Nanostructured particles assembled from natural building blocks for advanced therapies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it