M-cell targeting acid-resistant oral vaccine delivery for immunization against Hepatitis B infection using cationic solid lipid nanoparticles
Bibliographic record
Abstract
PURPOSE: Viral infection caused by Hepatitis B is transmitted by permucosal or parenteral exposure and also one of the prime causes of hepatocellular carcinoma and liver cirrhosis.M-cell targeting acid-resistant oral vaccine delivery have been formulated for immunization against Hepatitis B infection. METHOD: Cationic solid lipid nanoparticles (cSLNs) were prepared utilizing solvent injection technique. Hepatitis B surface antigen (HBsAg) loaded alginate coated cSLNs were anchored with lipopolysaccharide (LPS). SDS-PAGE was performed to evaluate acid degradation protection of prepared formulation. RESULT: Induction of immunity produced by prepared nanoparticle for Hepatitis B was determined on female Balb/c mice followed by ELISA assays for assessing anti-HBsAg IgG/IgA antibodies in mucosal fluids.Sustained release of HBsAg (60.66%) has been exhibited from alginate coated cSLNs in comparison to cSLNs without alginate coating (97.72%) after 48 _h_. The production of anti-HBs titer in intestinal, salivary and vaginal secretions was 3.41 IU/ml, 3.1 IU/ml and 2.51 IU/ml respectively in comparison to the control group. CONCLUSION: Integrity of the M-cells has been maintained after binding with SLN, and oral administration delivered the antigen to the desired site of gut. It was found effective in producing antibodies in mucosal immunization against Hepatitis B virus. So, this formulation could be used as a promising alternative preexisting vaccine to prevent Hepatitis B infection.
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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".