Hydrogels bearing bioengineered mimetic embryonic microenvironments for tumor reversion
Bibliographic record
Abstract
Embryonic microenvironments can reverse the metastatic phenotype of aggressive tumors by inhibiting the Nodal signaling pathway. Here, we hypothesize that embryonic microenvironments can be transplanted for the purpose of oncotherapy. We report the development of an injectable bioactive hydrogel system containing the key antagonists of Nodal signaling-Cripto-1 receptor antibodies (2B11)-for the creation of embryonic microenvironments and the examination of their effect on tumor reversion treatment using a mouse model. Our in vitro results show that the hydrogel system can reduce the mitochondrial membrane potential of MDA-MB-231 and MCF-7, promote cell apoptosis, and reduce the invasive ability of cells. Our in vivo results illustrate that the hydrogel system can significantly inhibit tumor growth in both breast cancer and melanoma tumor-bearing mouse models, as well as transform the cell morphology of melanoma B16 cells to melanin-like cells. Furthermore, the results of the up-regulation of tumor suppressor genes and the down-regulation of oncogenes by high-throughput sequencing confirm that the developed system can also selectively turn on some tumor suppressor genes and turn off certain oncogenes so as to prompt the benign reversion of the tumor phenotype. Taken together, our results demonstrate the injectable biomaterial system is able to create an effective microenvironment for melanoma and breast tumor therapy.
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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 | 0.000 |
| 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".