Tailoring the Inherent Properties of Biobased Nanoparticles for Nanomedicine
Bibliographic record
Abstract
Biobased nanoparticles are at the leading edge of the rapidly developing field of nanomedicine and biotherapeutics. Their unique size, shape, and biophysical properties make them attractive tools for biomedical research, including vaccination, targeted drug delivery, and immune therapy. These nanoparticles are engineered to present native cell receptors and proteins on their surfaces, providing a biomimicking camouflage for therapeutic cargo to evade rapid degradation, immune rejection, inflammation, and clearance. Despite showing promising clinical relevance, commercial implementation of these biobased nanoparticles is yet to be fully realized. In this perspective, we discuss advanced biobased nanoparticle designs used in medical applications, such as cell membrane nanoparticles, exosomes, and synthetic lipid-derived nanoparticles, and highlight their benefits and potential challenges. Moreover, we critically assess the future of preparing such particles using artificial intelligence and machine learning. These advanced computational tools will be able to predict the functional composition and behavior of the proteins and cell receptors present on the nanoparticle surfaces. With more advancement in designing new biobased nanoparticles, this field of research could play a key role in dictating the future rational design of drug transporters, thereby ultimately improving overall therapeutic outcomes.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".