Prophylactic, therapeutic and immune enhancement effect of liposome‐encapsulated PolyICLC on highly pathogenic H5N1 influenza infection
Bibliographic record
Abstract
BACKGROUND: In view of the magnitude and severity of outbreaks of the highly pathogenic H5N1 influenza virus (H5N1-HPIV) and the threat to public health, there is an urgent need to develop broad-spectrum prophylactic and therapeutic agents against infection by H5N1-HPIV and other subtypes. METHODS AND RESULTS: In the present study, we explored the use of LE-PolyICLC, a liposome encapsulated double-stranded RNA, as a possible prophylactic, therapeutic and immune enhancement agent. In a mouse infection model, we showed that the administration of LE-PolyICLC intranasally before or shortly after infection could inhibit virus replication, leading to a significant reduction in pulmonary viral titres and a higher survival rate of infected mice. When used as a molecular adjuvant, LE-PolyICLC significantly enhanced both the humoral and cellular responses elicited by inactivated H5N1 vaccine and augmented the protective efficacy provided by vaccination. Most importantly, the data also demonstrate that LE-PolyICLC could effectively attenuate the development of pulmonary fibrosis during the restoration period at day 14 after H5N1 infection. CONCLUSIONS: Taken together, the data obtained in the present study suggest that strong consideration should be given for the use of LE-PolyICLC as prophylactic and therapeutic agents and also as a vaccination adjuvant to combat highly pathogenic influenza infection and its associated complications such as pulmonary fibrosis.
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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".