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Record W4293577655 · doi:10.1016/j.isci.2022.105028

Resolving primary pathomechanisms driving idiopathic-like spinal curvature using a new katnb1 scoliosis model

2022· article· en· W4293577655 on OpenAlexafffund
Anne Meyer-Miner, Jenica L.M. Van Gennip, Katrin Henke, Matthew P. Harris, Brian Ciruna

Bibliographic record

VenueiScience · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsZebrafishCiliumNeuroscienceCell biologyBiologyNeuroinflammationScoliosisCerebrospinal fluidMotile ciliumMotilityAnatomyPathologyInflammationMedicineImmunologyGeneticsGene

Abstract

fetched live from OpenAlex

Idiopathic scoliosis (IS) refers to abnormal spinal curvatures that occur in the absence of vertebral or neuromuscular defects. IS accounts for 80% of human spinal deformity, afflicts ∼3% of children worldwide, yet pathogenic mechanisms are poorly understood. A key role for cerebrospinal fluid (CSF) homeostasis in zebrafish spine development has been identified. Specifically, defects in cilia motility of brain ependymal cells (EC), CSF flow, and/or Reissner fiber (RF) assembly are observed to induce neuroinflammation, oxidative stress, abnormal CSF-contacting neuron activity, and urotensin peptide expression, all associating with scoliosis. However, the functional relevance of these observations to IS remains unclear. Here we characterize zebrafish katnb1 mutants as a new IS model. We define essential roles for Katnb1 in motile ciliated lineages, uncouple EC cilia and RF formation defects from spinal curvature, and identify abnormal CSF flow and cell stress responses as shared pathogenic signatures associated with scoliosis across diverse zebrafish models.

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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.304
Teacher spread0.273 · 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

Citations16
Published2022
Admission routes2
Has abstractyes

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