Translating current basic research into future therapies for neurofibromatosis type 1
Bibliographic record
Abstract
Neurofibromatosis type 1 (NF1) is a hereditary tumour syndrome that predisposes to benign and malignant tumours originating from neural crest cells. Biallelic inactivation of the tumour-suppressor gene NF1 in glial cells in the skin, along a nerve plexus or in the brain results in the development of benign tumours: cutaneous neurofibroma, plexiform neurofibroma and glioma, respectively. Despite more than 40 years of research, only one medication was recently approved for treatment of plexiform neurofibroma and no drugs have been specifically approved for the management of other tumours. Work carried out over the past several years indicates that inhibiting different cellular signalling pathways (such as Hippo, Janus kinase/signal transducer and activator of transcription, mitogen-activated protein kinase and those mediated by sex hormones) in tumour cells or targeting cells in the microenvironment (nerve cells, macrophages, mast cells and T cells) might benefit NF1 patients. In this review, we outline previous strategies aimed at targeting these signalling pathways or cells in the microenvironment, agents that are currently in clinical trials, and the latest advances in basic research that could culminate in the development of novel therapeutics for patients with NF1.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".