Cancer Spheroids Embedded in Tissue-Engineered Skin Substitutes: A New Method to Study Tumorigenicity In Vivo
Bibliographic record
Abstract
Tumorigenic assays are used during a clinical translation to detect the transformation potential of cell-based therapies. One of these in vivo assays is based on the separate injection of each cell type to be used in the clinical trial. However, the injection method requires many animals and several months to obtain useful results. In previous studies, we showed the potential of tissue-engineered skin substitutes (TESs) as a model for normal skin in which cancer cells can be included in vitro. Herein, we showed a new method to study tumorigenicity, using cancer spheroids that were embedded in TESs (cTES) and grafted onto athymic mice, and compared it with the commonly used cell injection assay. Tumors developed in both models, cancer cell injection and cTES grafting, but metastases were not detected at the time of sacrifice. Interestingly, the rate of tumor development was faster in cTESs than with the injection method. In conclusion, grafting TESs is a sensitive method to detect tumor cell growth with and could be developed as an alternative test for tumorigenicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".