Polyglycerols-based hydrogels for biomedical applications: a comprehensive review
Bibliographic record
Abstract
Polyglycerols (PGs) and their derivatives are promising biomaterials for fabricating hydrogels due to their high performance and inherent physicochemical (e.g., hydrophilicity, and long-term temperature and pH stabilities) as well as biological (e.g., cyto-/bio-compatibility, biodegradability, and minimal cell adhesion and protein absorption) properties. These hydrogels are suitable candidates for many biomedical applications such as drug, biomacromolecules, and gene delivery, regenerative medicine, and diagnostic molecules carrier for various imagines. The presence of functional hydroxyl groups on the polyether backbone allows further modification of the PGs structures, and increasing their solubilities, targeting abilities, and biocompatibilities. In addition, PGs can be incorporated into “smart” systems to afford stimuli-responsive hydrogels that provide the possibility of changing the mechanical and biochemical properties widely for the hydrogels. Addressing some challenges, including industrial-scale production of PGs with precise branching and controlled molecular weight, refine the synthesis and purification processes, and designing novel biodegradable crosslinkers can be facilitate the manufacturing of PGs-based hydrogels for various biomedical applications. This review consolidates the recent progresses in the synthesis and properties of PGs, and biomedical applications of PGs-based hydrogels.
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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 | 0.002 |
| 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) | 0.002 | 0.001 |
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".