A Novel Approach to Drug Delivery for Hepatities C Virus (HCV) for High Immune Responses
Bibliographic record
Abstract
Hepatities C Virus (HCV) is a significant health problem worldwide due to the lack of effective vaccines. HCV plasmid DNA (pDNA) vaccine represents a promising means to induce a Th1-biased cell-mediated response which tends to be associated with HCV clearance. However, the immune responses induced by naked pDNA vaccine in large animals as well as in humans are usually too weak to show sufficient protection against new infections. Therefore, it is interesting to look for new ways to deliver HCV pDNA vaccine. In this research, carbon nanotube (CNT) is used as a carrier to deliver the pDNA vaccine of HCV to induce high immune responses, because CNT has some excellent properties such as high strength and good biocompatibility. One of the key approaches to make this idea work is to treat CNT so that it can bind with HCV pDNA with good stability. An approach called 1, 3-dipolar cycloaddition of azomethine ylides was modified. We analyzed the complex of f-CNTs combined with pDNA vaccines expressing HCV E2 protein by using Enzyme-linked immunospot (ELISPOT) or Enzyme-linked immunosorbent assay (ELISA) assay in vitro. The result showed that the CNT approach can induce stronger protective immune responses than the needle delivery of naked pDNA vaccine. We have also found an optimal way to treat CNT in light of the highest immune response in the same testing environment. The success of this research will warrant testing HCV vaccine in large animal models and human clinical trials.
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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) | 0.001 | 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".