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Record W2891048636 · doi:10.1016/j.celrep.2018.08.039

Interrogating the ccm-3 Gene Network

2018· article· en· W2891048636 on OpenAlexafffund
Benjamin Lant, Swati Pal, Eric M. Chapman, Bin Yu, Daniel Witvliet, S.R. Choi, Lisa Zhao, Corinne Albigès‐Rizo, Eva Faurobert, W. Brent Derry

Bibliographic record

VenueCell Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicVascular Malformations Diagnosis and Treatment
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalCanada Research ChairsSickKids FoundationUniversity of TorontoHospital for Sick Children
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthCanadian Institutes of Health ResearchHospital for Sick Children
KeywordsPhenocopyBiologyGeneCell biologyGeneticsPhenotype

Abstract

fetched live from OpenAlex

Cerebral cavernous malformations (CCMs) are neurovascular lesions caused by mutations in one of three genes (CCM1-3). Loss of CCM3 causes the poorest prognosis, and little is known about how it regulates vascular integrity. The C. elegans ccm-3 gene regulates the development of biological tubes that resemble mammalian vasculature, and in a genome-wide reverse genetic screen, we identified more than 500 possible CCM-3 pathway genes. With a phenolog-like approach, we generated a human CCM signaling network and identified 29 genes in common, of which 14 are required for excretory canal extension and membrane integrity, similar to ccm-3. Notably, depletion of the MO25 ortholog mop-25.2 causes severe defects in tube integrity by preventing CCM-3 localization to apical membranes. Furthermore, loss of MO25 phenocopies CCM3 ablation by causing stress fiber formation in endothelial cells. This work deepens our understanding of how CCM3 regulates vascular integrity and may help identify therapeutic targets for treating CCM3 patients.

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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.257
Teacher spread0.242 · 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

Citations17
Published2018
Admission routes2
Has abstractyes

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