Using UV-Responsive Nanoparticles to Provide <i>In Situ</i> Control of Growth Factor Delivery and a More Constant Release Profile from a Hydrogel Environment
Bibliographic record
Abstract
Nanoparticles are popular delivery vehicles, but their diffusional release results in inconstant drug delivery. Here, we flatten the delivery profile into a more constant, zero-order profile. Brain-derived neurotrophic factor (BDNF) is attached to photoactive titanium dioxide nanoparticles and loaded into a nanofibrous self-assembling peptide (SAP) hydrogel. Different UV exposure conditions show three distinct profiles, including a counterintuitive decrease in release after UV exposure. We propose that the adsorption of the freed growth factor onto the hydrogel nanofibers affects release. Nanoparticles diffuse from the hydrogel readily, carrying the bound growth factor, but the freed growth factor (released from the nanoparticles by UV) instead interacts with─and is released less readily from─the hydrogel. UV shifts growth factor from nanoparticles to the hydrogel, therefore changing the diffusional release. Through midpoint UV exposure, we achieve a flattened delivery profile─unusual for diffusion─by changing in situ the amount of growth factor bound to the diffusing nanoparticles. With nanoparticle diffusion alone, we observed an increasing release profile with 36% of release in the first 6 h and 64% in the second 6 h. With midway UV exposure, this was controlled to 49 and 51%, respectively. The release of an unbound (soluble) control growth factor, glial cell-line derived neurotrophic factor (GDNF), was not affected by UV treatment, demonstrating the potential for independent control of temporal delivery profiles in a multiagent material.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".