Research progress on hydrogel-based drug therapy in melanoma immunotherapy
Bibliographic record
Abstract
Melanoma is one of the most aggressive skin tumors, and conventional treatment modalities are not effective in treating advanced melanoma. Although immunotherapy is an effective treatment for melanoma, it has disadvantages, such as a poor response rate and serious systemic immune-related toxic side effects. The main solution to this problem is the use of biological materials such as hydrogels to reduce these side effects and amplify the immune killing effect against tumor cells. Hydrogels have great advantages as local slow-release drug carriers, including the ability to deliver antitumor drugs directly to the tumor site, enhance the local drug concentration in tumor tissue, reduce systemic drug distribution and exhibit good degradability. Despite these advantages, there has been limited research on the application of hydrogels in melanoma treatment. Therefore, this article provides a comprehensive review of the potential application of hydrogels in melanoma immunotherapy. Hydrogels can serve as carriers for sustained drug delivery, enabling the targeted and localized delivery of drugs with minimal systemic side effects. This approach has the potential to improve the efficacy of immunotherapy for melanoma. Thus, the use of hydrogels as drug delivery vehicles for melanoma immunotherapy has great potential and warrants further exploration. [BMB Reports 2024; 57(2): 71-78].
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".