Tumor and normal thyroid spheroids: from tissues to zebrafish
Bibliographic record
Abstract
BACKGROUND: Multicellular spheroids represent an interesting experimental model with promising applications in the pre-clinical studies on anticancer drugs. We recently demonstrated that thyroid spheroids recapitulate the features of the original tissues, in either the differentiated and "stem-like" components. Here we were aimed to characterize thyroid spheroids and to investigate in vivo the proangiogenic potential of patient-derived xenografts (PDX) of spheroids obtained from papillary thyroid cancer (PTC) and the matched normal tissues. METHODS: Spheroids cultures were obtained from 11 PTCs and matched normal tissues and characterized by immunohistochemistry. The expression of p53, involved in the regulation of stem cell homeostasis, was evaluated. The proangiogenic effect of thyroid spheroids was assessed by the injection in zebrafish embryos. RESULTS: Thyroid spheroids are enriched in stem-like cells, as shown by the positivity for the stem cell marker OCT4, and by the low level of p53 expression. Interestingly, PTCs and normal thyroid tissues have a detectable p53 expression, whereas the derived spheroids are mainly constituted by cells that express p53 at a lower level. Finally, we show that PDXs derived from PTC or normal spheroids stimulate the migration and the growth of sprouting vessels toward the implant into the zebrafish embryos. CONCLUSIONS: We report the characterization of multicellular spheroids obtained from PTCs and normal thyroid tissues, showing that they are enriched in stem-like cells. Moreover, we established xenografts of spheroids in zebrafish, demonstrating that they stimulate neoangiogenesis. This in vivo model could be considered as a valuable platform to test the effects of anticancer drugs.
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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 | 0.001 | 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) | 0.002 | 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".