Bibliographic record
Abstract
The use of hydrogels for therapeutic delivery is a burgeoning area of investigation. These water-swollen polymer matrices are ideal platforms for localized drug delivery that can be further combined with specific ligands or nanotechnologies to advance the controlled release of small-molecule drugs and proteins. Due to the advantage of hydrophobic, electrostatic, or specific extracellular matrix interactions, affinity-based strategies can overcome burst release and challenges associated with encapsulation. Future studies will provide innovative binding tools, truly stimuli-responsive systems, and original combinations of emerging technologies to control the release of therapeutics spatially and temporally. Local drug delivery can be achieved by directly injecting a therapeutic to its site of action and is advantageous because off-target effects associated with systemic delivery can be minimized. For prolonged benefit, a vehicle that provides sustained drug release is required. Hydrogels are versatile platforms for localized drug release, owing to the large library of biocompatible building blocks from which they can be formed. Injectable hydrogel formulations that gel quickly in situ and provide sustained release of therapeutics are particularly advantageous to minimize invasiveness. The incorporation of polymers, ligands or nanoparticles that have an affinity for the therapeutic of interest improve control over the release of small-molecule drugs and proteins from hydrogels, enabling spatial and temporal control over the delivery. Such affinity-based strategies can overcome drug burst release and challenges associated with protein instability, allowing more effective therapeutic molecule delivery for a range of applications from therapeutic contact lenses to ischemic tissue regeneration.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".