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Record W2012506738 · doi:10.1017/s1740925x06000068

New observations on the compact myelin proteome

2005· article· en· W2012506738 on OpenAlexaff
Alejandro D. Roth, Anna Ivanova, David Colman

Bibliographic record

VenueNeuron Glia Biology · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsMyelinProteomeProteolipid protein 1BiologyCytoplasmCell biologyTransmembrane proteinMembrane proteinMyelin basic proteinMembraneBiochemistryCentral nervous systemNeuroscienceReceptor

Abstract

fetched live from OpenAlex

Myelin formation and maintenance depends on the establishment of two structurally and biochemically discernible domains: (a)compact myelin, that is multilamellar stacks of plasma membrane sheets; and (b) cytoplasmic channels that border the compact myelin domains, attach them to the cell body and anchor the myelin sheath to the axonal membrane. To identify proteins involved in the organization of these domains we took advantage of the high lipid content of compact myelin to separate it cleanly from other neural membranes and then used reverse-phase HPLC coupled to Electro-Spray Double Mass Spectrometry('MudPIT') to characterize the proteome of this sample. MudPIT allowed us to sidestep the bias of 2D-PAGE against either highly charged or transmembrane proteins. Thus, of 97 proteins that presented at least two, fully tryptic peptides (a stringent threshold), seven were well known myelin markers, including the mayor CNS myelin proteins: proteolipid protein and myelin basic protein, which are not resolvable by 2D-PAGE. Furthermore, we have confirmed and extended the known compact myelin proteome by 22 proteins and confirmed that CNS and PNS myelinated tracts present Sirtuin 2, a tubulin deacetylase, and Septin7, a small GTPase that is likely to be involved in membrane and cytoplasm partitioning.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.415

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 science0.0000.000
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 head0.046
GPT teacher head0.308
Teacher spread0.261 · 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

Citations46
Published2005
Admission routes1
Has abstractyes

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