mechanistic investigations showed that P-APS + vaccine activates the immune pathway by upregulating the expression of these genes. P-ASP+vaccine induced effective innate and adaptive immune responses that were stronger than single vaccines after vaccination and IHNV challenged. Our findings will provide a promising vaccine candidate against IHNV.
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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) | 0.000 | 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".