MétaCan
Menu
Back to cohort
Record W2606033132 · doi:10.1098/rsif.2017.0123

Giant axonal neuropathy alters the structure of keratin intermediate filaments in human hair

2017· article· en· W2606033132 on OpenAlexafffund
Asfia Soomro, Richard J. Alsop, Atsuko Negishi, Laurent Kreplak, Douglas S. Fudge, Edward R. Kuczmarski, Robert D. Goldman, Maikel C. Rheinstädter

Bibliographic record

VenueJournal of The Royal Society Interface · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsDalhousie UniversityUniversity of GuelphMcMaster University
FundersNational Institute of General Medical SciencesNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Economic Development and InnovationCanada Foundation for InnovationMcMaster University
KeywordsIntermediate filamentNeurofilamentKeratinAnatomyHypotoniaBiologyAtaxiaPathologyMedicineNeuroscienceGeneticsCytoskeleton

Abstract

fetched live from OpenAlex

Giant axonal neuropathy (GAN) follows an autosomal recessive genetic inheritance and impedes the peripheral and central nervous system due to axonal swellings that are packed with neurofilaments. The patients display a number of phenotypes, including hypotonia, muscle weakness, decreased reflexes, ataxia, seizures, intellectual disability, pale skin and often curled hair. We used X-ray diffraction and tensile testing to determine potential changes to the structure of keratin intermediate filaments (IFs) in the hair of patients with GAN. A statistically significant decrease in the 47 and the 27 Å diffraction signals were observed. Tensile tests determined that the hair was slightly stiffer, stronger and more extensible in GAN patients. These results suggest that the structure of keratin IFs in hair is altered in GAN, and the findings are compatible with an increased positional disorder of the keratin tetramers within the hair fibres.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.040
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.287
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 teacher head, 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

Citations8
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueJournal of The Royal Society InterfaceSame topicSkin and Cellular Biology ResearchFrench-language works237,207