Revisiting the NPcis mouse model: A new tool to model plexiform neurofibroma
Bibliographic record
Abstract
Neurofibromatosis Type I (NF1) is a rare genetic disorder. NF1 patients frequently develop a benign tumor in peripheral nerve plexuses called plexiform neurofibroma. In the past two decades, tissue-specific Nf1 knockout mouse models were developed using commercially available tissue-specific Cre recombinase and the Nf1 flox mice to mimic neurofibroma development. However, these models develop para-spinal neurofibroma, recapitulating a rare type of neurofibroma found in NF1 patients. The NPcis mouse model developed a malignant version of neurofibroma called malignant peripheral nerve sheath tumor (MPNST) within 3 to 6 months but intriguingly without apparent benign precursor lesion. Here, we revisited the NPcis model and discovered that about 20% display clinical signs similar to Nf1 tissue-specific knockout mice models. However, a systematic histological analysis could not explain the clinical signs we observed although we noticed lesions reminiscent of a neurofibroma in a peripheral nerve, a cutaneous neurofibroma, and para-spinal neurofibroma on rare occasions in NPcis mice. We also observed that 10% of the mice developed a malignant peripheral nerve sheath tumor (MPNST) spontaneously, coinciding with their earring tag identification. Strikingly, half of the sciatic nerves from NPcis mice developed plexiform neurofibroma within 1-6 months when intentionally injured. Thus, we provided a procedure to turn the widely used NPcis sarcoma model into a model recapitulating plexiform neurofibroma.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".