Stimuli-Responsive Hydrogels for Local Post-Surgical Drug Delivery
Bibliographic record
Abstract
Currently, surgical operations, followed by systemic drug delivery, are the prevailing treatment modality for most diseases, including cancers and trauma-based injuries. Although effective to some extent, the side effects of surgery include inflammation, pain, a lower rate of tissue regeneration, disease recurrence, and the non-specific toxicity of chemotherapies, which remain significant clinical challenges. The localized delivery of therapeutics has recently emerged as an alternative to systemic therapy, which not only allows the delivery of higher doses of therapeutic agents to the surgical site, but also enables overcoming post-surgical complications, such as infections, inflammations, and pain. Due to the limitations of the current drug delivery systems, and an increasing clinical need for disease-specific drug release systems, hydrogels have attracted considerable interest, due to their unique properties, including a high capacity for drug loading, as well as a sustained release profile. Hydrogels can be used as local drug performance carriers as a means for diminishing the side effects of current systemic drug delivery methods and are suitable for the majority of surgery-based injuries. This work summarizes recent advances in hydrogel-based drug delivery systems (DDSs), including formulations such as implantable, injectable, and sprayable hydrogels, with a particular emphasis on stimuli-responsive materials. Moreover, clinical applications and future opportunities for this type of post-surgery treatment are also highlighted.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".