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Record W2963648456 · doi:10.4052/tigg.1905.2se

HNK-1 Carbohydrate, an Attractive Unique Glyco-Epitope

2019· article· en· W2963648456 on OpenAlexfundno aff
Shogo Oka

Bibliographic record

VenueTrends in Glycoscience and Glycotechnology · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsnot available
FundersInstitute of GeneticsRitsumeikan University
KeywordsEpitopeNeural cell adhesion moleculeMonoclonal antibodyBiologyCell adhesion moleculeImmunoglobulin superfamilyCarbohydrateCell adhesionAntibodyCell biologyBiochemistryCellImmunology

Abstract

fetched live from OpenAlex

The HNK-1 carbohydrate antigen, which recognized by a monoclonal antibody HNK-1, was originally noticed as a maker of human natural killer (HNK) cells. Subsequent studies revealed that HNK-1 carbohydrate was highly expressed in the nervous system and was commonly expressed on cell adhesion molecules belonging to the immunoglobulin superfamily. Therefore, it was suggested the involvement of HNK-1 carbohydrate in migration of neural crest cells, adhesion of neurons to glial cells, and axon outgrowth. I was interested in this epitope and investigated the biosynthetic enzymes for this epitope. I fortunately received the JSCR Young Investigator Award 1998. In this review, I illustrated a series of our studies including recent findings about the HNK-1 carbohydrate.

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 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 score0.156
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.012
GPT teacher head0.299
Teacher spread0.288 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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