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Record W4416414313 · doi:10.3390/curroncol32110649

Neurofibromatosis Type 1 and the Search for Effective Tumor Therapies Using High-Throughput Drug Screening

2025· article· en· W4416414313 on OpenAlexvenueno aff
Stephanie J. Bouley, Benjamin E. Housden, James A. Walker

Bibliographic record

VenueCurrent Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicNeurofibromatosis and Schwannoma Cases
Canadian institutionsnot available
FundersCongressionally Directed Medical Research ProgramsNeurofibromatosis Therapeutic Acceleration ProgramAction Medical ResearchGilbert Family FoundationLifeArcBloomberg Family Foundation
KeywordsNeurofibromatosisDrugDiseaseIdentification (biology)Neurofibromin 1GermlinePrecision medicineGenetic testingPharmacogenetics

Abstract

fetched live from OpenAlex

Neurofibromatosis type 1 (NF1) is a complex, multisystem, genetic disorder caused by germline NF1 variants that predispose affected individuals to tumors of the nervous system. With the identification of the NF1 gene in the late 1980s and the elucidation of the role of the encoded protein, neurofibromin, in regulating RAS signaling, considerable research effort has been invested to identify therapeutic treatments for NF1 tumors. Over the past two decades, high-throughput drug screening approaches have been a significant component of these endeavors. However, considerable variability exists among studies in terms of disease models, symptom targets, screening libraries, methods, and outcomes. In this review, we present an overall summary of efforts toward discovering new therapeutic strategies for NF1-related tumors using high-throughput screening and how such findings can be employed for prospective research in the NF1 field.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.078
GPT teacher head0.399
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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