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Record W1981214889 · doi:10.3390/antib2020193

In Vivo Applications of Single Chain Fv (Variable Domain) (scFv) Fragments

2013· article· en· W1981214889 on OpenAlexaff
Philippe P. Monnier, Robin Vigouroux, Nardos G. Tassew

Bibliographic record

VenueAntibodies · 2013
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsIn vivoSingle-chain variable fragmentComputational biologyMonoclonal antibodyAntibodyFunction (biology)BiologyChemistryCell biologyImmunologyGenetics

Abstract

fetched live from OpenAlex

Single chain variable domain (Fv) fragments (scFv) are powerful tools in research and clinical settings, owing to better pharmacokinetic properties compared to the parent monoclonal antibodies and the relative ease of producing them in large quantities, at low cost. Though they offer several advantages, they suffer from lower binding affinity and rapid clearance from circulation, which limits their therapeutic potential. However, these fragments can be genetically modified to enhance desirable properties, such as multivalency, high target retention and slower blood clearance, and as such, a variety of scFv formats have been generated. ScFvs can be administered by systemic injection for diagnostic and therapeutic purposes. They can be expressed in vivo through viral vectors in instances where large infection rates and sustenance of high levels of the antibody is required. ScFvs have found applications as tools for in vivo loss-of-function studies and inactivation of specific protein domains, diagnostic imaging, tumor therapy and treatment for neurodegenerative and infectious diseases. This review will focus on their in vivo 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 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 categoriesInsufficient payload (model declined to judge)
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 score0.497
Threshold uncertainty score0.999

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)0.0020.000

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 head0.017
GPT teacher head0.293
Teacher spread0.276 · 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

Citations131
Published2013
Admission routes1
Has abstractyes

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