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Record W3028550487 · doi:10.1039/d0bm00574f

Nanobodies derived from Camelids represent versatile biomolecules for biomedical applications

2020· review· en· W3028550487 on OpenAlexaff
Xinyu Yu, Qilan Xu, Yuan Wu, Huijun Jiang, Wei Wei, Azhati Zulipikaer, Yan Guo, Jirimutu, Jin Chen

Bibliographic record

VenueBiomaterials Science · 2020
Typereview
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsInstitute of Cancer Research
FundersNational Laboratory of BiomacromoleculesNatural Science Research of Jiangsu Higher Education Institutions of ChinaPriority Academic Program Development of Jiangsu Higher Education InstitutionsNatural Science Foundation of Jiangsu ProvinceState Key Laboratory of Chemo/Biosensing and ChemometricsNanjing Medical UniversitySoutheast UniversityNational Natural Science Foundation of China
KeywordsBiomoleculeComputational biologyNanotechnologyComputer scienceChemistryBiologyMaterials science

Abstract

fetched live from OpenAlex

Nanobodies are antigen binding variable domains of heavy-chain antibodies without light-chains, and these biomolecules occur naturally in the serum of Camelidae species. Nanobodies have a compact structure and low molecular weight when compared with antibodies, and are the smallest active antigen-binding fragments. Because of their remarkable stability and manipulable characteristics, nanobodies have been incorporated into biomaterials and used as molecular recognition and tracing agents, drug delivery systems, molecular imaging tools and disease therapeutics. This review summarizes recent progress in this field focusing on nanobodies as versatile biomolecules for biomedical applications.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.096
GPT teacher head0.428
Teacher spread0.331 · 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

Citations31
Published2020
Admission routes1
Has abstractyes

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