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Record W2981679255 · doi:10.1097/pai.0000000000000814

Neurofilament Immunohistochemistry Followed by Luxol Fast Blue, for Staining Axons and Myelin in the Same Paraffin Section of Spinal Cord

2019· article· en· W2981679255 on OpenAlexaff
Alexander J. Moszczynski, Kathryn Volkening, Michael J. Strong

Bibliographic record

VenueApplied immunohistochemistry & molecular morphology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSkin and Cellular Biology Research
Canadian institutionsWestern UniversityRobarts Clinical Trials
Fundersnot available
KeywordsLuxol fast blue stainStainingMyelinNeurofilamentImmunohistochemistryPathologyStainSpinal cordMultiple sclerosisNeuropathologyNissl bodyMyelin basic proteinCentral nervous systemMedicineAnatomyBiologyNeuroscienceImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Many disorders of the central nervous system are characterized by both axonal pathology and demyelination. In assessing this concurrent pathology, techniques for staining axons or myelin are frequently used separately. Here we report the development of a combined immunohistochemical and tinctorial staining technique in which we have modified the Luxol fast blue myelin stain to be used in conjunction with a diaminobenzidine-based immunohistochemical stain for high molecular weight neurofilament (SMI-31). This modification of staining will have utility in experimental neuropathology laboratories investigating demyelination and axonal damage in human tissue and animal 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0010.000
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 head0.008
GPT teacher head0.276
Teacher spread0.269 · 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.

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
Published2019
Admission routes1
Has abstractyes

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