Virus-like Particles: Fundamentals and Biomedical Applications
Bibliographic record
Abstract
Nanotechnology is a fast-evolving field focused on fabricating nanoscale objects for industrial, cosmetic, and therapeutic applications. Virus-like particles (VLPs) are self-assembled nanoparticles whose intrinsic properties, such as heterogeneity, and highly ordered structural organization are exploited to prepare vaccines; imaging agents; construct nanobioreactors; cancer treatment approaches; or deliver drugs, genes, and enzymes. However, depending upon the intrinsic features of the native virus from which they are produced, the therapeutic performance of VLPs can vary. This review compiles the recent scientific literature about the fundamentals of VLPs with biomedical applications. We consulted different databases to present a general scenario about viruses and how VLPs are produced in eukaryotic and prokaryotic cell lines to entrap therapeutic cargo. Moreover, the structural classification, morphology, and methods to functionalize the surface of VLPs are discussed. Finally, different characterization techniques required to examine the size, charge, aggregation, and composition of VLPs are described.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".