The gamut of perspectives, challenges, and recent trends for<i>in situ</i>hydrogels: a smart ophthalmic drug delivery vehicle
Bibliographic record
Abstract
Polymers have a major role in the controlled delivery of pharmaceutical compounds to a targeted portion of the body. In this quest, a high priority research area is the targeted delivery of ophthalmic drugs to the interior regions of the eyes. Due to their complex anatomical/biochemical nature. This necessitates an advanced drug delivery cargo that could administer a therapeutic agent to the targeted location by evading various obstacles. The ongoing focus is to design an ophthalmic formulation by coupling it with a smart in situ forming polymeric hydrogel. These smart macromolecules have an array of unique theranostic properties and can utilize the in vivo biological parameters as a stimulus to change their macromolecular state from liquid to gel. The fast gelling hydrogel improves the corneal contact time, facilitates sustained drug release, resists the burst-out effect, and assists drug permeability to anterior regions. This review summarizes the rationale, scientific objectives, properties, and classification of the biologically important in situ hydrogels in the niche of ophthalmic drug delivery. The current trends and prospectives of the array of stimulus-responsive polymers, copolymers, and nanomaterials are discussed broadly. The crucial biointerfacial attributes with pros and cons are reviewed by investigating the effect of the nature of polymers as well as the ratio/percentage of additives and copolymers that influence the overall performance.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".