Biofabrication of a three dimensional human‐based personalized neurofibroma model
Bibliographic record
Abstract
Neurofibromas are the most characteristic feature of neurofibromatosis type 1 (NF1), a multisystemic disorder caused by aberrations in the neurofibromin gene (NF1). Despite significant progress over the last several years in understanding this disease, a suitable in vitro model to better mimic neurofibroma formation and growth has yet to be described. There is therefore a need to establish an in vitro, three dimensional model that allows the incorporation of multicellular lineages and the modulation of the cellular microenvironment-known to be important for cellular crosstalk and distribution of soluble factors-to study neurofibroma biology and morphogenesis. A self-assembly approach was used to generate tissue-engineered skins (TES) in which patient-derived spheroids made of NF1-associated Schwann cells and fibroblasts were seeded. We describe the first in vitro three dimensional neurofibroma model-directly derived from NF1 patients presenting with histopathological features-having an ECM protein expression profile quite similar to that of a native tumor. We observed efficient incorporation, proliferation, and migration of spheroids within NF1-TES over time. This biotechnological approach could provide a unique tool for precision medicine targeting NF1 and for assessing the tumorigenic properties of each NF1 gene mutation linked to tumor formation.
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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".