Functional Self-Immolative Hydrogels with Dendritic Cross-Linkers for Controlled Drug Delivery
Bibliographic record
Abstract
In the biomedical field, the design of materials with controlled degradation is highly desired. Herein, we present a family of dendritic hydrogels accomplished through copper-assisted azide-alkyne cycloaddition click reaction between dendritic cross-linkers and complementary linear polymers. As cross-linkers, an innovative family of bifunctional carbosilane dendrimers was designed for this purpose, bearing multiple alkyne groups available for network formation as well as pendant hydroxyl groups for postfunctionalization. Additionally, different azide-pendant polymers were employed, including difunctional poly-(ethylene glycol) with cleavable and noncleavable nature, as well as poly-(ethyl glyoxylate) with and without self-immolative behavior. The rational design of the dendritic hydrogels, through the careful selection of these two components, enabled an accurate manipulation of properties like swelling and mechanical properties. The network degradation could be tuned from a few hours, for a traditional ester-cleavable dendritic hydrogel, to several days under pH-controlled conditions, for the self-immolative hydrogel (SIH). The impact of network degradation on the release of curcumin as a model drug was also confirmed. This work showcased the potential of dendritic SIHs for biomedical applications.
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) | 0.000 | 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".