Cancer theranostic platforms based on injectable polymer hydrogels
Bibliographic record
Abstract
Theranostic platforms that combine therapy with diagnosis not only prevent the undesirable biological responses that may occur when these processes are conducted separately, but also allow individualized therapies for patients. Polymer hydrogels have been employed to provide well-controlled drug release and targeted therapy in theranostics, where injectable hydrogels enable non-invasive treatment and monitoring with a single injection, offering greater patient comfort and efficient therapy. Efforts have been focused on applying injectable polymer hydrogels in theranostic research and clinical use. This review highlights recent progress in the design of injectable polymer hydrogels for cancer theranostics, particularly focusing on the elements/components of theranostic hydrogels, and their cross-linking strategies, structures, and performance with regard to drug delivery/tracking. Therapeutic agents and tracking modalities that are essential components of the theranostic platforms are introduced, and the design strategies, properties and applications of the injectable hydrogels developed via two approaches, namely chemical bonds and physical interactions, are described. The theranostic functions of the platforms are highly dependent on the architecture and components employed for the construction of hydrogels. Challenges currently presented by theranostic platforms based on injectable hydrogels are identified, and prospects of acquiring more comfortable and personalized therapies are proposed.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".