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Record W1902656581 · doi:10.1093/glycob/cwv037

Antibody recognition of carbohydrate epitopes

2015· review· en· W1902656581 on OpenAlexafffund
Omid Haji‐Ghassemi, R.J. Blackler, N. Martin Young, Stephen V. Evans

Bibliographic record

VenueGlycobiology · 2015
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsNational Research Council CanadaUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEpitopeAntigenGlycanAntibodyGlycoproteinGlycolipidVirologyBiologyImmunologyBiochemistry

Abstract

fetched live from OpenAlex

Carbohydrate antigens are valuable as components of vaccines for bacterial infectious agents and human immunodeficiency virus (HIV), and for generating immunotherapeutics against cancer. The crystal structures of anti-carbohydrate antibodies in complex with antigen reveal the key features of antigen recognition and provide information that can guide the design of vaccines, particularly synthetic ones. This review summarizes structural features of anti-carbohydrate antibodies to over 20 antigens, based on six categories of glyco-antigen: (i) the glycan shield of HIV glycoproteins; (ii) tumor epitopes; (iii) glycolipids and blood group A antigen; (iv) internal epitopes of bacterial lipopolysaccharides; (v) terminal epitopes on polysaccharides and oligosaccharides, including a group of antibodies to Kdo-containing Chlamydia epitopes; and (vi) linear homopolysaccharides.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.073
GPT teacher head0.391
Teacher spread0.318 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations152
Published2015
Admission routes2
Has abstractyes

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