An injectable thermosensitive hydrogel/nanomicelles composite for local chemo-immunotherapy in mouse model of melanoma
Bibliographic record
Abstract
Recently, cancer immunotherapy and its combination with chemotherapy has been considered to improve therapeutic efficacy with lower systemic toxicity. Here, we prepared a thermosensitive hydrogel based hyaluronic acid (HA) encapsulated with macrophage colony-stimulating factor (GM-CSF) and paclitaxel (PTX) for chemoimmunotherapy of cancer. For this purpose, the micelles were prepared with the mixture of pluronic F127 (PF127) and tocopheryl polyethylene glycol (TPGS) and loaded with PTX. In the following step, thermosensitive hydrogel using PF127 and HA was prepared and co-encapsulated with the micelles and GM-CSF. Rheological performance, friability, release patterns for PTX and GM-CSF, and stability of GM-CSF in the hydrogel were evaluated in details. In-vitro and in vivo immunologic activities of GM-CSF in the hydrogel were also evaluated via numbering macrophages and recruited DCs in transwells and after subcutaneous injection of the GM-CSF-loaded hydrogel. Finally, mouse model of subcutaneous melanoma was induced in female C57 mice using B16 F10 cell line and the effect of optimized formulation was evaluated based on tumor volume and histological analysis. The hydrogel could maintain the biological activity of the incorporated drugs and exhibited a more prolonged release for PTX compared to GM-CSF. GM-CSF-releasing HA/PF127 hydrogel successfully recruited macrophages in vitro. Moreover, the most potent anti-tumor effect was observed following the intra-tumoral injection of the optimized formulation in melanoma bearing mice, compared to immunization by the GM-CSF and PTX alone. The current formulation shows a great promise to conquer resistant malignancies and provides a new approach for co-encapsulating of hydrophobic anticancer drugs and growth factor.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".