Gemcitabine‐Loaded Injectable Hydrogel for Localized Breast Cancer Immunotherapy
Bibliographic record
Abstract
Abstract Injectable hydrogels for cancer immunotherapy are effective for both active and passive approaches. Tumor‐infiltrating lymphocyte (TIL) immunoshaping can change the tumor microenvironment to favor tumor cell elimination. The primary objective of immunoshaping is to reduce regulatory T ‐cells (Tregs), which can enhance the effectiveness of ex vivo immune cell therapy in solid tumors. A shear‐thinning injectable hydrogel that consists of gelatin and Laponite (Gel‐Lap) is used in this study. By optimizing the formulation, their immunotherapeutic and anti‐tumor properties are examined. Gemcitabine (GEM), an anti‐metabolite cancer chemotherapy agent, is loaded into a Gel‐Lap hydrogel (immunogel). The study compares the effects of immunogel on 4T1 inoculated breast cancer animal models. Results show that immunogel increases survival rates and significantly inhibits metastasis. The Treg cell population reduction is observed up to 70% in TILs and splenocyte population in line with CD8+ T ‐cells population increment in inguinal lymph nodes near the tumor region; the CD8+ T ‐cells function may be mediated through overexpression of eomesodermin (EOMES) as cytotoxic T lymphocyte (CTL) activation transcription factor. The human 3D cell culture model confirmed results in animal data demonstrating T ‐cell migration through the hydrogel and anticancer efficacy. Local delivery of GEM using our silicate‐based hydrogel holds promise for editing tumor microenvironment in favor of systemic immune responses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".