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Record W7135038057 · doi:10.1093/neuped/wuaf001.201

MDB-01. Cerebrospinal fluid shear stress activates a targetable mechano-metastatic cascade to promote medulloblastoma metastasis

2025· article· en· W7135038057 on OpenAlexaff
Xi Huang

Bibliographic record

VenueNeuro-Oncology Pediatrics · 2025
Typearticle
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsCerebrospinal fluidMetastasisMechanosensitive channelsMedulloblastomaGLUT1Shear stressMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Abstract Biofluid flow generates fluid shear stress (FSS), a mechanical force widely present in tissue microenvironments. How brain tumor growth alters the conduit of biofluid, thereby impacting FSS-regulated cancer progression is unknown. Dissemination of medulloblastoma (MB) cells into the cerebrospinal fluid (CSF) initiates metastasis within the central nervous system. By simulating CSF dynamics based on magnetic resonance imaging of MB patients, we discovered that FSS is elevated at the cervicomedullary junction. MB-relevant FSS promotes metastasis along mouse spinal cords. Mechanistically, FSS induces metastatic cell behaviours, including weakened cell-substrate adhesion, increased motility, cell clustering, and plasma membrane localization of glucose transporter 1 (GLUT1) to enhance glucose uptake. FSS is perceived by mechanosensitive ion channel PIEZO2, which drives actomyosin contractility-dependent GLUT1 recruitment at the plasma membrane. Genetic targeting of PIEZO2 or pharmacologic inhibition of GLUT1 mitigates metastasis. Collectively, these findings define a targetable FSS-activated mechano-metastatic cascade for the treatment of MB metastasis.

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.296
Teacher spread0.276 · 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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