Nanostructured particles assembled from natural building blocks for advanced therapies
Bibliographic record
Abstract
, proteins, lipids, polysaccharides, and polyphenols) have increasingly found use as fundamental building blocks for nanostructured particles as their advantageous properties, including biocompatibility, biodegradability, inherent bioactivity, and diverse chemical properties make them suitable for advanced therapeutic applications. This review provides a timely and comprehensive summary of the use of a broad range of natural building blocks in the rapidly developing field of advanced therapeutics with insights specific to nanostructured particles. We focus on an up-to-date overview of the assembly of nanostructured particles using natural building blocks and summarize their key scientific and preclinical milestones for advanced therapies, including adoptive cell therapy, immunotherapy, gene therapy, active targeted drug delivery, photoacoustic therapy and imaging, photothermal therapy, and combinational therapy. A cross-comparison of the advantages and disadvantages of different natural building blocks are highlighted to elucidate the key design principles for such bio-derived nanoparticles toward improving their performance and adoption. Current challenges and future research directions are also discussed, which will accelerate our understanding of designing, engineering, and applying nanostructured particles for advanced therapies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".