Sequence independent immune effects of white spot syndrome virus (WSSV) dsRNA complexed with phytoglycogen nanoparticles in freshwater crayfish
Bibliographic record
Abstract
White spot syndrome virus (WSSV), a double stranded (ds)DNA virus, is a pathogen that causes massive mortalities in crustaceans worldwide. The present study focuses on using dsRNA to induce sequence-independent immune responses to control virus replication. DsRNA is a well characterized innate immune stimulant in vertebrates and effectively induces an antiviral state. In crustaceans, it has been shown that dsRNA containing WSSV sequences (WSSV-dsRNA) can trigger an immune response independent of RNA interference (RNAi) to mitigate disease. We hypothesized that the potency and efficacy of dsRNA-induced immunity would be enhanced using a biodegradable, cationic phytoglycogen nanoparticle, Nanodendrix (nanoparticle; NP), to deliver the dsRNA. Two in vivo studies were conducted to test the efficacy of long dsRNA as an innate immune stimulant with or without the NP in crayfish. Long dsRNA, 360-500 bp in length, was synthesized based on two WSSV sequences, viral particle 28 (VP28) and viral particle 19 (VP19) respectively. Crayfish were injected in the ventral sinus with WSSV-dsRNA (VP28 or VP19 sequence) either in complex with the NP or alone. High molecular weight (HMW) poly inosinic: polycytidylic acid (poly IC), a synthetic viral dsRNA mimic, was used as a positive control. In the negative control groups, crayfish were injected with either phosphate buffered saline or NP alone. These studies found WSSV-dsRNA could enhance hemocyte numbers, nitric oxide levels and phenoloxidase activity. This enhancement was more effective than when using HMW poly IC. Finally, the nanoparticle did not increase dsRNA's immune activation capability, but it did reduce dsRNA's toxicity. Further studies may help determine the efficacy of these treatments as immune stimulants for preventing pathogenic outbreaks in the invertebrate aquaculture industry.
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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".