Lipid-based colloidal nanoparticles for applications in targeted vaccine delivery
Bibliographic record
Abstract
Bioactive molecules and their effects have been influenced by their solubility and administration route. In many therapeutic reagents, the performance of therapeutics is dependent on physiological barriers in the human body and delivery efficacy. Therefore, an effective and stable therapeutic delivery promotes pharmaceutical advancement and suitable biological usage of drugs. In the biological and pharmacological industries, lipid nanoparticles (LNPs) have emerged as a potential carrier to deliver therapeutics. Since studies reported doxorubicin-loaded liposomes (Doxil®), LNPs have been applied to numerous clinical trials. Lipid-based nanoparticles, including liposomes, solid lipid nanoparticles (SLNs), and nanostructured lipid nanoparticles, have also been developed to deliver active ingredients in vaccines. In this review, we present the type of LNPs used to develop vaccines with attractive advantages. We then discuss messenger RNA (mRNA) delivery for the clinical application of mRNA therapeutic-loaded LNPs and recent research trend of LNP-based vaccine development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".